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.J

EEMARY
Michigan State
University

 

 

 

This is to certify that the

dissertation entitled

AN INVESTIGATION OF PERFORMANCE MEASUREMENT:
THE IMPACT OF LOGISTICS STRUCTURE AND STRATEGY

presented by
Richard Nicholas Germain

has been accepted towards fulfillment
of the requirements for

Ph.D. Marketing

degree in

 

 

“(QanM/Ifiww

ajo professor

Date Nov. 10, 1989

 

MSU is an Affirmative Action/Equal Opportunity Institun'on 0-12771

 

PLACE N RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or baton data duo.

DATE DUE DATE DUE DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MSU Is An Affirmative Action/Equal Opportunity Institution

 

AN INVESTIGATION OF PERFORMANCE MEASUREMENT:
THE IMPACT OF LOGISTICS STRUCTURE AND STRATEGY

BY
Richard Nicholas Germain
A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Marketing and Transportation Administration

1989

ABSTRACT

AN INVESTIGATION OF PERFORMANCE MEASUREMENT:
THE IMPACT OF LOGISTICS STRUCTURE AND STRATEGY

BY

Richard Nicholas Germain

Performance measures monitor the conversion of labor and
capital inputs, such as warehousing costs, into outputs, such
as customer service. The research objective was to examine
empirically how logistics strategy and structure relate to
performance measurement across three channel positions:
manufacturers, wholesalers and retailers.

A 2x2 typology of performance measurement was developed.
The first dimension distinguished cost from differentiation
performance measurement. The second dimension distinguished
internal performance measurement from competitive bench-
marking. These four types of performance measurement served
to create six dependent variables.

Three groups of independent variables were examined. ‘Fhe
first group consisted of: (1) formal integrative devices such
as a logistics ‘mission statement; (2) whether logistics
controls customer service or logistics systems planning; and
(3) the total number of activities controlled by logistics.
These variables are positively related to performance
measurement within all three types of firms. Thus,
organizations having these attributes were found to have more
extensive performance measurement systems.

The second group of independent variables consisted of:

(1) the :maturity' of ‘the logistics; (2) participation in
business unit strategic planning by the senior logistics
executive; and (3) the title level of the senior logistics
executive. 'rhese ‘variables are sporadically related to
performance measurement. These variables, especially
variables describing the "position" of the senior logistics
executive, are of lessor importance with respect to
performance measurement.

The third group of independent variables consists of: (1)
the number of times logistics has been reorganized during the
past five years; and (2) the frequency of updating the
logistics strategic plan. These variables, possibly because
they are indicative of logistical flexibility, are unrelated
to performance measurement.

The major finding regarding channel position is that
while firms at different levels of the channel are not equally
involved in logistics performance measurement, the
relationship of logistics strategy and structure to
performance measurement are the same. The results of this
research provide additional evidence that the "best" practices
of logistics management are independent of channel position.
Additional insights are provided regarding the impact of

structure and strategy upon best practice.

Copyright by

RICHARD NICHOLAS GERMAIN

1989

ACKNOWLEDGEMENTS

Above all, I would like to thank Donald J. Bowersox,
Chairman of the dissertation committee, for his guidance
during the writing of this dissertation and throughout my
tenure in the doctoral program. I am also greatly indebted
to him for the numerous research opportunities he provided to
me.

I would also like to thank David J. Closs and Cornelia
L. Droge who were also members of the dissertation committee.
Their insights were invaluable.

Lastly, I would like to thank my parents, Gilles and
Zoria Germain. Without their continual support, none of this

would have been possible.

TABLE OF CONTENTS

 

 

Page

LIST OF TABLES .......................................... i
LIST OF EXHIBITS ...................................... iii
I: INTRODUCTION ......................................... 1
Focus of the Dissertation ............................ 3
Theoretical Framework.. .............................. 4

A Typology of Performance Measurement.. ........... 6
Constructs and Theoretical Framework .............. 7
Analysis............. ............................. 9
Potential Contributions ..... . ........................ 9
Organization of the Dissertation.. .................. 10
II: THEORETICAL AND EMPIRICAL BACKGROUND ............... 12
Theoretical Background.. ............................ 13
Environment ...................................... 13
Strategy......... ................................ 16
Structure..... ......... . ......... .. .............. 17
Perfomanceoooooooooooooo ......... 0.00000... 00000 18
Contingency Relationships .................. . ..... 18
Performance Measurement. ..... ....................... 22
Internal Performance Measurement ................. 22
Strategic Intelligence and Benchmarking.......... 26
Hypotheses............ ........ .. ............ ..... 30
Logistics Strategy........... ....... .... ..... ....... 31
Structuring Variables ...... ...... ................... 33

Structuring Variables as Dependent Variables..... 35
Structuring Variables as Independent Variables... 37
Hypotheses........ ..... ......... ......... . ....... 4O
Pictorial Configuration ............................. 42
Pictorial Configuration as Dependent Variables... 46
Pictorial Configuration as Independent Variables. 52

Hypotheses...................... ................. 55
Channel Position............ ...... .. ........... ..... 57
Summary of Hypotheses............. ............... ... 61

vi

 

III: METHODOLOGY.... ................................... 66
Sampling Design........ ............................. 66
Questionnaire Design.... ....... . .................... 67
Overview of Methodology ............................. 75

IV: RESULTS.... ........................................ 79
Initial Results ..................................... 79
Hypothesis One... .............. . .................... 82
Overall Analysis ............... .. ................... 86

Strategy and Performance Measurement ............. 87
Structuring Variables and Performance
Measurement ................................... 89
Pictorial Configuration and Performance
Measurement ................................... 98
Summary of Overall Analysis.. ................... 107
Subgroups Analysis ............. . ................... 110
Subgroup Analysis: Strategy ..................... 111
Subgroup Analysis: Structuring Variables ........ 111
Subgroup Analysis: Pictorial Configuration...... 121
Summary of Subgroup Analysis ........... . ........ 123
Summary of Results ............ . .................... 125

V: DISCUSSION AND CONCLUSIONS ......................... 128

Discussion................... .................. .... 128
Channel Position.... ............................ 128
Interrelationships Among Performance Measures... 130
Logistics Strategy .............................. 131
Structuring Variables ........ . .................. 132

Formalization. ......... ..... ................. 132
Reorganizations................... ......... .. 136
Participation in Strategic Planning .......... 136
Pictorial Configuration Variables............... 137
Activity Allocation............ .............. 137

Title Level................. ..... ............ 140
Implications for Leading Edge Logistics............ 141
Suggestions for Future Research... ...... . ......... . 147

Research from Dissertation Data Set............. 148
Research from a New Study....................... 151

Limitation8000000000000000.... .................... 0 156
Summary of Dissertation. ........................... 157
APPENQIM A: Manufacturer Questionnaire ........... ..... 159
APPENDIX B: Update Manufacturer Questionnaire ......... 174

Vii

Page

 

APPENDIX C: Wholesaler - Retailer Questionnaire ....... 182
APPENDIX D: Statistical Hypotheses .................... 196
VI: REFERENCES.... ...... . ........ . ............. . ...... 231

 

viii

10.

11.

12.

13.

14.

15.

LIST OF TABLES
Page

Means and Standard Deviations of Performance

Measures used by Channel Members........ ..... ...... 80
One-Way ANOVA Models of Performance Measures used

as a Function of Channel Position and Tukey Test

Results......... ........ .............. ........ ..... 81
Correlation Matrix of Performance Measures used
(All Firms)........... ....... ........ ....... .. ..... 83
Correlation Matrix of Performance Measures used
(Manufacturers).............................. ...... 83
Correlation Matrix of Performance Measures used
(Wholesalers).............. ....... ... ........ . ..... 84
Correlation Matrix of Performance Measures used
(Retailers)................. .............. .. ....... 84

GLM Models of Performance Measures used as a

Function of Frequency of Strategic Plan Update

and Channel Position....... ........................ 88
ANOVA Models of Performance Measures used as a
Function of Logistics Mission Statement and

Channel Position................................... 90
ANOVA Models of Performance Measures used as a
Function of Logistics Strategic Plan and Channel
Position........................................... 92
GLM Models of Performance Measures used as a

Function of Number of Years Logistics has been a
Formal Function and Channel Position............... 94
GLM Models of Performance Measures used as a

Function of the Number of Times Logistics has

been Reorganized and Channel Position.............. 96
GLM Models of Performance Measures used as a

Function of the Senior Logistics Executive
Participation in Strategic Planning and Channel
Position........................................... 97
ANOVA Models of Performance Measures used as a
Function of Whether Logistics Controls Customer
Service and Channel Position....................... 99
ANOVA Models of Performance Measures used as a
Function of Whether Logistics Controls Logistics
Systems Planning and Channel Position....... ...... 101
GLM Models of Performance Measures used as a

Function of Number of Activities Controlled by
Logistics and Channel Position.................... 103

LIST OF TABLES (CONT'D)
Page

16. GLM Models of Performance Measures used as a

Function of Level of Senior Logistics Executive

and Channel Position.............................. 104
17. Correlations for All Firms........................ 105
18. T-tests of Difference in Means of Performance

Measures used: All Firms.......................... 106
19. Summary of ANOVA and GLM Models................... 108
20. Subgroup One-tailed t-tests of Difference in Means

of Performance Measures used Between Firms with

Versus without a Logistics Mission Statement...... 112
21. Subgroup One-tailed t-tests of Difference in Means

of Performance Measures used Between Firms with

Versus without a Logistics Strategic P1an......... 113
22. Subgroup t-tests of Difference in Means of

Performance Measures used Between Firms with

Logistics Controlling Versus not Controlling

Customer Service.................................. 114
23. Subgroup t-tests of Difference in Means of

Performance Measures used Between Firms with

Logistics Controlling Versus not Controlling

Logistics Systems Planning........................ 115
24. Subgroup Correlations: Manufacturers.............. 116
25. Subgroup Correlations: Wholesalers................ 117
26. Subgroup Correlations: Retailers.................. 118
27. Summary of Subgroup Analysis...................... 124

1.
2.
3.
4.
5.

10.
11.
12.
13.
14.

15.
16.

LIST OF EXHIBITS

Page

Elements of Competitive Advantage..................
Classification of Performance Measurement..........

2
5

Testing Framework0000000.0.0000000000000000 000000000 8

Theoretical Framework..............................
Empirical Research with Structuring Variables as
Dependent Variables................................
Empirical Research with Structuring Variables as
Independent Variables..............................
Stages Paradigm...... ..... .........................
Empirical Research with Pictorial Configuration

as Dependent Variables....................... ......
Empirical Research with Pictorial Configuration

as Independent Variables...........................
Theoretical Framework of the Dissertation..........
Variables from the Questionnaire...................
Activities used to Measure Span of Logistics
Control....................................... .....
Items used to Indicate Internal Performance
Measurement........................................
Items used to Indicate Benchmarking Performance
Measurement........................................
Sample Sizes.................................. .....
Statistical Methodology... ..... .. ............. .....

xi

14

36

38
44

47
53
63
68
71
72
73

74
76

CHAPTER I: INTRODUCTION

A comprehensive examination of logistics texts reveals
many sections, if not chapters, devoted to logistics
performance measurement (Coyle and Bardi 1984, p. 451-453:
Ballou 1985, p. 547-554; Bowersox, Closs and Helferich 1986,
p. 321-340; Stock and Lambert 1987, p. 698-711). Performance
measurement is important because of its direct relationship
to operational results such as profitability or market share.
For example, one study found a 14 to 22 percent productivity
gain by firms with sophisticated performance measurement
systems (A.T. Kearney, Inc. 1984).

Exhibit 1 demonstrates the importance of performance
measurement (Day auui Wensley 1988). Positional advantage
refers. to the extent. to ‘which a firm actually' holds a
competitive advantage. It is through formal performance
measurement that positional advantage is determined. As seen
in Exhibit 1, positional advantage leads to results such as
market share, profits and customer satisfaction. Knowledge
of positional advantage and its causal attributes ideally
guide reinvestment into appropriate advantage generating
skills and resources.

This competitive advantage framework provides a mechanism

to interpret performance-related research. For example, the

2

Exhibit 1
THE ELEMENTS OF COMPETITIVE ADVANTAGE *

 

 

SOURCES OF POSITIONAL RESULTS
ADVANTAGE _-> ADVANTAGE _>
-superior skills -value through -market share
-superior differentiation -profitability
resources -lower relative costs

.I‘. Investment of Profits “z'

* From Day and Wensley (1988. p. 3)

early PIMS research by Buzzell, Gale and Sultan (1975), which
was restricted to an examination of the interrelationships
among performance outcomes, found a positive relationship
between market share and return on investment. Another
example was provided by Phillips, Chang and Buzzell (1983).
They examined the effect of direct relative costs, a type of
positional advantage, on market share and return on
investment. These two examples are representative of the
large body of research conducted in marketing on
organizational performance.

In logistics, research has been devoted to: (1) defining
how performance can be measured (Bowersox gt a1. 1989): (2)
how logistics can conceptually impact profits (Shapiro 1984);
and (3) how productivity can be measured and improved (A.T.
Kearney, Inc. 1978; Grottke and Norris 1981; Gautschi 1983).
The topic of performance measurement remains timely in that
it was identified as a major source of concern among senior

logistics executives (Bowersox:§thgl. 1989, p. 273-274). This

3
concern was expressed equally by manufacturers, wholesalers

and retailers.

FOCUS OF THE DISSERTATION

For the purpose of this research, marketing can be viewed
as two interrelated subsystems. One subsystem is viewed as
creating or stimulating demand. The second subsystem
primarily services demand (Bowersox 1961; Lewis and Erickson
1969; Reidenbach and Oliva 1981). Some of the activities
involved in stimulating demand are advertising, promotion and
personal selling. Some of the activities involved in
servicing demand are transportation, warehousing, inventory
management, order processing and material handling, among
others. Marketing management can be viewed as the process of
integrating and coordinating demand stimulation and demand
servicing.

Demand servicing is discussed under the topics of
physical distribution, materials management, or logistics, the
marriage of both physical distribution and materials
management. A title currently gaining industrial support to
describe the demand servicing aspects of marketing is product
supply (Dumaine 1989).

For the purpose of this research it is important to
define the terms "logistics activities" and "logistics
function." The term "logistics activities" is used to

describe the flow of goods and related information throughout

4
an organization. The term "logistics function" is used to
describewthat.organizational.hierarchy“with.responsibility for
logistics activities.

The dissertation is concerned with relationships between
the structure of the logistics function and the extent or
usage of performance measurement of logistics activities. 'Phe
dissertation focuses on three research questions.

(1) HOW' are ‘various types of performance

measurement of logistics activities interrelated?

(2) What organizational factors describing

logistics function structure and strategy are

related to performance measurement?

(3) Does a firm's channel position alter how

different types of performance measurement are

interrelated or how logistics function structure

and strategy are related to performance
measurement?

THEORETICAL FRAMEWORK

The framework of the dissertation is presented in three
sections. First, a typology of performance measurement is
introduced. Next, the constructs and major linkages between
constructs examined are presented. lastly, analysis

considerations are discussed.

A TYPOLOGY OF PERFORMANCE MEASUREMENT
Performance measurement takes many different forms. To

help understand the complexity and goals of performance

5

Exhibit 2
CLASSIFICATION OF PERFORMANCE MEASUREMENT

 

 

Performance Measurement Focus

Internal External
Strategic Focus Measurement Measurement
(Benchmarking)

 
   
   
  
 

   
   
  
 

   
   
  
  
   
 
   
  
 
 

Measurement of
competitor costs.
Benchmarking firm
cost with that of
key competitors.

Measurement of firm
costs. Examples
include cost,
productivity and
asset management
measurement.

Cost approach

   

   
  
 

  
   
  
   
  
 

Measurement of items
that can be used as
differentiators.
Examples include
customer service and
quality measurement.

Measurement of
competitor differ-
entiation. Bench-
marking firm differ-
entiation with that
of key competitors.

Differentiation
approach

    
 

 

measurement, Exhibit 2 offers a 2x2 typology. The term
performance measurement focus refers to whether the
measurement is internal or external. Internal performance
measurement consists of monitoring: (1) financial data such
as return on investment; (2) specific logistics measures such
as 'total logistics. cost, inbound freight. cost. and. order
processing cost: (3) customers perceptions of customer
service: and (4) levels of attained customer service.

Internal measurement provides an across time indicator of

6
relative performance but it does not help assess how well a
firm is doing in relation to key competitors.

External performance measurement compares a firm's
performance to key competitors. It is often called
benchmarking (Camp 1989). The term benchmarking is used
throughout 13m: dissertation. While internal. performance
measurement is important, benchmarking provides depth to the
positional advantage pictured in Exhibit 1. For example, a
firm that only measures its own customer service does not
fully understand relative competitive advantage.

The second dimension of the typology is the strategic
focus of performance measurement. Strategic focus is related
to either cost or differentiation. The strategic focus is
derived from the generic overall low cost and differentiation
strategies of Porter (1980). With respect to logistics, cost
measures are defined to include logistical productivity and
asset management. Differentiation refers to a distinctive
customer service strategy. Shapiro (1984) provided numerous
examples of low cost and differentiation logistics strategies.

The typology relates four types of performance
measurement: (1) internal cost. measurement; (2) internal
measurement of factors that can be used as differentiators;

(3) cost benchmarking; and (4) differentiation benchmarking.

CONSTRUCTS AND THEORETICAL FRAMEWORK

The linkages examined empirically in this research are
presented in Exhibit 3. First, the dependent or endogenous
variables in the box are the four types of performance
measurement. Total or combined internal and benchmarking
performance measurement offer two additional dependent
variables that are also examined. Thus, the research design
treats six dependent variables. In addition, relationships
existing between benchmarking and internal performance
measurements are examined.

Second, four categories that represent independent or
exogenous constructs are illustrated across the top of Exhibit
3. They are: (1) logistics strategy; (2) structuring
variables describing the latent organizational structure of
the logistics function; (3) pictorial configuration variables
which also describe the organizational structure of logistics:
and (4) channel position. A theoretical framework that
incorporates these constructs is discussed next.

Contingency theory, which provides an approach for
linking environment, strategy, structure and performance,
interconnects the constructs examined in the dissertation.
Environment is examined through the channel position at which
an organization operates. In this research, the positions
examined are manufacturers, wholesalers and retailers.
strategy is examined through how frequently the logistics
strategic plan is updated.

structure is examined through various structuring and

8

Exhibit 3
TESTING FRAMEWORK

 

 

INDEPENDENT VARIABLES

Strategy Structuring Pictorial Channel

Variables Configuration Position

 

DEPENDENT VARIABLES
Internal Cost Performance Cost Benchmarking
Measurement Performance Measurement

Internal Differentiation Differentiation Benchmarking
Performance Measurement Performance Measurement

Combined Internal Combined Benchmarking
Performance Measurement Performance Measurement

 

 

pictorial configuration variables (Dalton gt gt. 1981).
Structuring variables refer to latent constructs such as: (1)
formalization which consists of formal rules, policies,
procedures, cost and quality' control measures; (2)
centralization which is the vertical locus of decision-making
authority; and (3) specialization. Discussion of structuring
variables has long standing in the management literature (Hage
1965: Pugh gt gt. 1967; Miller and Droge 1986).
Pictorial configuration variables can literally be drawn.
Examples are functional versus divisional organizational
structures, tall versus flat hierarchies, activity allocation

to various functions and spans of control.

ANALYSIS

The analyses is conducted in two stages. First, each of
the strategy, structuring and pictorial configuration
independent variables is modeled with the channel position
variable as a covariate. Second, separate subgroup analysis
of manufacturers, wholesalers and retailers is completed.
This two stage approach.helps isolate whether the relationship
between logistics function structure and. performance
measurement is invariant across channel positions. This will
allow generalizations regarding the extent to which
manufacturers, wholesalers and retailers are similar or

dissimilar with respect to logistics performance measurement.

POTENTIAL CONTRIBUTIONS

Contributions of the dissertation are expected in two
areas. First, from a theoretical perspective, elements of
structure and strategy of the logistics function.have not been
examined in relation to performance measurement. The research
design includes hypotheses to potentially facilitate new
theoretical insights about logistics performance measurement
and channel position. A theoretical contribution may result
from an examination of the extent to which manufacturers,
wholesalers and retailers are comparatively similar or
dissimilar.

The second potential contribution of the dissertation is

the generation of managerially relevant guidelines for

10
logistics structure and strategy. For example, the hypotheses
could yield insights into what role.a formal logistics mission
statement plays in encouraging performance measurement.
Another example of a potential contribution is the relatedness
of the senior logistics executive's title to performance
measurement. These results could offer managerial guidelines
toward understanding the importance or lack thereof of the
vertical organizational positioning of the senior logistics

executive.

ORGANIZATION OF THE DISSERTATION

Chapter II initially presents a description of
contingency theory which is used to provide a framework for
hypothesis development and testing. This is followed by
sections on performance measurement, logistics strategy,
latent structuring of the logistics function, pictorial
configuration of the logistics function, and channel position.
In. each section, the appropriate ‘variables are defined,
relevant empirical and conceptual research is reviewed, and
research hypotheses developed. Chapter II concludes with a
summary of the research hypotheses. Statistical hypotheses
are presented in Appendix D.

Chapter III presents methodology. The samples from which
the data was collected are discussed. Questionnaire design
and scaling considerations are also detailed. Chapter IV

contains results of statistical tests. Chapter V presents

11
managerial implications of the research and discusses the
implications of the research for Leading Edge logistics
(Bowersox gt gt. 1989). The chapter concludes with

suggestions for future research, limitations and a final

summary .

CHAPTER II: THEORETICAL AND EMPIRICAL BACKGROUND

Chapter II is reviews relevant literature and presents
the hypotheses that are tested. The first section of this
chapter presents the contingency theory framework of
organizational design. To illustrate the complex manner in
which environment, strategy, structure and performance
interact, three types of relationships are discussed: (1)
relationships among indicators of a: single construct; (2)
relationships between pairs of constructs; and (3) the effect
of "fit" between pairs of constructs on a third construct.

A section on reviewing internal and benchmarking
performance measurement follows and hypotheses are proposed
relating performance measurement variables to one another.

The :next 'three sections discuss logistics strategy,
structuring of logistics and pictorial configuration,
respectively. In each section, empirical research focusing
on the structure of the logistics function is reviewed and
hypotheses developed.

Channel position is discussed in the sixth section. The
last section summarizes the hypotheses that are examined in

this research.

12

THEORETICAL BACKGROUND

Exhibit 4 depicts the contingency theory frameworkn This
theory focuses on four constructs: environment, strategy,
structure and performance. The following sections define

these constructs and describe contingency relationships.

ENVIRONMENT

Environment refers to organizational contexts which
cannot easily be altered in the short-run. The most
frequently discussed contextual variables are size, technology
and uncertainty (Lawrence and Lorsch 1967; Duncan 1972;
Ruekert, Walker and Roering 1985) . Other contextual variables
have been discussed including stage of the product or business
life cycle (Anderson and Zeithaml 1984) and percent of
manufactured output made to stock (Persson 1982: Christopher
1986; Phofl and Zollner 1987).

Organizational size is multidimensional and
operationalizations include measures of physical output such
sales, the number of employees, or resources available such
as net assets or profits (Jackson, Morgan and Paolillo 1986,
p. 216).

Technology is defined as: "The techniques and technical
process an organization uses to change inputs such as
materials, knowledge, energy, and capital into outputs such
as products or services" (Jackson, Morgan and Paolillo 1986,

p.242). While the definition of technology applies to all

13

14

Exhibit 4
THEORETI CAL FRAMEWORK

 

 

Fit

ENVIRONMENT :DSTRATEGY

 

 

 

 

I I I

Fit :DPERFORMANCE <: Fit

I

 

 

 

STRUCTURE

/

Structuring Pictorial
Variables Configuration

 

15
firms, empirical studies generally focus on the effect of
manufacturing technology (n1 structure (Woodward 1965;
Khandwella 1974: Marsh and Mannari 1981; Lincoln, Hanada and
McBride 1986; Miller and Droge 1986). Measurement frequently
distinguishes among custom, small batch, large batch, mass
production and continuous process manufacturing technologies.

Uncertainty refers to actual and perceived levels of
environmental uncertainty. Perceived uncertainty may actually
be more important than actual uncertainty since management
reacts to environmental perceptions (Duncan 1972). Elements
of uncertainty include predictability of competitors,
variability of customer requirements, and heterogeneity of
products in the marketplace (Miller and Droge 1986).

Another environmental variable receiving attention is
percent of manufactured output made-to-stock. Thompson (1967)
discussed this variable claiming that firms producing to stock
"buffer" internal operations from environmental variances.
The proportion of output made-to-stock has been linked to
"task predictability" (Christopher 1986). As firms produce
a greater proportion of output to order, the predictability
of logistical tasks declines. Furthermore, Blois (1980) and
Hayes and Wheelwright (1979) related percent of manufactured
output made-to-stock to manufacturing technology. They argued
that made-to-order manufacturers are more likely to use custom
or batch technologies while made-to-stock firms are more
likely to use mass production or continuous process

technology.

16

Channel position has not explicitly been identified in
the literature as an environmental variable. However, other
descriptors of firm "type" are found in contingency research.
Child and Mansfield (1972) explored how "work-flow"
integration, a specific operationalization of technology,
relates to structure. They examined manufacturers and service
firms, at first simultaneously, then separately, and found
some differences. For relationships between specific
constructs, the manufacturing versus service distinction
appears important.

Channel position is not the same as channel structure.
Channel structure describes how a channel is organized for a
product or an industry. A study of channel structure would
compare channels across products or industries. One goal of
this research is to compare manufacturers, wholesalers and

retailers without regard to product or industry type.

STRATEGY

Strategy refers to the methods by‘ which goals are
achieved. Three strategic typologies receiving considerable
literature attention are discussed. Porter's (1980) typology
consists of the generic low cost, differentiated and focus
strategies. Recently, the mutual exclusivity of this typology
has been questioned (Miller 1988). A firm need not follow one
and only one strategy: a firm can be a differentiator
servicing a focused market niche.

The Miles and Snow (1978) typology is based on new

17
product introduction. strategy. Ftu: example, prospectors
aggressively search for both new products and new markets for
existing products, while reactors introduce new products only
if they are seriously threatened.

Another important strategic variable is product
diversification. Chandler (1962) concluded that the
divisionalized structure was primarily instituted in response
to product diversification. A typology based on
diversification is Rumelt's (1982) related, unrelated, single

and dominant strategic typology.

STRUCTURE

Structure refers to the manner in which the firm is
organized. As seen in Exhibit 4, structure can been
dichotomized in structuring variables, or latent constructs,
and pictorial configuration variables, or variables that can
literally be drawn (Dalton gt gt. 1980). Structuring
variables include: (1) formalization, or the existence of
formal rules, policies, cost.and quality control measures; (2)
centralization, or the vertical locus of decision-making
authority; and (3) specialization, or the extent to which
individuals and/or departments specialize. Integration has
been identified as an important structuring variable
(Mintzberg 1979; Bowersox, Closs and Helferich 1986).

Pictorial configuration variables include, but are not
limited to: (1) vertical differentiation, or the number of

layers in an organization: (2) horizontal differentiation, or

18
the number of units or departments; (3) divisional versus
functional forms; (4) activity sharing across business units
in a divisionalized firm; (5) spans of control, or the number
of subordinates reporting directly to, for example, a CEO or
a factory foreman; and (6) activity allocation to various

functions.

PERFORMANCE

Performance is a multidimensional construct consisting
of: (1) efficiency, or how well inputs are used: (2)
effectiveness, or how'well the organization is able to achieve
goals: and (3) and flexibility. Flexibility refers to either
long-term adaptability to changing environmental conditions
through strategic new product introductions or to short-term
operational adaptability to temporary marketplace alterations

(Phofl and Zollner 1987).

CONTINGENCY RELATIONSHIPS

Three types of contingency relationships are typically
researched: (1) relationships among elements of a particular
construct: (2) relationships between pairs of constructs; and
(3) the effect of the relationship between a particular pair
on another construct. All three constitute contingency
relationships, although the latter type is the most
sophisticated since it tests whether the "fit" between pairs
of constructs is dependent on or is related to other

constructs“ In order to fully understand the nature and logic

19
of contingency theory, eaCh of the three types of
relationships are discussed.

First, relationships among the elements of a single
construct can be examined. When Buzzell, Gale and Sultan
(1975) concluded that market share and return on investment
were positively related, they examined a relationship between
two dimensions of performance. Hage (1965) argued
conceptually that flexibility and efficiency were negatively
related.

The second type of relationship is that existing between
pairs of constructs. A few examples of how strategy is
related to other constructs arejpresentedn ‘Varadarajan (1986)
examined how product diversification affects financial
performance. vancil (1979) examined the effect of product
diversification on resource sharing across business units, a
pictorial configuration structure variable. Gupta and
Govindarajan (1986) examined the effect of low cost versus
differentiation strategy on resource sharing across business
units. Walker and Ruekert (1987) described how firms should
be structured, along structuring dimensions, in response to
combinations of the Miles and Snow (1978) and Porter's (1980)
strategic typologies.

Research.has frequently focused on relationships between
environment and structure (Ford and Slocum 1977: Fry 1982).
Firms operating in stable, certain environments are more
formalized, leSS<centralized.and.rely less.on specialists than

firms operating in complex, uncertain environments (Lawrence

20

and Lorsch 1967). Khandwella (1974) examined the effect of
manufacturing technology and size on structure. Argarwal
(1979) examined how size and structure are related and
reported nonlinearities, while Blau gt gt. (1976) examined the
effect of computerization technology on structure. For a
review of contingency ‘theory research. in. marketing, see
Zeithaml, Varadarajan and Zeithaml (1988).

The third.type of relationship emerging in the literature
examines "fit." The essence of "fit" theory is that the level
of "fit" between environment and structure, strategy and
structure, and environment and strategy affects performance.
In other words, performance is dependent on the level of "fit"
between pairs of constructs.

Two difficulties are associated with "fit" theory.
First, fitness testing requires larger sample sizes than'those
needed to test the first two types of contingency
relationships. Second, the level of "fit" between a pair of
constructs may be more important than the level of "fit"
between other pairs of constructs. There can exist a large
number of divergent environmental and strategic antecedents,
and the firm "fitting" structure to all might do very little
properly (Galbraith and Nathenson 1978).

Drazin and Van de Ven (1985) discussed empirical
congruencies between pairs of constructs. They claimed
congruencies exist between environment and structure, for
example, because firms are capable of "fitting" structure to

environment (John and Martin 1984: Morgan 1986).

21

Miller (1988) provides an example of testing "fit." He
split the firms in his sample into those that had high and low
levels of performance and then tested the strength of
relationships between strategy and structure variables,
between. environment. and structure ‘variables, and. between
environment.and strategy variables. Ifimzstrategy'-- structure
relationships were significantly stronger for high performers.
The interpretation is that high performing firms are better
"fitting" structure to strategy.

Alternatively, one could test whether certain strategies
are more profitable under specific environmental contexts, or
whether specific structures are more profitable under specific
strategies. Hamermesh and White (1984) found that business
units following a low cost strategy were more profitable when
they shared resources with other business units than when they
did not share resources with other business units.
Conversely, they found, the opposite 'when business units
followed a differentiation strategy. The interpretation is
that the level of "fit" between strategy and structure affects
performance.

McKee, Varadarajan and Pride (1989) tested whether
strategy types, as defined by the Miles and Snow (1978)
typology, were more profitable under specific environmental
contexts. Again, this is another example of "fit" testing.

In summary, the environment, strategy, structure and
performance constructs underlying the contingency theory

approach to organizational design and three types of

22

relationships among these constructs were defined.

PERFORMANCE MEASUREMENT

Performance 'measurement. has long' been researched in
management. Taylor (1911) introduced "scientific management"
and modernized business by applying scientific methods to the
study of management. Taylor, a proponent of time and motion
study, monitored a method of working, measured performance,
altered the method of working, and remeasured performance.
He was able to determine if the alteration improved perfor-
ance. This sequence of events still applies in many cases
today as a firm may alter inventory levels in expectation of
lower costs. In logistics, monitoring performance is critical
because the potential of trade-off analysis cannot be realized
until performance is measured (Tyndall and Busher 1985).

The discussion of performance measurement is presented
in three parts. The first two sections discuss internal and
benchmarking measurement, respectiveky. The third section
develops hypotheses concerning how performance measurement

variables are expected to be interrelated.

INTERNAL PERFORMANCE MEASUREMENT

Bowersox gt gt. (1989) discussed five areas in which
internal logistics performance measurement can focus: asset
management, cost, productivity, customer service and quality.

Each is discussed separately.

23

Asset management refers to how efficiently assets are
utilized over a specified time period. Emamples of asset
management performance measures include: (1) return on net
assets, or profit after taxes divided by total assets; (2)
return on investment, or profit after taxes divided by net
investment; and (3) inventory turnover, or cost of goods sold
divided by the value of inventory (Soloman and Pringle 1980;
Schall and Haley 1983; Halloran and Lanser 1985). Asset
management performance measures are important because they
focus on the end result of logistical practices. For example,
the level of inventory investment to support a particular
level of sales volume directly impacts return on investment
and profitability. The importance of asset management to
logisticians is not unique. Return on investment has come
under close scrutiny by marketers (Jackson, Ostrum and Evans
1982; Jacobson and Aaker 1985; Jacobson 1988). Asset
management is also of equal importance to manufacturers,
wholesalers and retailers.

The measurement of internal logistical costs is
important. An understanding of costs allows the firm to
implement trade-off analysis. The components that comprise
total cost analysis are transportation costs, warehousing
costs, order processing costs, lot quantity costs and the cost
of lost sales due to lack.of stock (Lekashman and Stolle 1965;
Lambert and Mentzer 1980; Stock and Lambert 1987). The cost
of lost sales is generally not used in total cost analyisi

because of estimation difficulties. Unlike the other

24
components of total cost analysis, the cost of lost sales is
dependent on how product availability and customers' reaction
to stockouts are related.

The third performance measurement area identified by
Bowersox gt gt. (1989) is productivity. Productivity ratios
are units of output divided by units of input (Chew 1988).
Important logistics-related productivity' measures include
units shipped per employee and units per labor dollar. The
use of too few productivity indices can decrease productivity
rather than promote it (Skinner 1986). Management can be
sensitized to a small number of productivity indices and
ignore other equally important.but unused indices. 'Ehis could
results in productivity gains on the indices being used, but
productivity declines on other indices. It is the
productivity of the entire system that is critical, and not
the productivity of a given index.

The fourth area of performance measurement is customer
service. Important components of customer service include
fill rate, stockouts, on-timeadelivery'and backorders (LaLonde
and Zinszer 1976). The overall objective of the logistics
approach to customer service is to minimize total logistics
costs subject to meeting prespecified service levels.
Measuring customer service performance is thus of critical
importance to the organization.

The last area of performance measurement is quality.
Quality refers to: "how effectively the logistics task is

performed" (Bowersox, Closs and Helferich 1986, p. 274).

25
Examples of quality-related performance measures include
frequency of damage, dollar amount of damage and number of
customer returns. Quality is related to differentiation since
it is associated with the quality of the service provided to
customers. In other words, a firm can differentiate itself
from competitors on the basis of logistical quality.

Three of the five performance measurement areas are
related to organizational efficiency, or costs: asset
management, cost and productivity. The remaining two
performance measurement areas, customer service and quality
of service, are related to differentiation. Shapiro (1984)
examined generic logistics strategies, two of which were the
low cost and differentiated strategies. The strategic thrust
of performance measurement may be related to the strategic
thrust of the organization and of logistics.

Only a few studies have examined.how logistical organiza-
tional factors affect internal performance measurement.
Bowersox gt gt, (1989) reported that manufacturers, whole—
salers and retailers engage in more performance measurement
as logistical sophistication increases. Leading Edge
organizations, use larger numbers of asset management, cost,
productivity, customer service and quality performance
measures.

Lambert and Sterling (1987) examined performance reports
received by marketing managers. They found 28% of managers
receive profitability reports containing direct variable

transportation costs. They, however, made no effort to

26
examine organizational factors affecting type of report
received.

A study by Jackson, Ostrum and Evans (1982) examined
measures used to evaluate marketing performance. The measures
examined were related to sales statistics, cost, margins and
return on investment. They explored how these measures were
related to "control segments" such as order size, market
segment, and geographical area. Similar to Lambert and
Sterling (1987), they did not examine how organizational
factors relate to type of measures or control segments used.

Khandwella (1974) examined the use of formal "internal"
controls as a function of several organizational design
variables. While size and type of manufacturing technology
were found to be related to the use of internal controls,
Khandwella (1974), using path analysis, concluded that
variables describing other elements of cmganizational
structure such as centralization and formalization intervened
the relationship between size and use of controls and between
technology and the use of controls.

In summary, the logistics literature has yet to fully
examine how logistics strategy and structure are related to
performance measurement. The following section reviews the

literature on benchmarking performance measurement.

STRATEGIC INTELLIGENCE AND BENCHMARKING
Benchmarking has been discussed in conjunction with

strategic intelligence and environmental scanning. Strategic

27

intelligence is: " . . . information about competitor capabilities
and intentions that provides a basis for planning long term
strategy and goals" (Hershey 1980, p. 22). Strategic
intelligence is distinct from strategic espionage, although
the exact boundary between the two is at times blurred.
Strategic espionage often involves activities of a clandestine
nature which, if not illegal, are unethical (Wall 1974).
These activities include theft, bribery, extortion and/or
wiretapping (Sammon, Kurland and Spitalnic 1984). The
difference between strategic intelligence and espionage thus
lies in data collection methods and not in goals.

Strategic intelligence represents the gathering of
environmental information to guide long term strategy
formulation. Porter (1980) suggested the following sources
of strategic intelligence: publicly filed reports, speeches
given by executives, business press, the salesforce, customers
and/or suppliers, inspection of competitor's products,
engineering estimates and managerial insight.

Montgomery and Weinberg (1979) discussed three purposes

of strategic intelligence. First, strategic intelligence can

be defensive. The goal is to determine whether basic
assumptions about the environment hold . The essence of
defensive intelligence is to minimize "sudden surprises." An

example of a defensive intelligence act is when a firm
monitors the prices competitors are charging.
The second type of intelligence is passive and refers to

comparing competitor performance to that of the firm. This

28
type of intelligence enables the firm to determine positional
advantage. Montgomery and Weinberg (1979) state that:
"Passive intelligence is designed to provide benchmark data
for objective evaluation" (p. 42).

The third of strategic intelligence is offensive in
nature. It is undertaken to identify opportunities and spot
problems. The firm can then act to improve its competitive
position or to protect its present market position.

Montgomery and Weinberg (1979) also discussed six areas
in which strategic intelligence can focus: competitive,
technological, customer, economic, political/regulatory and
social. The benchmarking variable examined in this research
include competitive, technological and customer facets.

The bounds delineating the three purposes of strategic
intelligence are themselves not all that distinct. McLagen
and Zeismer (1982) suggested that cost benchmarking, which
would appear to be defensive, can lead to an understanding of
opportunities or problems. Thus, if cost benchmarking is
undertaken by a firm, it is difficult, if not impossible, to
determine whether the purpose of the intelligence operation
is offensive or passive. Knowledge of the act of benchmarking
gives little insight into the purpose or motive behind the
act.

Sharman (1984) discussed how profiles of competitors
along logistical customer service levels can be used by firms
to spot problems and opportunities. The profiles are similar

to those used in product positioning research to plot customer

29
perceptions of competing products. The development of
competitor profiles requires some form of information about
competitor performance. This method of spotting opportunities
is thus one type competitor intelligence.

Porter (1980) suggested that the extent to which firms
engage in strategic intelligence is related to organizational
factors, including organizational needs, industry
characteristics, staff capability, management interest and
management ‘talent. As already' mentioned, this list is
extended to include logistics strategy and structure.

Empirical support of industry differences in the
importance of type of strategic intelligence is provided by
Wall (1974). Across industries he found that managers most
frequently felt the need. to know information concerning
competitor pricing. In terms of rank order, this was followed
by expansion plans, competitive plans, promotional strategy,
cost data, sales statistics, research.and.development, product
styling, financing and executive compensation. Customer
service was not examined in this study. Industry differences
in the importance of type of intelligence gathered.were found.
For'example, for the retail and wholesale industries, the most
important aspect of competitor intelligence was pricing, while
for the advertising, media and publishing industries, the most
important aspect was competitive plans.

Zinkham and Gelb (1985) demonstrated that organizational
and industry characteristics affect the extent of engagement

in competitor intelligence. They found the amount of

30

competitor intelligence gathered by industrial marketing firms
was positively related to the number of competitors in the
industry, a "build share" versus "harvest" marketing strategy,
and whether or not the purpose of the intelligence operation
was offensive. Conversely, the amount of intelligence
gathered was less for firms with a passive intelligence
purpose. Organizational size, as measured by dollar sales,
was found not to be related to the amount of intelligence
gathered.

In summary, this section described two types of
performance measurement. While limited research exists on
organizational factors impacting’ performance measurement,
little research was identified that examined how internal and
benchmarking performance measurement are related, especially

in the context of logistics across channel positions.

HYPOTHESES

Both internal performance measurement and benchmarking
essentially represent different facets of a formal performance
measurement or formal control construct. For this reason,
internal performance measurement and benchmarking measurement
should be positively correlated.

This generalized relationship can be made specific for
particular dimensions of performance measurement. Firms
following a low cost logistics strategy may be more committed
to measuring logistics costs, productivity and/or asset

management. On the other hand, some firms may be more

31

committed to providing a differentiating level of customer
service, and thus may place a larger degree of importance on
measuring customer service and qualityu The result of varying
degrees of commitment to various strategic foci means that
firms benchmarking in one area, say differentiation, may
measure a larger number of items of internal performance
within the same area. These expectations are stated in the
following hypothesis.

H1: The use of internal performance measures is
positively related with the use of benchmarking
performance measures; (Hla) cost benchmarking is
related to the use of internal cost performance
measures; and (Hlb) differentiation benchmarking is
related to the use of internal differentiation
performance measures.

The hypothesis does not imply that internal performance
measurement "causes" benchmarking performance measurement or
vice versa, or that cost benchmarking "causes" internal cost
performance measurement. Rather, a relationship between two

types of performance measurement is indicative of a firm's

commitment to monitoring performance.

LOGISTICS STRATEGY

'Logistics strategy has increasingly received attention
in the literature. Shapiro (1984) discussed generic logistics
strategies including low cost, differentiation and new product
introduction strategies. Bowersox and Daugherty (1987)

discussed process versus market versus channel strategies.

32

The strategy"variable, examined. in ‘the «dissertation,
however, is not related to generic logistics strategies.
Rather, it is the frequency with which the logistics strategic
plan is updated. Daugherty (1988) reported that this variable
was unrelated to anticipated use of outside service vendors.
Make-to-stock manufacturers update their logistics strategic
plan less frequently than make-to-order manufacturers (Germain
19890. The latter finding suggests that environmental factors
influence the frequency of strategic plan updating. Make-to-
order manufacturers face more complex, unstable and
unpredictable markets and customer requirements than make-to-
stock manufacturers.

The same may also apply for wholesalers and retailers.
Those that frequently update their strategic plan could face
unstable and unpredictable markets and customer requirements.
What should be considered is how firms facing unstable markets
compare to firms facing stable markets in terms of the extent
of performance measurement. There is no obvious reason why
market stability should be related to cost versus
differentiation performance measurement. run: is there any
reason to suspect that market complexity should be related to
internal performance measurement, for example, but not
benchmarking performance measurement. However, the
possibility exists that total performance measurement, both
internal and benchmarking, could be related to the frequency
of updating the logistics strategic plan. A firm in a stable

market could plan longer into the future than a firm in an

33

unstable‘marketu Stability could imply that organizations are
more willing to invest.in.performance measurement since market
changes over time are less likely to invalidate the
performance measures being used. Conversely, market
instability could retard performance measures since the firm's
investment in performance measurement could be invalidated by,
for example, changing customer requirements.

This expectation is formally expressed in the following
hypothesis.

H2: The frequency with which the logistics strategic

plan is updated is inversely related to the use of
performance measures.

STRUCTURING VARIABLES

The study of specific structuring variables such as
formalization, has long been explored in management and can
be traced back at least to Weber during the late 19th century
(Hage 1965). In logistics, and in marketing, certain
structuring variables have recently received attention.
LaLonde and Zinszer (1976) stated that a written customer
service mission statement was a pretransaction component of
customer service. More recently, Bowersox gt gt. (1989)
discussed: (1) the importance of a written logistics mission
statement as an integrative device; and (2) the importance of
a formal logistics strategic plan as a strategic planning

tool. A written customer service mission statement, a written

34
logistics mission statement and a formal logistics strategic
plan are indicative of the level of formalization, a
structuring variable.

Research has been conducted on customer service and
channel conflict (Levy 1981a), segmenting the market on the
basis of customer service (Gilmour 1982), cross industry
customer service differences (LaLonde, Cooper and Noordewier
1988), customer service management (Levy 1981b; Uhr, Houck and
Rogers 1981; Christopher 1983; Tucker 1983; Jackson, Keith
and Burdick 1986), and on logistics strategy (Shapiro 1984;
Shapiro and Heskett 1985; Stock and Lambert 1987; Gattorna
1988). However, very little research has been conducted on
how structuring variables are related to performance
measurement.

The structuring constructs that are examined in the
dissertation are: (1) formalization, as indicated by the
existence of a formal logistics mission statement, the
existence of a formal logistics strategic plan, and maturity,
or the length of time that logistics has been a formal
function within the firm; (2) reorganization, as indicated by
the number of times that the logistics function has been
reorganized during the previous five years; and (3)
participation in business unit strategic planning by the
senior logistics executive. The number of reorganizations is
an important variable describing the static nature of the
organization. It is considered a structuring variable since

it cannot be pictorially represented.

35
The following two sections review the limited empirical
research examining logistical structuring variables in
relation to other organizational variables. A brief summary

and hypotheses development follow.

STRUCTURING VARIABLES AS DEPENDENT VARIABLES

Exhibit 5 lists empirical studies which have examined
the structuring of logistics as a dependent variable. Each
study is discussed separately.

A study that examined logistical formalization was
conducted by Bowersox gt gt. (1989). They found that leading
edge firms, as indicated by a common attributes index, were
more likely than other firms to have a formal logistics
mission statement, a formal logistics strategic plan, to have
had logistics organized as a formal function for a longer
period of time, and to have reorganized the logistics function
a larger number of times over the past five years.

A relationship was found between the proportion of
manufactured output made-to-stock versus made-to-order and
logistical formalization (Germain 1989). Make-to-stock firms
are more formalized than make-to-order with respect to a
formal logistics mission statement and a formal logistics
strategic plan. This finding supports contingency theorists
who suggest that logistical task predictability, an
environmental variable, of make-to-stock firms is higher than
the task predictability of make-to-order firms, and that

higher levels of task predictability are more conducive to a

36

Exhibit 5

EMPIRICAL RESEARCH WITH STRUCTURING VARIABLES

AS DEPENDENT VARIABLES

 

 

Study

Bowersox
et a1. (1989)

Germain (1989)

Droge and Germain
(1989)

Major Constructs

Formalization, maturity and
reorganization as a function of
leading edge logistics.

Formalization as a function of
percent of manufactured output to
customer order.

Formalization and participation

in business unit strategic planning
by the senior logistics executive as
a function of functional versus
divisional structure and centralized
versus decentralized structures.

 

37
formalized logistics function (Persson 1982; Christopher 1986:
Phofl and Zollner 1987). In general, the more predictable the
task, the more the cost savings that can result from a
formalized bureaucracy.

Droge and Germain (1989) found that functionally
structured manufacturers with a decentralized logistics
function were less likely to have a formal logistics mission
statement and a formal logistics strategic plan than
divisionalized manufacturers with logistics centralized in a
cross-divisional function. In the case of the latter, the
logistics function had both line and staff responsibility
cutting across divisions. Participation by the senior
logistics executive in business unit strategic planning was
found to be independent of the pictorial configuration of

logistics.

STRUCTURING VARIABLES AS INDEPENDENT VARIABLES

Exhibit 6 lists empirical studies which have examined
logistics structuring variables as independent variable.

Kyj (1987) examined relationships between the existence
of a formal customer service mission statement and attitudes
of senior executives. This is related to logistics since the
logistics function in many firms has line authority over
customer service (Bowersox e_t_ gt. 1989). Kyj (1987) found
that executives in firms with a written customer service
mission statement were more likely to believe that customer

service is used as a competitive weapon. In addition, a

38

Exhibit 6

EMPIRICAL RESEARCH WITH STRUCTURING VARIABLES

AS INDEPENDENT VARIABLES

 

 

Study

Kyj (1987)

Daugherty (1988)

Germain and
Cooper (1989)

Major Constructs

Use of logistical customer service
as a competitive weapon and the
importance of elements of customer
service as a function of whether or
not a formal customer service
mission statement exists.

Use of outside service vendors as a
function of formalization, maturity
of the logistics function,
reorganizations and participation
in business unit strategic planning
by the senior logistics executive.

Use of customer surveys,
quantitative measures of customer
service and total number of customer
service performance measures kept as
a function of whether or not a
formal customer service mission
statement exists.

 

39
number of customer service elements were found to be more
important for firms with a written customer service mission
statement. For example, the provision of backorder
information was considered to be more important in firms with
a written customer service mission statement.

Daugherty (1988) examined how formalization, maturity of
the logistics function, reorganizations and participation by
the senior logistics executive in business unit strategic
planning are related to the use of outside service vendors.
Daugherty (1988) reported.that.maturity“was positively related
to anticipated outsourcing of inventory management, and
reorganization and participation in business unit strategic
planning by the senior logistics executive was positively
related to anticipated outsourcing of order entry/processing
services.

A third study that examined formalization as an
independent variable was conducted by Germain and Cooper
(1989). They found that firms with.a written customer service
mission or policy statement were far more likely to survey
customers and. to keep quantitative ‘measures of customer
service performance. In addition, these same firms actually
measured customer service performance in a larger number of
ways. This result was found to hold across three types of
firms: manufacturers, industrial service firms and consumer
service firms.

In summary, structuring variables describing the

logistics function have been examined empirically both as

40

independent and dependent variables. Much of this research
is grounded, if not explicitly, in the contingency theory
framework, Little research has explored, potential
relationships between structuring variables and formal

performance measurement.

HYPOTHESES

Based on the previous discussion, three hypotheses are
proposed. They are concerned with the relationship between
the structuring of the logistics function and performance
measurement.

The mission statement, strategic plan and maturity
variables, all dimensions of formalization, should be
positively related to the usage of performance measures. 'This
is expected because performance measurement is also a
dimension of formalization. In addition, a logistics mission
statement and a logistics strategic plan often state
performance criteria. Measurement of performance becomes
necessary to determine if these criteria are being met.

The length of time that logistics has been a formal
function should be related to the use of performance measures.
Increasing maturity of the logistics function implies that
logistics has had a longer time to implement an increasingly
sophisticated performance measurement system.

These expectations are expressed in the following

hypothesis.

41

H3: Formalization is positively related to the use

of performance measures.

This hypothesis implies that all four types of
performance measurement plus total internal and total
benchmarking performance measurement, vary positively with the
level of formalization of the logistics function.

The number of reorganizations that the logistics function
has undertaken during the past five years is expected to be
related to performance measurement. More frequent
reorganizations of logistics could signify, as described by
Bowersox g gt. (1989), leading edge logistics. In other
words, frequent reorganizations could be an indicator of the
organization's overall commitment to logistics. This
increased commitment to logistics could be reflected in an
increased belief in the importance of measuring the
performance of logistics.

This expectation is expressed in the fellowing
hypothesis.

H4: The frequency with which the logistics function

is reorganized is positively related to the use of

performance measures.

The last structuring variable examined in this research
is participation by the senior logistics executive in business
unit strategic planning. Increasing levels of participation
by the senior logistics executive should be related to all
four types of performance measurement. Participating in

overall strategic planning implies increasing importance of

42

and reliance on logistics. This could result in a greater
reliance (n1 performance measurement. Internal performance
results could be filtered up chains of command to senior
executives. The senior logistics executive participating in
strategic planning may integrate logistics into overall
corporate planning. Just as manufacturing and marketing
positional advantages are used to plot a future organizational
course, so could positional advantages derived from logistics.
Hence, participation in business unit strategic planning by
the senior logistics executive is expected to be related to
internal and benchmarking performance measurement, and it is
not expected to be related to the strategic focus of the
performance measurement.

These expectations are expressed in the following
hypothesis.

H5: Participation by the senior logistics executive

in business units strategic planning is positively
related to the use of performance measures.

PICTORIAL CONFIGURATION

Certain dimensions of the pictorial configuration of the
logistics function have been discussed in the literature.
Stolle (1967) discussedwwhether'activities should.belallocated
to logistics on a staff basis, a line basis, or a combination
of both. Schiff (1972) provided further delineation of

organizational structure types using the case study method to

43
discuss staff, line and staff-line organization of logistics.
The allocation of activities in a staff versus line manner is
one of many dimensions of pictorial configuration.

Another issue is: which activities should the logistics
function control? The stages paradigm (A.T. Kearney 1981;
Bowersox and Daugherty 1987) and the closely related
"organizational types" (Bowersox, Closs and Helferich 1986,
p. 304) partly address this question.

These two perspectives on the organizational design of
logistics each present three "stages" or "types" of
structures. Exhibit 7 presents the basic outline of the
stages paradigm. The underlying logic of the stages paradigm
is that the eventual integration of logistical activities
proceeds through a series of recognizable stages. The process
is evolutionary, rather than revolutionary. As shown in
Exhibit 7, organizations first integrate outbound
transportation and finished goods field warehousing. While
the focus is operational, cost savings can arise from
balancing the number and location of warehouses against total
transportation costs.

Once these two logistical activities have been
integrated, further cost reductions can arise if physical
supply and physical distribution are integrated. Inbound and
outbound transportation systems are coordinated under a single
organizational function. In this manner, new trade-offs are
made across functions.

In the last stage, materials management activities are

L’T"

44

Exhibit 7
STAGES PARADIGM *

 

 

Stage I

Logistics activities dispersed across
various functions. "Core" activities of
outbound transportation and finished
goods warehousing integrated under a
function typically called physical
distribution. Operations orientation.

Stage II

Integration of physical distribution and
physical supply. Inbound and outbound
transportation coordinated under a single
function. Trade—offs made across
functions.

Stage III

Physical supply, physical distribution
and materials management integrated under
a single function which can be called
logistics. Trade-offs being made across
functions. Strategic nature of logistics
recognized and utilized by the
organization.

 

* from A.T Kearney (1981) and Bowersox, Closs and
Helferich (1986. p. 312—313.)

(n

In

45
integrated with Stage II activities. The focus of logistics
at this point becomes strategic as well as operational. The
organization is now positioned to identify and create
positional advantages that can be integrated into long-term
strategic planning.

Each organizational "type" described by Bowersox, Closs
and Helferich (1986) is related to a particular stage in the
stages paradigm. With each type, a typical organizational
chart is presented. These charts illustrate an archetypical
pictorial configuration closely associated with a particular
stage.

"Beyond functions" was recently discussed by Bowersox gt
_t. (1989, p. 79). Beyond functions are activities that are
not traditionally considered to be logistics-related. These
include, but are not limited to, real estate activities, data
processing for distribution applications and facilities
management. As stated by Bowersox gt gt. (1989, p. 80): "The
assignment. of beyond functions means that the logistics
organization and its senior officer have gained sufficient
organizational longevity to be viewed as part of the permanent
management structure."

Another related issue is: How many activities should the
logistics function control? This has also been called the
span of logistics control (Bowersox gt _t. 1989; Droge and
Germain 1989). The concept is independent of the stages

paradigm since it is concerned with how many activities report

to the logistics function, not how many and which activities

46
report to the logistics function, jHowever, even this view'may
be somewhat limited since: "...how you manage logistics is
more important than how many functions are managed" (Bowersox
gt gt. 1989, p. 286).

Still another important variable is the title level of
the senior logistics executive which represents the vertical
position or visibility of the executive. This variable was
recently discussed by Bowersox gt gt. (1989).

The pictorial configuration variables describing the
logistics function examined in this research are: (1) whether
the logistics function.has formal line authority over customer
service; (2) whether the logistics function has formal line
authority over logistics systems planning; (3) the logistics
span of control; and (4) the title level of the senior

logistics executive.

PICTORIAL CONFIGURATION AS DEPENDENT VARIABLES

Exhibit 8 lists empirical studies which have examined
pictorial configuration as a: dependent variable. In some
studies, the structure of the logistics function was of
primary interest, while in others it was embedded in a larger
context. The articles in Exhibit 8 have been grouped into
three categories: (1) resource sharing across divisions; (2)
span of logistical control; and (3) activity allocation to
various hierarchies.

Vancil (1979) researched the business units of

divisionalized manufacturers. Relying on Rumelt's (1982)

47

Exhibit 8

EMPIRICAL RESEARCH WITH PICTORIAL CONFIGURATION AS

DEPENDENT VARIABLES

 

 

Author and Year

Resource Sharing

Vancil (1979)

Gupta and
Govindarajan
(1986)

Germain and
Daugherty
(1989)

Maior Constructs

Resource sharing of resource and
development, manufacturing,
distribution and marketing assets
across strategic business units as
a function product diversification.

Resource sharing across business
units as a function of low cost
and differentiation strategies.

Line and staff versus staff only
consolidation of logistics

activities across divisions of
divisionalized firms as a function of
product diversification.

Span of Loqigtics Control

Bowersox,
et a1. (1989)

Germain (1989)

Droge and Germain
(1989)

Span of logistics control and line
control over individual activities
as a function of leading edge
logistics.

Span of logistics control as a
function of percent of manufactured
output to customer order.

Span of logistics control as a
function of centralized versus
decentralized and functional
versus divisionalized structures.

l 3.”

De

V OI

Di]

Dr<
am
(15

48

Exhibit 8 (Cont'd)

EMPIRICAL RESEARCH WITH PICTORIAL CONFIGURATION AS

DEPENDENT VARIABLES

 

 

Author and Year

Daugherty
and Droge
(1989)

Activity Allocation

Lynagh and Poist
(1984)

Voorhees, Teas,
Allen and
Dinkler (1988)

Droge, Germain
and Daugherty
(1989)

Major Constructs

Span of logistics control as a
function of line and staff versus
staff only logistics resource
sharing across divisions in
divisionalized firms.

Perceptual differences between
marketing and logistics executives
over whether activities should
report to logistics versus
marketing.

Perceptual differences between
marketing and logistics executives
over whether activities should
report to logistics versus
marketing.

Logistics line control over various
activities as a function of
centralized versus decentralized and
functional versus divisionalized
structures.

 

b:

pr
Un
9X.
St:
ac:
St:
diI
Ste
a c
Dal;

div

49

diversification typology, he classified each business unit as
being either a single business, a dominant diversifier, a
related diversifier, or an unrelated diversifier. Vancil
(1979) found that related diversifiers were more likely than
unrelated diversifiers to share manufacturing, selling,
research and development and distribution resources across
business units.

Another study that examined resource sharing as a
function of strategy was conducted by Gupta and Govindarajan
(1986). In this study resource sharing was examined across
six functions, one of which was purchasing. Resource sharing
was greater for organizations pursuing a low cost versus a
differentiation strategy, although the relationship was weak.

Germain and Daugherty (1989) also examined the effect of
product diversification on resource sharing across business
units in divisionalized manufacturers. In particular, they
examined two archetypical resource sharing structures: (1) a
structure where major logistics activities are consolidated
across divisions in a cross divisional function; and (2) a
structure where major logistics activities reside in the
divisions, but a cross divisional structure sharing mostly
staff type responsibilities exists. While Vancil (1979) used
a categorical measure of product diversification, Germain and
Daugherty (1989) used continuous measures of product
diversification based on SIC codes (Varadarajan 1986). They
found that the number of four-digit manufacturing SIC codes

was predictive of logistics structure. As product

50

diversification increased, firms were more likely to use the
structure in which the resource sharing was mostly staff.
These firms are constrained by differences in logistical
requirements across divisions. A diversified organization is
more likely to have heterogeneous product handling equipment,
warehousing requirements, channels and customers across
divisions. In contrast, a less diversified company can share
line resources across divisions and gain economies of scale
because warehouses, customers, channels and product handling
equipment are more likely to be similar across divisions.

The similar findings of Vancil (1979) and Germain and
Daugherty (1989) based on different operationalizations of
product diversity and resource sharing are not without
theoretical support. Similar results have also been found in
relation to other functional areas of the firm. Porter (1985)
claimed that resource sharing across business units is easier
when jproducts are related. because of inherently similar
manufacturing, distribution and selling assets and because of
similar channels of distribution and customers (Phofl and
Zollner 1987).

Another aspect of logistics structure which has recently
been examined is the span of logistics control. Bowersox gt
gt. (1989) reported that the span of logistics control was
related to leading edge logistics. Firms on the leading edge
were found to have a larger span of logistics control than
less sophisticated organizations. Interestingly, this finding

held for manufacturers, wholesalers and retailers suggesting

51
a level of similarity in logistics across channel position.

One study found percent of manufactured output made-to-
order was related to the span of logistics control (Germain
1989). The span of logistics control for make-to-stock
manufacturers exceeded that of make-to-order manufacturers,
possibly in response to market complexity and to the need to
control finished goods inventory. The aggregation of
logistics activities in a single function in response to
production to stock may be indicative of the existence of a
formal logistics function. The need to organize logistics on
a formal basis because of production to stock was noted by
Heskett, Ivie and Glaskowsky (1964, p. 484).

A third study that examined span of logistics control
was conducted by Droge and Germain (1989). Survey respondents
were asked to select that pictorial diagram that best
represented the structure of logistics within the firmu Among
functional firms, those that selected an organizational chart
depicting logistics activities as centralized in a single
function had a larger span of logistics control than those
that selected a diagram depicting logistics activities as
being dispersed across other functions.

A forth study that examined span of logistics control
was conducted by Daugherty and Droge (1989) . The cross-
divisional logistics function in manufacturers that have both
line and staff responsibility was found to have a larger span
of logistics control than the function that has mostly staff

responsibility.

52

Droge, Germain and Daugherty (1989) relied on the same
organizational charts as those used by Droge and Germain
(1989) and found that functionally structured firms that
selected the centralized diagram were more likely than those
that selected the dispersed diagram to have formal line
authority over sales forecasting, customer service and order
processing. CHua previous three studies demonstrated that
various pictorial configuration variables are interrelated.
These studies also demonstrate the difficulties involved in
organizational design research as the same variable can be
exogenous or endogenous depending on the research objective.

Two studies, one by Lynagh and Poist (1984) and the other
by Voorhees gt gt. (1988) examined perceptual differences
between logistics and marketing managers as to which of

logistics versus marketing should control various activities.

PICTORIAL CONFIGURATION AS INDEPENDENT VARIABLES

Exhibit 9 lists empirical studies which have examined
pictorial configuration as an independent variable.

Anderson, Jermin and Constantin (1978) found the
importance of physical distribution goals were not affected
by the degree to which logistics activities were centralized.
1However, the importance of some goals were higher ‘when
logistics controlled a limited number of activities, one of
which was warehousing.

Droge, Germain and Daugherty (1989) studied the impact

of structural variables on the total number of logistics

53

Exhibit 9
EMPIRICAL RESEARCH WITH PICTORIAL CONFIGURATION AS
INDEPENDENT VARIABLES

 

 

 

Author and Year Major Constructs
Anderson, Jermin Importance of physical distribution
and Constantin goals as a function of degree of
(1978) centralization of logistics.

Droge, Germain Computerization and characteristics
and Daugherty of information as a function of
(1989) centralized versus decentralized

and functional versus
divisionalized structures.

Daugherty Use of outside logistics service
and Droge vendors as a function of line and
(1989) staff versus staff only logistics

resource sharing across divisions in
divisionalized firms.

Germain and Formalization of logistics as a
Daugherty function of line and staff versus
(1989) staff only resource sharing of

logistics activities across
divisions in divisionalized firms.

 

54

computer applications and on information related variables.
They found that functionally structured decentralized firms
were less computerized than functionally structured
centralized firms and divisionalized firms“ Inraddition, they
found a relationship between pictorial configuration and the
impact of logistics communication with other functions.

Daugherty and Droge (1989) found a relationship between
the pictorial configuration of logistics and perceptions
concerning anticipated use of outside service vendors such as
public warehousing, transportation services. (Rm: types of
divisional firms were compared: those that have a corporate
level logistics function with mostly staff responsibility
versus those with staff and line responsibility. The former
anticipated higher future use of outside service vendors.
Firms that consolidate mostly staff activities across business
units use outside service vendors to achieve economies of
scale that would be otherwise impossible to obtain. Given
that use of outside vendors is implicitly related to the
structural "picture" of the firm, this research examined
interrelationships among pictorial configuration variables.

Comparing the same two types of structures, Germain and
Daugherty (1989) found that divisionalized firms with a line
and staff cross-divisional function were more formalized with
respect to a formal logistics mission statement and a formal
logistics strategic plan than firms with.a mostly staff cross-
divisional logistics function.

In summary, studies have examined the pictorial config-

55
uration of the logistics function, both as an independent
variable and as a dependent variable. However, no studies
focusing on logistics were identified examining relationships

between pictorial configuration and performance measurement.

HYPOTHESES

An question that can be asked is: What are the
implications of the logistics function having formal line
authority over customer service? One answer to this question
is that the logistics function may be more committed to
customer service. 'The logistics function that controls
customer service may be more committed to measuring customer
service performance, both internally and through benchmarking.
These firms may be more committed to customer service and the
quality thereof, and. may' be attempting to differentiate
themselves from competitors along various customer service
dimensions. ILE so, then these firms may measure a larger
number of customer service and quality related performance
items. .However, an emphasis on differentiation.has few if any
implications for cost performance measurement. Hypothesis six
formally expresses these expectations.

H6: Logistics' formal line authority over customer

service is: (H6a): positively related to the use of

differentiation.performance measures; and (H6b) not
related to the use of cost performance measures.

A related question is: What is the effect of whether or

not the logistics function has line control over logistics

56

systems planning? Much like the answer to the previous
question, the answer can be formulated by considering revealed
versus actual strategy. The assignment of activities to
various functions could be an indicator of "revealed"
strategy, or what strategy turned out to be. The logistics
function that controls logistics systems planning may be
revealing a commitment to a low cost strategy. This may be
reflected in an increased level of cost-based performance
measurement. However, as mentioned by Miller (1988), the low
cost and differentiation strategies need not be mutually
exclusive. Thus, a potential focus on costs, as indicated by
the logistics function having line responsibility for
logistics systems planning, is not hypothesized to have
implications for the level of differentiation-based
performance measurement.

Hypothesis seven formally expresses these expectations.

H7: Logistics' formal line authority over logistics

systems planning is: (H7a): not related to the use

of differentiation performance measures; and (H7b)

positively related with the use of cost performance

measures.

The next pictorial configuration variable considered is
the total number of line activities that the logistics
function controls, or the span of logistics control. It is
expected that the larger the number of activities that the
logistics function has formal authority over, the greater the
reliance on internal and benchmarking performance measurement.

This is expected since the aggregation of activities in a

57
single function may be an indicator of: (1) increased
integration of all logistics related activities; and (2)
increased commitment to the logistics total cost concept which
includes both a customer service strategic focus and cost
strategic focus. This is expressed hypothesis eight.

H8: The number of activities that the logistics

function has line control over is positively related

to the use of performance measures.

The last variable examined is the title level of the
senior logistics executive which represents the vertical
visibility of the executive. Potentially, a higher title
level implies greater power for logistics relative to other
functions and an increased likelihood that the senior
executive participates in overall business unit strategic
planning. For these reasons, the higher the title level of
the senior logistics executive, the greater the use of
logistics performance measures.

H9: An increasing title level of the senior

logistics executive is positively related to the
use of performance measures.

CHANNEL POSITION

Channel position has been used to denote where in the
channel of distribution a firm operates: that is,
:manufacturer, wholesaler or retailer; .According to Bowersox,

Closs and IHelferich. (1986, p. 497-506), there are four

 

”I
de:

6313'
Th
ch
a;
pa

ct

58

approaches to understanding channels of distribution. The
descriptive institutional approach. seeks 1x: classify
"middlemen" institutions into "types" based on ownership of
goods and on the range of services offered. The graphic
approach illustrates channel flows of goods and information.
The commodity grouping approach identifies distribution
channels used for specific commodities. 'The functional
approach seeks to identify generic functions which are
performed in the majority of channels and to identify which
channel members are performing which functions.

The different descriptions of and approaches to
understanding channel members suggests that firms occupying
different channel positions are "totally different." In one
sense this is correct: "The appropriate activities to be
integrated through the logistical value-added process differ
depending on whether an organization is a manufacturer,
retailer or wholesaler" (Bowersox gt gt. 1989, p. 10).

However, from.a:management perspective, channel position
to some extent may not be that important. A major empirical
study which recently examined logistical practices for
different channel members concluded: "...the best practice as
typified by leading edge firms is remarkably similar
regardless of a firm's channel position" (Bowersox gt a_1_.
1989, p. 31). These best practices include the existence of
a formal logistics mission statement, an increased reliance
on information technology including internal computerization,

the use of electronic data interchange linkages, heightened

59
levels of operational flexibility, and a higher title level
of the senior logistics executive.

While the logistical activities managed by manufacturers,
wholesalers and retailers may be different, Bowersox gt gt.
(1989) concluded that the methods or processes by which top
performing firms manage logistical activities are similar,
regardless of channel position. This suggests that logistics
management concepts that have traditionally been developed in
manufacturing contexts may also be applicable to wholesaling
and retailing contexts.

Consider the case of inventory management. Many
retailing texts cover this topic and/or economic order
quantity (Markin 1971, p. 388-401; Berman and Evans 1979, p.
358-360; Larson, Weigand and Wright 1982, p. 251-253).
Inventory policy is frequently'discussed under "overall retail
marketing strategy" (Bates 1979, p. 59; Gattorna 1985, p. 148-
163; Knee and Walters 1985, p. 138) because of its
relationship to how consumers react to stockouts (Schary and
Christopher 1979) and to associated out of stock.costs (Walter
and.Grabner 1975). Inventory control has also been identified
as a primary operational concern in wholesaling management
(Revzan 1961; Lopata 1969; Stern and El-Ansary 1987).
Inventory is thus a critical activity to be managed regardless
of whether the firm is a manufacturer, wholesaler or retailer.

A second concept that demonstrates similarities across
channel positions is related to activity allocation to various

functions. Manufacturing firms frequently utilize three major

60
line functions: manufacturing, marketing and logistics
(Bowersox: t gt. 1989). Retailing organizational charts often
depict a merchandising/buying function and an operations func-
tion (Gattorna 1985). This is similar to typical wholesaling
organizational charts depicting a sales and an operations
function (Heskett, Ivie and Glaskowsky 1964, p. 496).

These two functions in retailing and wholesaling firms
have respective counterparts in manufacturing firms: marketing
and logistics. They are typically responsible for stimulating
demand and servicing demand, respectively. The
interdependence of these two marketing subsystems may
increasingly become important as firms seek strategic
alliances with suppliers. This applies to both retailers
(Droge, Germain and Stock 1989) and manufacturers pursuing
just-in-time strategies (Frazier, Spekman and O'Neil 1988).

The purpose of the previous two examples is to
demonstrate that while logistics has often been applied to
manufacturers, many of the concepts have parallel applications
for other members in the distribution channel. The study by
Bowersox gt a_1_. (1989) provides an excellent example. In
spite of these similarities, a recent review of retailing
research failed to identify retailing operations as either an
area needy of further research, let alone as an area of
inquiry in and of itself (Lindquist and Samli 1985).

In fact, contingency theory applies to all organizations,
not just manufacturers. Formalization, centralization, role

specialization, and tall versus flat hierarchies apply to all

61

organizations. Some of the empirical studies reviewed
contained different channel members in the same sample. For
example, Kyj's (1986) sample contained 'manufacturers and
wholesalers and Miller's (1988) sample contained manufacturers
and retailers. In neither case was channel position modeled
as an independent variable, suggesting that channel position
is not an independent variable worthy of study; However, this
may be explained in part by insufficient sample sizes.

The research hypotheses of the dissertation are not
concerned with whether manufacturers, wholesalers and
retailers use different performance measures. Rather, the
research. hypotheses. are concerned. with. whether logistics
structure and strategy are related to performance measurement.
A critical point is whether the research hypotheses one
through nine hold across channel positions. No difference in
these underlying relationships are expected to be found. The
research hypotheses explicated thus far are expected to hold
for all three types of organizations.

Hypothesis ten formally expresses this expectation.

H10: Hypotheses one through nine hold equally for
manufacturers, wholesalers and retailers.

SUMMARY OF HYPOTHESES

This last section of Chapter II summarizes the research

hypotheses that are tested in the dissertation and recasts

them in the framework presented in Exhibit 4. Exhibit 10

62
depicts the variables that are included in the dissertation.
There are a total of eleven independent variables and six
dependent variables.
Hypothesis one is concerned with relationships between
internal performance measurement and benchmarking. This

hypothesis examines interrelationships among the dependent

variables.

H1: The use of internal performance measures is
positively related with the use of benchmarking;
(Hla) cost benchmarking is related to the use of
internal cost performance measures; and (Hlb)
differentiation benchmarking is related to the use
of internal differentiation performance measures.

Hypothesis two deals with the relationship between
logistics strategy and performance measurement.

H2: The frequency withwwhich the logistics strategic

plan is updated is inversely related to the use of

performance measures.
Hypotheses three, four and five deal with how the structuring
of logistics is related to performance measurement.

H3: Formalization is positively related to the use
of performance measures.

H4: The frequency with which the logistics function
is reorganized is positively related to the use of
performance measures.

H5: Participation by the senior logistics executive
in business unit strategic planning is positively
related to the use of performance measures.

63

Exhibit 10
THEORETICAL FRAMEWORK OF THE DISSERTATION

 

 

Independent Variables
ENVIRONMENT
Channel position
1. manufacturer versus wholesaler versus retailer
STRATEGY
Frequency of strategic plan update
2. How often logistics strategic plan updated
STRUCTURE

Structuring Variables Pictorial Configuration

Formalization Span of control
3. logistics mission 8. Logistics controls
statement customer service
4. logistics strategic 9. Logistics controls
plan logistics systems
5. maturity of the logistics planning
function 10. Span of logistics

Frequency of reorganization
6. no. of times logistics
has been reorganized
during the past five
years.
Participation in planning
7. senior logistics executive
participates in strategic
planning

Dependent Variables

Internal cost measurement

Internal differentiation
measurement

Total internal measurement

11.

control

Title level

Title level of senior
logistics executive

Cost benchmarking

Differentiation
benchmarking

Total Benchmarking

 

64
Hypotheses six, seven eight and nine deal with how the

pictorial configuration of logistics is related to performance

measurement.

H6: Logistics' formal line authority over customer
service is: (H6a): positively related to the use of
differentiation performance measures; and (H6b) not
related to the use of cost performance measures.

H7: Logistics' formal line authority over logistics
systems planning is: (H7a): not related to the use
of differentiation performance measures; and (H7b)

positively related with the use of cost performance
measures.

H8: The number of activities that the logistics
function has line control over is positively related
to the use of performance measures.

H9: An increasing title level of the senior

logistics executive is positively related to the

use of performance measures.

The last research.hypothesis examines the role.of channel
position. It is expected that the relationships set forth in
hypotheses one through nine are invariant across channel
position. For example, the relationship between formalization
and performance measurement should hold for manufacturers,
wholesalers and retailers. This implies that no significant
interaction should be found between the channel position
variable and each of the independent variables. Hypothesis
ten formally expresses this expectation.

H10: Hypotheses one through nine hold equally for
manufacturers, wholesalers and retailers.

65

It should be noted that the hypotheses expressed above
are research hypotheses and not statistical hypotheses. For
example, H10 expresses the expectations for manufacturers
versus wholesalers versus retailers in one short sentence.
However, this hypothesis actually summarizes 3x9=27
statistical hypotheses: one for each of H1 through H9 for
manufacturers, wholesalers and retailers separately. The
complete test of H10 requires an examination of 6x9=54 two

factor GLM models.

CHAPTER III: METHODOLOGY

Chapter III presents the method used in the dissertation.
The sampling design is described first. This is followed by
a section covering the design of the questionnaires. Lastly,

the statistical methodology is described.

SAMPLING DESIGN

The data used in the dissertation was collected in three
separate surveys“ The sampling frame for the first survey was
provided by the Council of Logistics Management and A.T.
Kearney Management Consultants. A total of 3,002 letters were
mailed to the senior logistics executive of U.S. manufacturer
firms requesting participation in the study. Six hundred and
sixty-eight executives indicated a willingness to participate.
Questionnaires were then mailed, and a total of 380 usable
questionnaires were returned. This survey is referred to as
the "manufacturer" survey.

An "update" survey was mailed to the 380 manufacturers
that participated in the first study. Of these, 187 usable
questionnaires were returned. Since the second survey
contains the performance measurement scales, 187 manufacturers

are examined in the dissertation.

66

67

A third survey, the "wholesaler/retailer" survey, was
mailed to a sample of wholesalers and retailers. A sampling
frame was purchased from Dunn and Bradstreet consisting of
American and Canadian wholesalers and retailers with sales in
excess of $50 million. A total of 6678 questionnaires were
mailed to the Chief Executive Officer in each organization,
and 90 retailers and 148 wholesalers returned usable
questionnaires.

Seventy-seven vertically integrated firms responded to
the wholesaler/retailer survey. These firms are not included
in the analysis. The wholesalers and retailers included in
the analyses can be called "pure" in terms of the channel
position occupied. The questionnaires were designed by a
panel of academicians and executives and pretested prior to

the final mailing.

QUESTIONNAIRE DESIGN

For the variables relevant to the dissertation, identical
scaling methods were used in all questionnaires. The manu—
facturer questionnaire appears in Appendix A, the manufacturer
update questionnaire appears in Appendix B, and the
wholesaler/retailer questionnaire appears in Appendix C.

Exhibit 11 presents the variables examined in the
dissertation and for each variable lists the questionnaire
from which the variable is taken and its respective question

number. The measurement of the independent and dependent

68

Exhibit 11
VARIABLES FROM THE QUESTIONNAIRES

 

 

 

 

 

 

Questionnaire
Update
Manu Manu- Wholesaler

Variable facturer facturer - Retailer
Independent Variables
Frequency of strategic
plan update..... ........... Q8c Q5b
Mission statement .......... Q7a Q4
Strategic plan ............. Q8a Q4
Length of time logistics
a formal function.... ...... Q11 Q16
Number of reorganizations
during past five years..... Q10a Q17
Participation in
strategic planning.. ....... Q5 Q18
Logistics controls
customer service.. ......... Q13 Q20
Logistics controls
logistics systems planning. Q13 Q20
Number of activities
controlled by logistics.... Q13 Q20
Title level of the senior
logistics executive........ Q5a1 Q8
Dgpendent Variableg
Internal performance
measurement........... ..... Q7 Q22

Benchmarking performance
measurement................ Q6 Q23

 

69
variables follows.

The first independent variable examined is channel
position. In the wholesaler/retailer questionnaire,
respondents were asked whether the organization they
represented was a wholesaler, retailer or manufacturer and
they were allowed to select more than one category.

The strategy variable examined is the frequency with

which the logistics strategic plan is updated. (ha a free
response scale, respondents were asked: "How often is your
plan reviewed or updated?" The values on this scale were

reversed since a longer time between plan updates is syno-
nymous with decreasing frequency of strategic plan updating.

A total of nine variables describing the structure of the
logistics function are examined: five structuring variables
and four pictorial configuration variables.

Three measures of formalization were collected: whether
a formal logistics mission statement exists, whether a formal
logistics strategic plan exists, and the maturity of or the
length of time that logistics has been a formal function.

Participation in business unit strategic planning by the
senior logistics executive was measured by asking respondents:
"Does your senior logistics executive formally participate in
overall strategic planning for your business unit?" Response
categories of "yes" "no, but provides input through other
executives" and "no, provides no input" were used” These were
coded 2, l and 0. Increasing values on this scale indicate

increasing participation in business unit strategic planning.

70

The number of times that the logistics function has been
reorganized over the past five years was collected on a free
response scale in response to the question: "Within the last
five years, how many times have all or parts of logistics been
reorganized?"

Four pictorial configuration variables are examined.
Respondents were.asked whether the logistics function.has line
responsibility for’ a number of activities. Exhibit 12
presents the thirteen activities which are common to both the
manufacturer and wholesaler/retailer questionnaires. If the
logistics function had formal line authority over an activity,
respondents were instructed to "tick" the appropriate box.
The first two pictorial configuration variables are whether
the logistics function controls customer service and whether
the logistics function controls logistics systems planning.
The sum out of thirteen is assumed to represent the span of
logistics control or the number of line activities that the
logistics functions controls.

The last pictorial configuration variable examined which

is also the last independent variable is the title level of

the senior logistics executive. The following response
categories were provided: "president," "executive vice
president," "senior vice president," "vice president,"
"director," "manager," "supervisor" and "other." The

president response was coded as a seven, the executive vice
president response was coded as a six, and so on. IRespondents

selecting the "other" response category were treated as

71

Exhibit 12
ACTIVITIES USED TO MEASURES SPAN OF LOGISTICS CONTROL

 

 

Sales forecasting Purchasing
Inbound transportation Inventory management
Intra-company transportation Warehousing
Order processing Customer service
Outbound transportation Logistics system planning
Logistics administration International
Facilities design/logistical
engineering

 

missing since the actual title level is not known.

The dependent performance measurement variables were all
measured by the sum of a series of yes/no items. A total of
thirty-eight items were used to indicate usage of internal
performance measures (Appendix B, question 7 and Exhibit 13).
As shown in Exhibit 13, twenty-four of the items are cost
related. The sum out of twenty-four represents the extent to
which internal cost performance is measured.

Also shown in Exhibit 13 is that fourteen of the thirty-
eight items are classified as being differentiation related.
The sum of these out of fourteen represents the extent to
which internal differentiation performance is measured.
Lastly, the sum of all thirty-eight items is used to indicate
the extent tO‘WhiCh a firm uses internal performance measures.

Ten benchmarking items were examined using yes/no scales
(Appendix B, question 6 and Exhibit 14). As shown in Exhibit
14, the indicator of cost benchmarking performance measurement

is the sum of three items. The indicator of differentiation

72

Exhibit 13
ITEMS USED TO INDICATE INTERNAL PERFORMANCE MEASUREMENT

 

 

Cost Measures

Inventory turns

Inventory levels (no. of days)
Return on assets

Inventory carrying costs
Obsolete inventory

Return on investment

Total cost analysis

Cost as a percentage of sales
Outbound freight costs
Logistics administrative costs
Comparison of actual to budget
Cost per unit

Direct product profitability

Diffgrentiation Measures

Fill rate
Shipping errors
Backorders
Customer feedback
Customer surveys
Stockouts

On time delivery

Inbound freight costs
Warehouse costs
Order processing costs
Cost trend analysis
Direct labor costs
Units shipped per employee
Orders per sales rep.
Units per labor dollar
Goal programs
Productivity index
Comparison to historical
standards

Cycle time
Salesforce feedback
Frequency of damage
Number of credit claims
Cost of returned goods
Dollar amount of damage
Number of customer
returns

 

73

Exhibit 14
ITEMS USED TO INDICATE BENCHMARKING
PERFORMANCE MEASUREMENT

 

 

Cost Measures

Cost Asset management
Productivity

Differentiation Measures

Customer service Quality

Additional Measures

Logistics strategy Warehouse operations
Technology deployment Order processing operations
Transportation operations

 

benchmarking performance measurement is the sum of two items.

The five remaining "additional" items on which
benchmarking data was collected cannot be classified easily
as cost or differentiation related. The benchmarking of
"logistics strategy," "warehousing operations" and "technology
deployment" could. well be conducted for either cost of
differentiation purposes. These items are therefore not
included in the measures of cost or differentiation
performance measurement. However, all ten items are used to
indicate the total level of benchmarking.

The 187 manufacturers, 147 wholesalers and 90 retailers
form the respondent base. Missing values are present for a

number of variables. Exhibit 15 presents the sample sizes

74

Exhibit 15
SAMPLE SIZES

 

 

 

All Manu- Whole-
Variable Firms facturer sale Retail
Independent Variables
Frequency of strategic
plan update.......... ...... 185 93 45 47
Mission statement .......... 291/126* 119/66* 119/26* 53/34*
Strategic p1an........ ..... 230/189* 92/93* 101/46* 37/50*
Length of time logistics
a formal function.......... 382 187 116 79
Number of reorganizations
during past five years..... 319 172 128 79
Participation in
strategic planning... ...... 392 185 145 88
Logistics controls
customer service.. ......... 258/167* 85/102* 105/43* 68/22*
Logistics controls log. .
systems planning.... ....... 244/181* 68/119* 109/39* 67/23*
Number of activities
controlled by logistics.... 425 187 148 90
Title level of the senior
logistics executive........ 407 184 137 86
Dependent Variables
All elements of internal
and benchmarking
performance measurement.... 425 187 148 90

* Number in the sample who replied no/xes.

75
available for each of the variables overall, and within each

subgroup.

OVERVIEW OF METHODOLOGY

Within the context of General Linear Models (GLM), the
statistical test employed is dependent on the level of the
scales used as dependent and independent variables. All six
dependent variables examined in the dissertation are assumed
to be continuous. As already mentioned, they are the number
of: (1) internal cost measures used; (2) internal differen-
tiation measures used; (3) total internal measures used; (4)
benchmarking cost measures used; (5) benchmarking differen-
tiation measures used; and (6) total benchmarking measures
used.

As shown in Exhibit 16, the independent variables
examined are either binary or continuous. The binary
variables are: (1) the existence of logistics mission
statement; (2) the existence of a logistics strategic plan;
(3) whether the logistics function has line authority over
customer service; and (4) whether the logistics function has
line authority over logistics systems planning.

The continuous independent variables are: (1) the
frequency of the strategic logistics plan update; (2) the
length of time that the logistics function has formally
existed; (3) the number of times that the logistics function

has been reorganized over the past years; (4) the number of

76

Exhibit 16
STATISTICAL METHODOLOGY

 

 

 

 

 

 

Analysis

Overall Subgroup
Independent Continuous Type of Two- Univariate
Variable vs. Binary Factor Model Test
Frequency of strategic
plan update .............. C GLM corr.
Mission statement........ B ANOVA t-test
Strategic plan ........... C ANOVA t-test
Length of time logistics
a formal function ........ C GLM corr.
Number of reorganizations
during past five years... C GLM corr.
Participation in
strategic planning ....... C GLM corr.
Logistics controls
customer service... ...... B ANOVA t-test
Logistics controls log.
systems planning... ...... B ANOVA t-test
Number of activities
controlled by logistics.. C GLM corr.
Title level of the senior
logistics executive...... C GLM corr.

C=continuous independent variable
B=binary independent variable

 

77
line activities that the logistics function has formal line
authority over; and (5) the title level of the senior
logistics executive.

Exhibit 16 also summarizes the statistical methods that
are employed. The analysis is performed in two stages. In
the first stage, the overall analysis, two-factor models are
examined with the following independent variables: (1) the
channel position variable; (2) one of the independent
variables listed in Exhibit 16; and (3) an interaction term
between channel position and the respective independent
variable. Six of these models are examined for each
independent variable since there are six indicators of
performance measurement. Thus a total of 10x6=60 two-factor
models are examined. The two-factor plus interaction term
models employed are either GLM or ANOVA depending on whether
the independent variable is continuous or binary. Exhibit 16
presents the type of model required for each independent
variable. The significance of interaction terms in these
models are used to support or refute hypothesis ten. But
these models do not indicate the directionality of a
relationship. The directionality of a particular relationship
is examined using a correlation or a t-test, whichever is
applicable.

The second stage of the analysis is the subgroup
analysis. Exhibit 16 presents the type of statistical method
employed for the manufacturing, wholesaling and retailing

subgroups. If the independent variable listed in Exhibit 16

78

is continuous, then a correlation is used. If the independent
variable is binary, then a t-test is used. The statistical
tests for examining the directionality during the overall
analysis is the same as those employed during the subgroup
analysis.

Appendix D presents the exact statistical hypotheses that
are tested. The null hypotheses are presented in the same
order as tested in the results chapter. Appendix D lists the

corresponding Table in which the null hypothesis is tested.

CHAPTER IV: RESULTS

This chapter is divided into five sections. First, means
and initial ANOVA.models are presented" ‘Then, hypothesis one,
which examines interrelationships among the types of
performance measurement, is discussed. This is followed by
the overall analysis. In this section, hypotheses two through
ten are examined for all firms. This is followed by subgroup
analysis examining hypotheses two through nine for each of
manufacturers, wholesalers and retailers separately. Lastly,
the results are summarized. Implications appear in the

following chapter.

INITIAL RESULTS

Table 1 presents the means and standard deviations for
each of the performance measurement variables for each type
of firm and for all firms together. Table 2 presents the
results of a series of one-way ANOVA models. In each model,
the independent variable is the channel position variable.
The dependent variable in each.model is a different indicator
of performance measurement. As can be seen in Table 2, all
six models are significant at the .05 level. The results of

Tukey tests are also shown in Table 2 where means underlined

79

80

Table 1

MEANS AND STANDARD DEVIATIONS OF PERFORMANCE MEASURES

USED BY CHANNEL MEMBERS

 

 

 

 

Type of Manu- Whole- Mean
Performance facturer saler Retailer of all
Measure Mean Mean Mean Firms
Internal
Cost 16.48 14.89 15.50 15.72
(5.45) (5.78) (4.38) (5.40)
Differentiation 9.86 8.07 7.88 8.82
(3.89) (3.84) (3.88) (3.97)
Total 26.34 22.96 23.38 24.54
(8.57) (8.65) (7.53) (8.52)
Benchmarking
Cost 1.66 1.19 1.09 1.37
(1.05) (1.11) (1.09) (1.11)
Differentiation 1.61 0.91 0.67 1.16
(0.68) (0.90) (0.83) (0.89)
Total 5.77 3.91 3.58 4.66
(2.86) (3.16) (3.16) (3.18)

* Standard deviations in parentheses.

81

Table 2
ONE-WAY ANOVA MODELS OF PERFORMANCE MEASURES USED AS A
FUNCTION OF CHANNEL POSITION AND TUKEY TEST RESULTS

 

 

 

 

 

 

 

 

 

 

 

 

 

Type of

Performance Overall Model

Measure F * R-Square Tukey Test Results

Internal

Cost 3.72 0.017 W R M **
(.0250) ***

Differentiation 12.23 0.055 R W M
(.0001)

Total 7.81 0.036 W R M
(.0005)

Benchmarking

Cost 11.71 0.053 R W M
(.0001)

Differentiation 54.72 0.206 R W M
(.0001)

Total 22.85 0.098 R W M
(.0001)

* d.f.=2 and 422 for model and

** R=retailers; W=wholesalers;
underlined by the same line
different.

*** p-valugg in parentheses.

error, respectively.
M=manufacturers; means
are not significantly

82
by the same line are not significantly different. For all six
indicators of performance measurement, the mean for
manufacturers is the greatest. For both total internal
performance measurement and total benchmarking performance
measurement, the Tukey test reveals that the means of
manufacturing firms are significantly greater than the means
of wholesalers and of retailers, but no significant difference
exists between the means of the latter two types of firms.
At a minimum, all subsequent ANOVA and GLM models examining
channel position and other independent variables should

contain a significant channel position main effect.

HYPOTHESIS ONE

Hypothesis one related ‘various types of jperformance
measurement to one another. Table 3 presents the correlation
matrix of the performance measurement indicators, while Tables
4, 5 and 6 present the correlation matrices for the manufact-
uring, wholesaling and retailing subgroups, respectively.

As can be seen from Table 3, when all firms are
considered, the performance measurement intercorrelations are
significant at the .01 level. Several of the correlations
should be significant since they represent a type of item to
total correlation. An example is the relationship between
internal cost and total internal performance measurement.

Total internal and total benchmarking performance

83

 

 

 

Table 3
CORRELATION MATRIX OF PERFORMANCE MEASURES USED
(ALL FIRMS)
Variable (1) (2) (3) (4) (5)
(1) Internal cost -
(2) Internal diff 0.646 -
(.0001) *
(3) Total internal 0.935 0.875 -
(.0001) (.0001)
(4) Cost 0.419 0.284 0.398 -
benchmarking (.0001) (.0001) (.0001)
(5) Diff 0.262 0.357 0.332 0.506 -
benchmarking (.0001) (.0001) (.0001) (.0001)
(6) Total 0.420 0.421 0.463 0.798 0.757
benchmarking (.0001) (.0001) (.0001) (.0001) (.0001)

* p-values in parentheggs.

 

 

 

Table 4
CORRELATION MATRIX OF PERFORMANCE MEASURES USED
(MANUFACTURERS)
Variable (l) (2) (3) (4) (5)
(1) Internal cost -
(2) Internal diff 0.672 -
(.0001) *
(3) Total internal 0.942 0.882 -
(.0001) (.0001)
(4) Cost 0.393 0.290 0.382 -
benchmarking (.0001) (.0001) (.0001)
(5) Diff 0.318 0.340 0.357 0.427 -
benchmarking (.0074) (.0001) (.0001) (.0001)
(6) Total 0.445 0.417 0.472 0.768 0.684
benchmarking (.0001) (.0001) (.0001) (.0001) (.0001)

* p-values in parentheses.

 

84

 

 

 

Table 5
CORRELATION MATRIX OF PERFORMANCE MEASURES USED
(WHOLESALERS)
Variable (1) (2) (3) (41, (5t,
(1) Internal cost -
(2) Internal diff 0.603 -
(.0001) *
(3) Total internal 0.935 0.846 -
(.0001) (.0001)
(4) Cost 0.470 0.207 0.406 —
benchmarking (.0001) (.0116) (.0001)
(5) Diff 0.219 0.314 0.286 0.480 -
benchmarking (.0074) (.0001) (.0004) (.0001)
(6) Total 0.419 0.398 0.456 0.768 0.804
benchmarking (.0001) (.0001) (.0001) (.0001) (.0001)

* p-values in parentheses.

 

 

 

Table 6
CORRELATION MATRIX OF PERFORMANCE MEASURES
(RETAILERS)
Variable (l) 12) (3) (4) (5)
(1) Internal cost -
(2) Internal diff 0.659 -
(.0001) *
(3) Total internal 0.922 0.899 -
(.0001) (.0001)
(4) Cost 0.309 0.213 0.289 -
benchmarking (.0030) (.0440) (.0056)
(5) Diff 0.126 0.175 0.163 0.503 -
benchmarking (.2365) (.0999) (.1240) (.0001)
(6) Total 0.305 0.270 0.317 0.847 0.700
benchmarking (.0035) (.0100) (.0023) (.0001) (.0001)

* p-valueg in parentheses.

85
measurement are highly correlated (r=.463; p<.01), supporting
H1. Internal cost and cost benchmarking are correlated
(r=.419; p<.01), and internal differentiation and
differentiation benchmarking are correlated (r=.357; p<.01),
supporting Hla and Hlb, respectively.

However, an interesting pattern emerges from a closer
examination of these correlations. Not including the item to
total correlations, the largest correlations for all firms is
that existing between internal cost and differentiation
(r=.646) and between cost and differentiation benchmarking
(r=.506). The next strongest correlation is that existing
between total internal and total benchmarking performance
measurement (r=.463). Next are the correlations between
internal cost and cost benchmarking (r=.419) and between
internal differentiation and differentiation. benchmarking
(r=.357). The weakest correlations are those across both
focus and strategic thrust: r=.262 between internal cost and
differentiation benchmarking and r=.284 between internal
differentiation and cost benchmarking. Thus cost-based
measurements (Hla) and.differentiation-based measurements are
more strongly correlated (Hlb) than the correlations between
cost and differentiation-based measurements across strategic
thrust, but are weaker than the correlations between cost and
differentiation-based measurements within a particular
strategic thrust.

This pattern repeats itself across Tables 4, 5 and 6.

86

For manufacturers, the weakest correlations are those between
internal cost and differentiation benchmarking (r=.318) and
between internal differentiation and cost benchmarking
(r=.290). The correlations between internal and benchmarking
cost (r=.393) and between internal and benchmarking
differentiation (r=.340) are the next greatest. The largest
correlations ‘within ‘the ‘manufacturing subgroup are those
between the two internal performance measurement (r=.672) , the
two benchmarking performance measurement (r=.427) , and the two
total performance measurement variables (r=.472).

This pattern more or less holds for wholesalers and for
retailers. The only exception is that for retailers, the
correlation between internal and benchmarking differentiation
(r=.175) is less than all remaining correlations.

In summary, H1, Hla and Hlb are all fully supported, but
must be qualified by fact that an ordering of sorts exists
among the relatedness of performance measurement variables.
Furthermore, H1, Hla and Hlb also hold for the individual

subgroups.

OVERALL ANALYSIS

Tables 7 through 16 each contain the results of the six
two-factor GLM models. The models in each table examine the
main effect of a particular independent variable, the channel

position variable, and an interaction term between channel

87
position and the respective independent variable. The
significance of the channel position variable is estimated
after the main effect of interest, and the significance of the
interaction term is estimated after the main effect of
interest and the channel position main effect.

The directionality of the relationships are presented in
Tables 17 and 18. The upper half of Table 17 presents
correlations between each of the six continuous independent
variables and the three indicators of internal performance
measurement. The lower half of Table 17 presents the
correlations between the six continuous independent variables
and the three indicators of benchmarking measurement.

Table 18 presents t-tests of difference between means of
the four independent binary variables for all firms. These
tests are either one-tailed or two-tailed tests, depending on

the specific wording of the respective hypothesis.

STRATEGY AND PERFORMANCE MEASUREMENT

The results of the two-factor GLM models examining the
effect of frequency of the logistics strategic plan update,
channel position and an interaction term on the six indicators
of performance measurement are presented in Table 7. As with
all models examined in this subsection, the channel position
main effect is not of primary interest. As seen in Table 7,
the only significant "update" main effect is in the

differentiation benchmarking model. None of the interaction

88

Table 7

GLM MODELS OF PERFORMANCE MEASURES USED AS A FUNCTION OF

CHANNEL POSITION (CP)

FREQUENCY OF STRATEGIC PLAN UPDATE (PU) AND

 

 

 

 

Dependent Overall * R- Partial F-values
Variable F-Value Square PU CP PU*CP
Internal
Cost 1.57 0.042 0.01 2.00 1.91
(.1722) ** (.9421) (.1377) (.1517)
Differentiation 3.09 0.079 0.01 6.98 0.74
(.0106) (.9279) (.0012) (.4799)
Total 2.38 0.062 0.01 4.43 1.50
(.0407) (.9303) (.0132) (.2252)
Benchmarking
Cost 3.22 0.083 0.39 6.70 1.15
(.0083) (.5309) (.0016) (.3177)
Differentiation 10.96 0.234 7.31 22.65 1.10
(.0001) (.0075) (.0001) (.3339)
Total 4.29 0.107 0.82 8.90 1.42
(.0010) (.3768) (.0002) (.2453)

* d.f.=5 and 179 for model

** p-values in pargntheseg.

and error, respectively.

89
terms are significant.

The only significant correlation between the frequency
of updating the logistics strategic plan and the six indica-
tors of performance measurement is the one for differentiation
benchmarking (r=.177; p<.05), and the direction is opposite
from ‘that hypothesized (Table 17). The remaining five
indicators of performance measurement are unrelated to the
frequency of the logistics strategic plan update.

The evidence does not support H2. The only significant
correlation is positive and hence contradicts expectations.
The remaining five correlations are nonsignificant, weak, and
none are in the hypothesized direction. In general, the
frequency of strategic plan updating is unrelated to
performance measurement. This applies to internal and
benchmarking and to cost and differentiation based performance

measurement.

STRUCTURING VARIABLES AND PERFORMANCE MEASUREMENT

The first structuring variables examined are the
indicators of formalization of the logistics function: a
logistics mission statement, a logistics strategic plan and
the length of time that logistics has been a formal function.

Table 8 presents the results of the two-factor ANOVA
models examining the effects of a logistics mission statement,
channel position and an interaction.term.on‘the six indicators

of performance measurement. As expected, the presence of a

90

Table 8

ANOVA MODELS OF PERFORMANCE MEASURES USED AS A FUNCTION OF
LOGISTICS MISSION STATEMENT (MS) AND CHANNEL POSITION (CP)

 

 

 

 

 

Dependent Overall * R— Partial F-values
Variable F-Value Square MS CP MS*CP
Internal
Cost 6.91 0.078 26.66 1.86 2.10
(.0001) ** (.0001) (.1573) (.1244)
Differentiation 11.07 0.119 34.40 7.98 0.68
(.0001) (.0001) (.0001) (.5052)
Total 10.13 0.110 36.63 5.34 1.66
(.0001) (.0001) (.0051) (.1911)
Bgnchmarkinq
Cost 7.44 0.083 13.88 9.67 2.00
(.0001) (.0002) (.0001) (.1369)
Differentiation 24.35 0.229 13.55 52.28 1.81
(.0001) (.0003) (.0001) (.1642)
Total 15.52 0.159 30.85 20.76 2.63
(.0001) (.0001) (.0001) (.0736)

* d.f.=5 and 411 for model

** p-valueg in parentheses.

and error, respectively.

91
logistics mission is significantly related to internal cost,
internal differentiation, total internal, cost benchmarking,
differentiation benchmarking and total benchmarking
performance measurement. The interaction term is not
significant in any of the models.

The one-tailed t-tests of difference in means between
firmS‘with versus without a logistiCS‘mission statement (Table
18) demonstrate that the presence of a logistics mission
statement is associated with increasing usage of performance
measures. Hypothesis three, formalization and performance
measurement are positively related, is thus supported, at
least when a logistics mission statement is used to indicate
formalization of the logistics function. The nonsignificant
interaction ‘term in the models (Table 8) supports H10:
manufacturers, wholesalers and retailers do not differ with
respect to the effect of a logistics mission statement on
performance measurement.

The results of the two—factor ANOVA models examining the
effects of a logistics strategic plan, channel position and
a strategic plan -- channel position interaction term are
presented in Table 9. The results are similar to those found
with respect to the effect of a logistics mission statement.
The strategic plan main effect is significant in all six ANOVA
models, none of the interaction terms are significant, and
directionality of the significant one-tailed t-tests (Table

18) support H3 and H10. Formalization of the logistics

92

Table 9
ANOVA MODELS OF PERFORMANCE MEASURES USED AS A FUNCTION OF

LOGISTICS STRATEGIC PLAN (SP) AND CHANNEL POSITION (CP)

 

 

 

 

Dependent Overall * R- Partial F-values
Variable F-Value Square SP CP SP*CP
Internal
Cost 7.47 0.083 23.39 1.96 0.51
(.0001) ** (.0001) (.1417) (.6023)
Differentiation 13.00 0.136 40.73 11.01 1.12
(.0001) (.0001) (.0001) (.3267)
Total 11.49 0.122 44.04 5.88 0.83
(.0001) (.0001) (.0030) (.4362)
Benchmarking
Cost 7.91 0.087 16.44 10.79 0.71
(.0001) (.0001) (.0001) (.4943)
Differentiation 23.34 0.220 10.31 52.52 0.68
(.0001) (.0014) (.0001) (.5073)
Total 14.09 0.146 26.00 21.58 0.66
(.0001) (.0001) (.0001) (.5195)

* d.f.=5 and 413 for model
** p:values in parenthese .

 

and error, respectively.

93
function is positively related to performance measurement and
this holds for manufacturers, wholesalers and retailers, at
least when a logistics strategic plan is used to indicate
formalization.

Table 10 presents the results of the GLthodels examining
the effect of the third and last formalization variable
explored, the length of time that logistics has been a formal
function. The "length of time" main effect is significant in
all six models. The correlations between the length of time
that logistics has been a formal function and the six
indicators of performance measurement are positive and
significant (Table 17). The longer the length of time that
logistics has been a formal function, the greater the use of
internal and benchmarking performance measures. This also
applies to cost and differentiation performance measurement.

Thus, at the overall level, H3 is supported when the
length of time that logistics has been a formal function is
used to indicate formalization of the logistics function.

But support for H3 is not complete since the interaction
term in the three internal performance measurement GLM models
are significant (Table 10). This suggests that H10 is not
supported. The relationship between the length of time that
the logistics function has been.a formal function and internal
performance measurement is not consistent across
manufacturers, wholesalers and retailers. The exact nature

of these differences is explicated in the subgroup analysis.

94

Table 10
GLM MODELS OF PERFORMANCE MEASURES USED AS

A FUNCTION OF NUMBER OF YEARS LOGISTICS HAS BEEN A FORMAL

FUNCTION (NY) AND CHANNEL POSITION (CP)

 

 

 

 

Dependent Overall * R- Partial F—valueg
Variable F-Value Square NY CP NY*CP
Internal
Cost 3.54 0.045 3.65 2.34 4.69
(.0038) ** (.0569) (.0979) (.0097)
Differentiation 6.04 0.074 4.46 8.03 4.85
(.0001) (.0355) (.0004) (.0083)
Total 5.10 0.063 4.86 4.84 5.48
(.0002) (.0281) (.0084) (.0045)
Benchmarking
Cost 5.13 0.064 6.02 9.09 0.73
(.0001) (.0146) (.0001) (.4819)
Differentiation 20.95 0.218 5.28 48.65 1.07
(.0001) (.0221) (.0001) (.3499)
Total 9.97 0.117 10.00 17.92 2.01
(.0001) (.0017) (.0001) (.1351)

* d.f.=5 and 376 for model
** p-values in parentheseg.

and error, respectively.

95

The results of the ANOVA models examining the effects of
the number of times the logistics function has been
reorganized during the past five years (H4), channel position
and an interaction term are presented in Table 11. All the
"number of times" main effects and interaction terms are
nonsignificant. The correlations shown in Table 17 are also
nonsignificant. Hypothesis four is thus not supported: the
number of times that the logistics function has been
reorganized during the past five years is unrelated to
performance measurement.

The last structuring ‘variable examined in the
dissertation is participation in business units strategic
planning by the senior logistics executive. The GLM models
are presented in Table 12 and the overall correlations are
presented in Table 17. The "participation in strategic
planning" main effect is significant for all three internal
performance measurement models, and the correlations between
participation in strategic planning and internal performance
measurement are positive and significant (Table 17). The
interaction term in these three models are nonsignificant.
Thus, as the senior logistics executive increasingly
participates in strategic planning for the business unit,
internal performance measurement also increases. Hypothesis
five and ten are thus supported for internal performance
measurement.

The evidence for benchmarking is not quite as strong.

96

Table 11

GLM MODELS OF PERFORMANCE MEASURES USED AS A FUNCTION
OF NUMBER OF TIMES LOGISTICS HAS BEEN REORGANIZED (NR)

AND CHANNEL POSITION (CP)

 

 

 

 

Dependent Overall * R- Partial F-values
Variable F-Value Square NR CP NR*CP
Internal
Cost 1.25 0.017 0.12 2.24 0.83
(.2840) ** (.7274) (.1079) (.4366)
Differentiation 3.56 0.046 0.01 8.52 0.37
(.0037) (.9575) (.0002) (.6884)
Total 2.31 0.030 0.06 5.03 0.71
(.0437) (.8054) (.0070) (.4925)
Benchmarking
Cost 4.23 0.054 0.02 10.06 0.52
(.0009) (.8993) (.0001) (.5976)
Differentiation 19.49 0.207 0.01 48.26 0.47
(.0001) (.9382) (.0001) (.6226)
Total 7.24 0.094 0.01 18.26 1.08
(.0001) (.9123) (.0001) (.3409)

* d.f.=5 and 373 for model
** D-values in parentheses.

and error, respectively.

97

Table 12

GLM MODELS OF PERFORMANCE MEASURES USED AS A FUNCTION

THE SENIOR LOGISTICS EXECUTIVE PARTICIPATION IN
STRATEGIC PLANNING (PP) AND CHANNEL POSITION (CP)

 

 

 

 

Dependent Overall * R- Partial F-values
Variable F-Value Square PP CP PI*CP
Internal
Cost 5.71 0.065 16.13 5.54 0.68
(.0001) ** (.0001) (.0042) (.5092)
Differentiation 7.40 0.082 7.53 14.67 0.06
(.0001) (.0063) (.0001) (.9396)
Total 7.27 0.081 14.80 10.59 0.19
(.0001) (.0001) (.0001) (.8311)
Benchmarking
Cost 5.27 0.060 0.57 12.86 0.04
(.0001) (.4522) (.0001) (.9622)
Differentiation 23.45 0.222 0.16 55.21 0.33
(.0001) (.6884) (.0001) (.7179)
Total 11.18 0.120 3.59 25.85 0.31
(.0001) (.0588) (.0001) (.7329)

* d.f.=5 and 412 for model

** p-value§:in parentheses.

and error, respectively.

98
The interaction term in the three benchmarking models are all
nonsignificant (Table 12). The participation in strategic
planning main effect is significant for total benchmarking,
but the correlation (Table 17) is weak (r=.088; p<.10). Thus
H5 is weakly supported when total benchmarking performance
measurement is considered, but not supported for either cost

or differentiation benchmarking.

PICTORIAL CONFIGURATION AND PERFORMANCE MEASUREMENT

The results of the ANOVA and GLM models of the four
pictorial configuration variables appear in Tables 13 through
16.

The results of the ANOVA models for whether the logistics
function has line responsibility for customer service are
shown in Table 13. Hypothesis six stated that when the
logistics function has line control over customer service,
differentiation performance measurement would be enhanced
(H6a), but cost performance measurement would not be affected
(H6b). The t-tests shown in Table 18 are two-tailed for the
tests Of cost based performance measures and one-tailed for
the tests of differentiation based and total performance
measures.

The "customer service" main effect is significant in all
six ANOVA models. None of the interaction terms are
significant. The t-tests (Table 18) show that when the

logistics function has line control over customer service, a

99

Table 13

ANOVA MODELS OF PERFORMANCE MEASURES USED AS A FUNCTION
OF WHETHER LOGISTICS CONTROLS CUSTOMER SERVICE (CS) AND
CHANNEL POSITION (CP)

 

 

 

 

Dependent Overall * R- Partial F-values
Variable F-Value Square CS CP CS*CP
Internal
Cost 2.88 0.033 6.85 2.34 1.42
(.0145) ** (.0092) (.0975) (.2428)
Differentiation 6.89 0.076 17.91 7.85 0.42
(.0001) (.0001) (.0005) (.6550)
Total 5.00 0.056 13.18 4.85 1.05
(.0002) (.0003) (.0083) (.3493)
Benchmarking
Cost 5.04 0.057 5.91 9.36 0.29
(.0002) (.0155) (.0001) (.7468)
Differentiation 22.08 0.209 13.05 48.09 0.36
(.0001) (.0001) (.0001) (.6983)
Total 9.62 0.103 9.54 18.83 0.44
(.0001) (.0021) (.0001) (.6416)

* All models are based on d.f.=5 and 419.
** p-valuesfiin parentheses.

100
larger number of internal differentiation measures are used
(supporting H6a), but also that a larger number of internal
cost measures are used (contradicting H6b). Thus when the
logistics function controls customer service internal
performance is more intensely monitored.

With respect to benchmarking, the t-tests (Table 18) show
that when the logistics function controls customer service,
both cost and differentiation-based benchmarking and total
benchmarking are enhanced. Again, this supports H6a, but not
H6b.

The second pictorial configuration variable examined is
whether the logistics function has line responsibility for
logistics system planning. The results of the two-factor
ANOVA models are shown in Table 14 and the t-tests are shown
in Table 18. These t-tests are either one or two-tailed
depending on the wording of the hypothesis.

The interaction term is nonsignificant and the "logistics
systems planning" main effect is significant in all six ANOVA
models. The t-tests reveal that when the logistics function
has line authority over logistics systems planning that the
following types of performance measurement are enhanced:
internal cost and cost benchmarking (supporting H7b) and
internal differentiation and. differentiation benchmarking
(contradicting H7a). Both total internal and total
benchmarking performance measurement are enhanced when the

logistics function has line responsibility for logistics

101

Table 14
ANOVA MODELS OF PERFORMANCE MEASURES USED AS
A FUNCTION OF WHETHER LOGISTICS CONTROLS LOGISTICS
SYSTEMS PLANNING (LSP) AND CHANNEL POSITION (CP)

 

 

 

 

Dependent Overall * R- Partial F-values
Variable F-Value Square LSP CP LSP*CP
Internal
Cost 2.02 0.024 4.95 2.16 0.42
(.0748) ** (.0267) (.1168) (.6591)
Differentiation 6.44 0.071 19.03 6.46 0.12
(.0001) (.0001) (.0017) (.8857)
Total 4.05 0.046 11.78 4.16 0.07
(.0013) (.0007) (.0163) (.9337)
Benchmarking
Cost 5.14 0.058 9.09 7.99 0.30
(.0001) (.0027) (.0004) (.7418)
Differentiation 23.71 0.221 35.70 40.03 1.38
(.0001) (.0001) (.0001) (.2520)
Total 10.38 0.110 22.21 14.64 0.20
(.0001) (.0001) (.0001) (.8164)

* d.f.=5 and 419 for model
** prvalues in parentheses.

and error, respectively.

102
systems planning. Thus, at the general level, the evidence
does not support H7.

The third pictorial configuration variable examined is
the number of activities that the logistics function has line
responsibility over, or the span logistics of control. Table
15 present the results of the GLthodels and Table 17 presents
the correlations. The interaction term is nonsignificant in
all six models. The "number of activities" main effect is
significant in all six models. The correlations (Table 17)
between the span of logistics control and each six indicators
of' performance :measurement. are jpositive and significant.
Hypothesis eight is thus strongly supported. The larger the
span of logistics control, the greater the performance
measurement.

The last pictorial configuration variable examined is the
title level of the senior logistics executive. The results
of the GLM models are shown in Table 16 and the correlations
are presented in Table 17. The "title level of the senior
logistics executive" main effect is predictive of internal
differentiation and differentiation benchmarking performance
measurement. However, the respective significant correlations
(Table 17) are in a direction opposite to that hypothesized.
The correlation between internal differentiation performance
measurement and "title level" (r=-.091; p<.10) and between
differentiation benchmarking and "title level" (r=-.192;

p<.01) are negative. The inverse relationship indicates that

103

Table 15
GLM MODELS OF PERFORMANCE MEASURES USED AS A FUNCTION
OF NUMBER OF ACTIVITIES CONTROLLED BY LOGISTICS (NA)
AND CHANNEL POSITION (CP)

 

 

 

 

Dependent Overall * R- Partial F-values
Variable F-Value Square NA CP NA*CP
Internal
Cost 4.81 0.054 21.73 0.73 0.44
(.0003) ** (.0001) (.4848) (.6429)
Differentiation 11.58 0.121 48.24 4.08 0.75
(.0001) (.0001) (.0176) (.4718)
Total 8.73 0.094 38.52 1.87 0.69
(.0001) (.0001) (.1550) (.5040)
Benchmarking
Cost 5.55 0.062 11.06 7.74 0.60
(.0001) (.0010) (.0005) (.5469)
Differentiation 24.13 0.224 34.66 41.24 1.75
(.0001) (.0001) (.0001) (.1747)
Total 11.41 0.120 24.74 14.69 1.47
(.0001) (.0061) (.0001) (.2310)

* d.f.=5 and 419 for model and error, respectively.

** pevalues in parentheses.

104

Table 16

GLM MODELS OF PERFORMANCE MEASURES USED AS A FUNCTION

CHANNEL POSITION (CP)

OF LEVEL OF SENIOR LOGISTICS EXECUTIVE (LE) AND

 

 

 

 

Dependent Overall * R- Partial F-values
Variable F-Value Sguare LE CP LE*CP
Internal
Cost 2.32 .043 0.93 4.17 1.17
(.0425) ** (.3343) (.0161) (.3113)
Differentiation 4.95 .058 3.49 9.75 0.89
(.0002) (.0625) (.0001) (.4106)
Total 3.48 .042 0.07 7.45 1.21
(.0043) (.7952) (.0007) (.2985)
Benchmarking
Cost 5.24 .061 0.73 11.50 1.24
(.0001) (.3942) (.0001) (.2915)
Differentiation 21.80 .214 18.74 43.65 1.46
(.0001) (.0001) (.0001) (.2324)
Total 8.96 .100 2.67 20.26 0.80
(.0001) (.1031) (.0001) (.4491)

* d.f.=5 and 401 for model and error, respectively.
**1p-values in parentheses.

105

Table 17

CORRELATIONS FOR ALL FIRMS

 

 

Type of Performance Measure

 

Independent Variable Cost Diff Total

Internal

Strategy

Frequency of logistics 0.005 0.007 0.006
strategic plan update (.9427) (.9300) (.9317)

Structuring Variables

No. of years logistics 0.096 0.105 0.110
a formal function (.0602) (.0408) (.0316)

No. of times logistics -0.018 -0.003 -0.013
has been reorganized (.7281) (.9582) (.8072)

Participation in 0.194 0.129 0.182
strategic planning (.0001) (.0080) (.0002)

Pictorial Configuration

No. Of activities 0.221 0.318 0.289
logistics controls (.0001) (.0001) (.0001)

Title level of senior 0.048 -0.091 -0.012
logistics executive (.3383) (.0681) (.7984)

Benchmarking

Strategy

Frequency of logistics 0.045 0.177 0.064
strategic plan update (.5435) (.0161) (.3885)

Structuring Variables

No. of years logistics 0.122 0.105 0.153
a formal function (.0167) (.0406) (.0027)

No. of times logistics 0.006 0.004 0.005
has been reorganized (.9015) (.9477) (.9161)

Participation in 0.036 0.017 0.088
strategic planning (.4637) (.7221) (.0736)

Pictorial Configuration

No. of activities 0.157 0.253 0.228
logistics controls (.0011) (.0001) (.0001)

Title level of senior -0.041 -0.192 -0.077
logistics executive (.4063) (.0001) (.1191)

 

106

Table 18
T-TESTS OF DIFFERENCE IN MEANS OF

PERFORMANCE MEASURES USED: ALL FIRMS

 

 

 

 

 

 

 

Type of Perfor- Yes NO t- p-

mance Measure Mean s.d. Mean s.d. value value
Mission Statement

Internal Cost 17.75 (4.36) 14.87 (5.60) 5.663 .0001
Internal Diff. 10.47 (3.16) 8.13 (4.07) 6.340 .0001
Total Internal 28.21 (6.85) 22.99 (8.07) 5.567 .0001
Cost Benchmarking 1.67 (1.07) 1.24 (1.10) 3.641 .0002
Diff. Benchmarking 1.38 (0.82) 1.07 (0.90) 3.291 .0006
Total Benchmarking 5.85 (2.99) 4.11 (3.11) 5.288 .0001
Strategic Plan

Internal Cost 17.31 (4.80) 14.40 (5.53) 5.763 .0001
Internal Diff. 10.08 (3.55) 7.76 (3.97) 6.232 .0001
Total Internal 27.39 (7.69) 22.16 (8.45) 6.562 .0001
Cost Benchmarking 1.61 (1.06) 1.19 (1.12) 3.979 .0001
Diff. Benchmarking 1.30 (0.85) 1.05 (0.91) 2.877 .0021
Total Benchmarking 5.47 (3.14) 3.98 (3.09) 4.869 .0001
Logistics Controls Customer Service

Internal Cost * 16.56 (5.35) 15.17 (5.37) 2.607 .0095
Internal Diff. 9.80 (3.69) 8.18 (4.02) 4.171 .0001
Total Internal * 26.36 (8.43) 23.37 (8.39) 3.598 .0004
Cost Benchmarking * 1.53 (1.09) 1.27 (1.11) 2.388 .0174
Diff. Benchmarking 1.34 (0.86) 1.05 (0.89) 3.327 .0005
Total Benchmarking * 5.22 (3.06) 4.29 (3.22) 2.970 .0031
Logistics Controls Logistics Systems Planning

Internal Cost 16.39 (5.10) 15.22 (5.57) 2.221 .0135
Internal Diff. * 9.76 (3.62) 8.11 (4.08) 4.395 .0001
Total Internal * 26.15 (8.01) 23.34 (8.71) 3.414 .0007
Cost Benchmarking 1.56 (1.08) 1.24 (1.12) 2.971 .0015
Diff. Benchmarking * 1.43 (0.82) 0.97 (0.89) 5.486 .0001
Total Benchmarking * 5.46 (3.04) 4.06 (3.17) 4.576 .0001
* Implies that the t-test is two-tailed rather than

one-tailed.

107
as the title level increases, a less intense effort is made

at differentiation performance measurement.

SUMMARY OF THE OVERALL ANALYSIS

The results of the GLM models, the correlations and the
t-tests are summarized in Table 19. The independent variables
are listed by row and the dependent variables are listed by
column. The significance of the main effect for the each
independent variable, the channel position main effect and the
interaction term between the respective independent variable
and the channel position variable are shown.

An "i" in Table 19 indicates the significance of an
interaction term; an "x" indicates that the main.effect listed
in the column is significant and that the correlation or t-
test.is also significant in the hypothesized.direction; an "a"
indicates that the main effect is significant and that the
correlation or t-test is also significant but opposite to the
hypothesis; a "p" indicates that the channel position main
effect is significant: and a blank space indicates that the
main effect or the interaction term in the GLM model is not
significant.

If Table 19 met all expectations, then the following
would be found: (1) all entries under the interaction heading,
"I," would be blank, indicating that the H2 through H9 are
invariant across channel position; (2) except for the

"logistics controls customer service" and "logistics controls

108

Table 19

SUMMARY OF ANOVA AND GLM MODELS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Internal Benchmarking

Independent Cost Diff Total Cost Diff Total
Variable M P I M P I M P I M P I M P I M P I
Strategy
Strategic plan

updates [[71 I IIDII I IQII [:12lj IQIDII IIIDII
Structuring
Variables
Mission

statement x x D x D x D x D D
Strategic plan a D x D x D x D x D
Time a formal

function x p 1 p x p 1 x p x p x p
Reorganizations D D D D D
Participation in

planning LxIle IXIDII [xlpfl [19L] [JpLJ b11213
Pictorial
Configuration
Logistics

controls CS IaI I I klpl I EIQI I IaIpI IIXIQI I EIQII
Logistics

controls LSP [xlpl I E101] lepl I lxlpl ] [alpl ] [xlpl ]
Number of

activities [x1 U [xlpl 1 M H IxIpI I IxIpI I EIQI I
Title level of

executive I IpII IaIpI I Lip£_I I IpII IaIpII I IpII

=main effect of variable listed in column.
P=main effect for the channel position variable.
I=Interaction effect between main effect and channel
position variable.
i=significant interaction effect.
x=implies both significant main effect and significant

correlation (or t-test) in hypothesized direction.

p=significant channel position main effect.
a=implies both significant main effect and significant
correlation (or t-test) opposite to hypothesized

direction.

blank space=no significant relationship.

 

109

logistics system planning" rows, all columns under the main
effect headings, "M," would contain an "x": (3) the "customer
service" row would contain an "x" under the internal
differentiation and differentiation benchmarking main effect
columns; and (4) the "logistics systems planning" row would
contain an "x" under the internal cost and cost benchmarking
main effect columns. The symbol "a" would not appear
anywhere. No expectations were made for columns under the
channel position, "P," main effect.

The pattern that exists in Table 19 strongly supports
H10. Only three of sixty interaction terms are significant,
and all three are located in GLM models examining the effect
of the length of time logistics has been a formal function on
internal performance measurement. The conclusion that can be
drawn is that, except for the previous qualification, the
manner in which logistics strategy and structure are related
to performance measurement does not differ significantly
across manufacturers, wholesalers and retailers.

Table 19 shows which variables met expectations. The
models for mission statement (H3), logistics strategic plan
(H3) and the number of activities controlled by the logistics
function (H8) have significant main effects in the expected
direction and nonsignificant interaction terms. The relevant
hypotheses are thus supported. Participation in business
units strategic planning by the senior logistics executive

(H5) is related to four dependent variables. The frequency

110
of the strategic plan update (H2) and title level of the
senior logistics executive (H9) are each related to one
performance measurement variable apiece: differentiation
benchmarking and internal cost performance measurement,
respectively. The number of reorganizations (H4) is totally
unrelated to performance measurement.

The "logistics function controls customer service"
variable is positively related to all types of performance
measurement, supporting (H6a), but not (H6b). The "logistics
function controls logistics systems planning" variable is also
positively related to all types of performance measurement.
This supports (H7b), but not (H7a).

The channel position main effect is significant in the
majority of models. Two notable exceptions are that it is
significant in four of ten internal cost GLM models and that
it is nonsignificant in one of ten total internal GLM models.
This leads to the general conclusion that the level of perfor-
mance measurement differs across channel position, but that
the relatedness of logistics strategy and structure to perfor-

mance measurement does not differ across channel positions.

SUBGROUP ANALYSIS

Tables 20 through 26 present the results of the subgroup

analysis. The first four tables contain t-tests for mission

statement, logistics strategic plan, logistics controls

111
customer service and logistics controls logistics systems
planning, respectively. Each of these tables contains
separate sections on manufacturers, wholesalers and retailers.
Tables 24, 25 and 26 contain correlations for the

manufacturers, wholesalers and retailers, respectively.

SUBGROUP ANALYSIS: STRATEGY

The correlations between the strategy variable (H2) and
the performance measurement variables can be seen in Table 24
for manufacturers, in Table 25 for wholesalers and in Table
26 for retailers. The frequency of the logistics strategic
plan update is unrelated to all six indicators of performance
measurement for all three types of firms. However, 12 of 18
correlations are positive, suggesting an extremely weak
positive relationship could actually be present. This
contradicts H2. In several cases, the correlations approach
significance. The correlation for internal cost measures
(r=.226: p=.13) and for differentiation benchmarking (r=.243;
p=.10) among retailers provide examples. At the subgroup
level, it is apparent that performance measurement and the
frequency of strategic plan update are unrelated, however,

there is a tendency toward a weak positive relationship.

SUBGROUP ANALYSIS: STRUCTURING VARIABLES
The results of the subgroup analysis for the mission

statement (H3) variable appear in Table 20. Except for the

112

Table 20
SUBGROUP ONE-TAILED T-TESTS OF DIFFERENCE IN MEANS OF
PERFORMANCE MEASURES USED BETWEEN FIRMS WITH VERSUS WITHOUT
A LOGISTICS MISSION STATEMENT

 

 

 

 

 

 

 

Type of Mission Statement

Performance Yes No t- p-

Measure Mean s.d. Mean s.d. value value
Manufacturers

Internal Cost 17.53 (4.36) 15.84 (5.79) 2.159 .0162
Internal Diff. 10.97 (3.08) 9.20 (4.17) 3.283 .0007
Total Internal 28.50 (7.11) 25.04 (9.10) 2.861 .0024
Cost Benchmarking 1.77 (1.00) 1.57 (1.07) 1.252 .1061
Diff. Benchmarking 1.71 (0.60) 1.55 (0.72) 1.586 .0573
Total Benchmarking 6.33 (2.69) 5.39 (2.89) 2.168 .0157
Wholesalers

Internal Cost 18.62 (4.45) 14.16 (5.75) 3.711 .0002
Internal Diff. 10.27 (3.03) 7.61 (3.84) 3.304 .0006
Total Internal 28.88 (7.11) 21.77 (8.45) 3.991 .0001
Cost Benchmarking 1.81 (1.17) 1.04 (1.05) 3.294 .0006
Diff. Benchmarking 1.35 (0.80) 0.81 (0.90) 2.835 .0026
Total Benchmarking 6.07 (3.38) 3.40 (2.88) 4.156 .0001
Retailers

Internal Cost 17.50 (3.65) 14.26 (4.45) 3.543 .0003
Internal Diff. 9.65 (3.32) 6.87 (3.81) 3.488 .0004
Total Internal 27.15 (6.18) 21.13 (7.45) 3.919 .0001
Cost Benchmarking 1.35 (1.07) 0.96 (1.09) 1.642 .0522
Differentiation 0.76 (0.85) 0.60 (0.82) 0.881 .1905
Total Benchmarking 4.74 (3.04) 2.83 (3.12) 2.805 .0031

 

113

Table 21

SUBGROUP ONE-TAILED T-TESTS OF DIFFERENCE IN MEANS OF
PERFORMANCE MEASURES USED BETWEEN FIRMS WITH VERSUS

WITHOUT A LOGISTICS STRATEGIC PLAN

 

 

 

 

 

 

 

 

Type of Strategic Plan

Performance Yes No t- p-
Measure Mean s.d. Mean s.d. value value
Manufacturers

Internal Cost 18.10 (4.54) 14.73 (5.78) 4.405 .0001
Internal Diff. 11.14 (3.03) 8.50 (4.23) 4.876 .0001
Total Internal 29.24 (6.91) 23.23 (9.03) 5.079 .0001
Cost Benchmarking 1.83 (1.02) 1.49 (1.07) 2.203 .0145
Diff. Benchmarking 1.67 (0.61) 1.54 (0.75) 1.224 .1112
Total Benchmarking 6.30 (2.73) 5.20 (2.91) 2.667 .0041
Wholesalers

Internal Cost 16.41 (6.06) 14.24 (5.56) 2.139 .0171
Internal Diff. 9.07 (4.05) 7.60 (3.69) 2.161 .0162
Total Internal 25.48 (9.64) 21.84 (7.99) 2.395 .0089
Cost Benchmarking 1.61 (1.08) 1.01 (1.08) 3.110 .0012
Diff. Benchmarking 1.13 (0.91) 0.80 (0.88) 2.071 .0201
Total Benchmarking 5.22 (3.42) 3.33 (2.87) 3.481 .0003
lie—tall—ets

Internal Cost 16.66 (3.71) 14.00 (4.89) 2.770 .0037
Internal Diff. 9.04 (3.46) 6.35 (3.74) 3.465 .0004
Total Internal 25.70 (6.25) 20.35 (8.02) 3.497 .0004
Cost Benchmarking 1.22 (1.04) 0.92 (1.16) 1.272 .1035
Diff. Benchmarking 0.78 (0.86) 0.51 (0.77) 1.490 .0699
Total Benchmarking 4.14 (3.16) 2.73 (3.12) 2.070 .0207

 

114

Table 22

SUBGROUP T-TESTS OF DIFFERENCE IN MEANS OF
PERFORMANCE MEASURES USED BETWEEN FIRMS WITH LOGISTICS
CONTROLLING VERSUS NOT CONTROLLING CUSTOMER SERVICE

 

 

Logistics Function Controls

 

 

 

 

one-tailed.

Type of Customer Service

Performance Yes No t- p-

Measure Mean s.d. Mean s.d. value value
Manufacturers

Internal Cost * 16.72 (5.56) 16.20 (5.35) 0.643 .5211
Internal Diff. 10.24 (3.71) 9.41 (4.07) 1.445 .0751
Total Internal * 26.95 (8.65) 25.61 (8.46) 1.065 .2883
Cost Benchmarking * 1.74 (1.04) 1.56 (1.06) 1.104 .2710
Diff. Benchmarking 1.61 (0.68) 1.61 (0.69) 0.039 .4845
Total Benchmarking * 5.98 (2.80) 5.52 (2.93) 1.102 .2717
Wholesalers

Internal Cost * 15.56 (5.31) 14.62 (5.96) 0.897 .3711
Internal Diff. 9.07 (3.73) 7.66 (3.82) 2.056 .0208
Total Internal * 24.63 (8.51) 22.28 (8.65) 1.508 .1337
Cost Benchmarking * 1.30 (1.10) 1.14 (1.12) 0.789 .4314
Diff. Benchmarking 1.00 (0.95) 0.87 (0.88) 0.819 .2072
Total Benchmarking * 4.35 (3.14) 3.72 (3.17) 1.093 .2762
Retailers

Internal Cost * 17.82 (4.19) 14.75 (4.20) 2.981 .0037
Internal Diff. 9.18 (3.39) 7.46 (3.96) 1.836 .0349
Total Internal * 27.00 (6.98) 22.21 (7.37) 2.686 .0087
Cost Benchmarking * 1.05 (1.09) 1.10 (1.09) 0.214 .8308
Diff. Benchmarking 0.77 (0.97) 0.63 (0.79) 0.684 .2480
Total BenChmarking * 3.41 (2.97) 3.63 (3.24) 0.286 .7753
* Implies that the t-test is two-tailed rather than

115

Table 23
SUBGROUP T-TESTS OF DIFFERENCE IN MEANS OF
PERFORMANCE MEASURES USED BETWEEN FIRMS WITH LOGISTICS
CONTROLLING VERSUS NOT CONTROLLING LOGISTICS SYSTEMS PLANNING

 

 

Logistics Function Controls

 

 

 

 

 

 

Type of Logistics Systems Planning

Performance Yes No t- p-

Measure Mean s.d. Mean s.d. value value
Manufacturers

Internal Cost 16.95 (5.01) 15.66 (6.10) 1.479 .0709
Internal Diff. * 10.19 (3.58) 9.28 (4.35) 1.470 .1441
Total Internal * 27.14 (7.89) 24.94 (9.54) 1.614 .1091
Cost Benchmarking 1.73 (1.01) 1.53 (1.11) 1.262 .1042
Diff. Benchmarking * 1.63 (0.67) 1.57 (0.70) 0.546 .5856
Total Benchmarking * 5.97 (2.86) 5.43 (2.84) 1.244 .2152
Wholesalers

Internal Cost 15.00 (5.55) 14.85 (5.88) 0.136 .4461
Internal Diff. * 9.08 (3.55) 7.71 (3.89) 1.932 .0552
Total Internal * 24.08 (8.45) 22.56 (8.72) 0.940 .3489
Cost Benchmarking 1.21 (1.17) 1.18 (1.10) 0.104 .4588
Diff. Benchmarking * 1.18 (0.91) 0.81 (0.88) 2.250 .0259
Total Benchmarking * 4.62 (3.10) 3.65 (3.16) 1.644 .1023
Retailers

Internal Cost 15.87 (4.38) 15.37 (4.40) 0.467 .3208
Internal Diff. * 8.70 (3.67) 7.60 (3.94) 1.173 .2438
Total Internal * 24.57 (7.19) 22.97 (7.65) 0.876 .3836
Cost Benchmarking 1.26 (1.05) 1.03 (1.10) 0.878 .1913
Diff. Benchmarking * 0.83 (0.94) 0.61 (0.80) 1.062 .2909
Total Benchmarking * 4.26 (3.28) 3.34 (3.11) 1.204 .2319

* Implies that the t-test

onsétailed.

is two-tailed

rather than

116

Table 24
SUBGROUP CORRELATIONS: MANUFACTURERS

 

 

Type of Performance Measure

 

 

Independent Variable Cost Diff Total

Internal

Strategy

Frequency of logistics 0.006 -0.180 0.026
strategic plan update (.9546) (.6545) (.8086)

Structuring Variables

No. of years logistics -0.018 —0.065 -0.041
a formal function (.8080) (.3796) (.5789)

No. of times logistics -0.058 -0.006 -0.040
has been reorganized (.4485) (.9422) (.6063)

Participation in strategic 0.236 0.175 0.229
planning (.0012) (.0172) (.0017)

Pictorial Configuration

No. of activities 0.150 0.214 0.193
logistics controls (.0403) (.0033) (.0082)

Title level of senior 0.101 -0.023 0.054
logistics executive (.1721) (.7552) (.4678)

Benchmarking

Strategy

Frequency of logistics -0.098 0.003 -0.116
strategic plan update (.3489) (.9798) (.2673)

Structuring Variables

No. of years logistics 0.154 0.169 0.096
a formal function (.0352) (.0208) (.1906)

No. of times logistics -0.043 -0.038 -0.062
has been reorganized (.5758) (.6210) (.4185)

Participation in strategic 0.084 0.089 0.186
planning (.2532) (.2264) (.0115)

Pictorial Configgration

No. of activities 0.147 0.075 0.123
logistics controls (.0449) (.3050) (.0938)

Title level of senior -0.092 0.169 -0.022
logistics executive (.2122) (.0220) (.7647)

 

117

Table 25

SUBGROUP CORRELATIONS: WHOLESALERS

 

 

Type of Performance Measure

 

 

Independent Variable Cost Diff Total

Internal

Strategy

Frequency of logistics -0.180 -0.147 -0.176
strategic plan update (.2345) (.3352) (.2488)

Structuring Variables

No. of years a logistics 0.312 0.326 0.354
a formal function (.0006) (.0004) (.0001)

No. of times logistics 0.085 0.061 0.084
has been reorganized (.3405) (.4957) (.3438)

Participation in strategic 0.234 0.166 0.231
planning (.0047) (.0455) (.0053)

Pictorial Configuration

No. of activities 0.174 0.257 0.232
logistics controls (.0345) (.0016) (.0049)

Title level of senior 0.040 -0.059 0.001
logistics executive (.6428) (.4947) (.9985)

Benchmarking

Strategy

Frequency of logistics 0.171 0.210 0.164
strategic plan update (.2616) (.1665) (.2808)

Structuring Variables

No. of years a logistics 0.159 0.153 0.300
a formal function (.0881) (.1016) (.0011)

No. of times logistics 0.065 0.067 0.104
has been reorganized (.4760) (.4491) (.2430)

Participation in strategic 0.060 0.112 0.089
planning (.4720) (.1809) (.2896)

Pi tggial Configuration

No. of activities 0.052 0.214 0.222
logistics controls (.5319) (.0090) (.0067)

Title level of senior 0.052 0.214 0.009
logistics executive (.5319) (.0090) (.9127)

 

118

Table 26
SUBGROUP CORRELATIONS: RETAILERS

 

 

TVDe of Performance Measure

 

 

 

Independent Variable Cost Diff Total

Internal

Strategy

Frequency of logistics 0.226 0.031 0.154
strategic plan update (.1269) (.8367) (.3003)

Structuring Variables

No. of years logistics -0.012 0.145 0.070
a formal function (.9180) (.2013) (.5419)

No. of times logistics -0.074 -0.061 -0.076
has been reorganized (.5155) (.5899) (.5133)

Participation in strategic 0.115 0.193 0.167
planning (.2870) (.0174) (.1200)

Pictorial Configuration

No. of activities 0.328 0.360 0.377
logistics controls (.0016) (.0005) (.0003)

Title level of senior 0.255 0.122 0.211
logistics executive (.0177) (.2625) (.0517)

Benchmarking

Strategy

Frequency of logistics 0.052 0.243 0.100
strategic plan update (.7273) (.1003) (.5029)

Structuring Variables

No. of years logistics 0.037 0.006 0.082
a formal function (.7469) (.9604) (.4749)

No. of times logistics 0.047 -0.024 0.027
has been reorganized (.6793) (.8362) (.8146)

Participation in strategic 0.097 0.147 0.181
planning (.3665) (.7221) (.0909)

Pictorial Configuration

No. of activities 0.021 0.008 -0.008
logistics controls (.8463) (.9385) (.9422)

Title level of senior 0.125 0.048 0.142
logistics executive (.2514) (.6599) (.1912)

 

119

inability of a mission statement to predict the level Of
differentiation benchmarking for retailers, the presence of
a logistics mission statement is predictive of all four types
of performance measurement, plus total internal and total
benchmarking performance measurement for all three types of
firms. Firms with logistics mission statement use more
performance measures.

Table 21 presents the subgroup analysis for the existence
of a logistics strategic plan (H3). Aside from the nonsig-
nificant difference in means for differentiation benchmarking
among manufacturers and for cost benchmarking among retailers,
a logistics strategic plan is associated with a more intense
effort to monitor performance for all types of firms.

The subgroup correlations between the length Of time that
logistics has been a formal function (H3) and performance
measurement are presented in.Tables 24, 25 and 26 for manufac-
turers, wholesalers and retailers, respectively. The inter-
action term in the GLM models were significant (Table 10) for
internal performance measurement. For manufacturers and
retailers the length of time that logistics has been a formal
function and internal cost, internal differentiation and total
internal performance measurement are unrelated. The "length
of time" variable and internal performance measurement are
significantly related among wholesalers (r=312: p<.01 for
internal cost: r=.326; p<.01 for internal differentiation:

r=.354: p<.01 for total internal). The:GLthodel interactions

120
can.be explained as follows: the length of time that logistics
has been a formal function is positively related to internal
performance :measurement. among ‘wholesalers, but. not. among
manufacturers or retailers.

With respect to benchmarking, the length of time that
logistics has been a formal function is: (1) related to cost
and differentiation benchmarking among manufacturers: (2)
related to cost and total benchmarking among wholesalers; and
(3) not related to any of the benchmarking variables among
retailers. While the interaction term in the relevant GLM
models were not significant, the length of time that logistics
has been a formal function is related to total benchmarking
in manufacturers and wholesalers, but not retailers.

The subgroup correlations for the number of times the
logistics function has been reorganized during the past five
years (H4) appear in Tables 24, 25 and 26. All six
correlations for manufacturers, wholesalers or retailers are
nonsignificant.

Several subgroup correlations between participation in
business unit strategic planning by the senior logistics
executive (H5) and performance measurement are significant.
Participation is related to the total number of internal
performance measures used among manufacturers (r=.229; p<.01)
and in wholesalers (r=.231: p<.01); among retailers, the
correlation is almost significant (r=.167: p=.12). As far as

the total number of benchmarking measures used is concerned,

121
participation in strategic planning is significantly
correlated among manufacturers (r=.186: p<.10) and retailers

(r=.181; p<.10), but not among wholesalers (r=.089: p=.28).

SUBGROUP ANALYSIS: PICTORIAL CONFIGURATION

The first pictorial configuration variable examined is
whether the logistics function has line responsibility over
customer service (H6). The results of the t-tests are shown
in Table 22. These tests are one or two-tailed depending on
the specific wording of the hypothesis. As can be seen in
Table 22, whether the logistics function controls customer
service is unrelated to the usage of cost based performance
measures (supporting H6b). Except for retailers and internal
cost performance measurement, this holds for manufacturers,
wholesalers and retailers. It was expected that when the
logistics function has line responsibility for customer
service, differentiation performance measurement would be
enhanced (H6a). This holds only for internal performance
measurement, and not for benchmarking measurement for all
three subgroups.

The results of the t-tests for whether the logistics
function controls logistics systems planning (H7) are shown
in Table 23. Cost measurement (H7b), but not differentiation
measurement (H7a), is expected: to Ibe enhanced ‘when the
logistics function controls logistics systems planning. From

Table 23, it can be seen within manufacturers and retailers

122

that whether the logistics function controls logistics systems
planning is unrelated to internal and benchmarking
differentiation performance measurement (supportimg H7a in
these subgroups), but that this configuration variable is
related to internal and benchmarking differentiation
performance measurement among wholesalers (contradicting H7a
for this subgroupo. Only among manufacturers is internal cost
performance measurement positively related to the logistics
function controlling logistics systems planning (H7b).

The subgroup correlations between the number of
activities that the logistics function has line responsibility
over (H8) and performance measurement are shown in Tables 24,
25 and 26. Among manufacturers, wholesalers and retailers,
the span of logistics.contro1 is significantly correlated with
cost, differentiation. and 'total internal performance
measurement. The total number of benchmarking measures used
is significantly correlated with the number of activities
controlled by the logistics function among manufacturers
(r=.123; p<.10) and wholesalers (r=.222; p<.10), but.not.among
retailers (r=-.008; p=.94). In fact, among retailers, neither
cost (r=.021; p=.85) nor differentiation benchmarking (p=.008;
p=.94) are related to the span of logistics control.

The last pictorial configuration variable examined is
title level of the senior logistics executive (H9): the
results are shown in Tables 24, 25 and 26. The total number

of internal performance measures used is unrelated to title

123
level among manufacturers (r=.054; p=.47) and wholesalers
(p=.001; p=.99), but these two variables are related in the
retailer subgroup (r=.211: p<.10). The only significant
relationship between title level and the remaining internal
performance measurement indicators is that existing between
internal cost and title level among retailers (r=.255: p<.05):
all remaining relationships are nonsignificant. With respect
to benchmarking, the only significant correlation is that
between differentiation benchmarking and title level among

wholesalers (r=.214: p<.01).

SUMMARY OF SUBGROUP ANALYSIS

Table 27 summarizes the subgroup analysis. In this
table: (1) an "x" represents a significant correlation or
difference in means that supports the respective hypothesis;
(2) a "z" represents no significant difference between means
that supports the respective hypothesis; (3) an "a" represents
a significant correlation or difference in means that is
opposite to the respective hypothesis: and (4) a blank space
represents a nonsignificant correlation or difference in means
not supporting the respective hypothesis.

The frequency of strategic plan updating (H2) and the
number of times that logistics has been reorganized (H4) are
totally unrelated to performance measurement within the

subgroups.

124

Table 27
SUMMARY OF SUBGROUP ANALYSES

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Manufacturers Wholesalers Retailers

Independent Int Ben Int Ben Int Ben
Variable C D T C D T C D T C D T C D T C D T
Strategy
Strategic plan

update HI]LII][IIJ[1HFTIILHI
Structuring
Variables

 

Mission statement
Strategic plan
Maturity
Reorganization
Participation in

planning IxIxIfi I I Ifi Izlxlg I I II IIIXLJ III [fl

Pictorial
Configuration

   

 

 

 

 

Log. controls CS
Log. controls LSP
No. of activities
Level of executive

 

Int=Internal performance measurement.

Ben=Benchmarking performance measurement.

C=Cost based performance measurement.

D=Differentiation based performance measurement.

T=Total internal or benchmarking performance measurement.

x=positive correlation or significant increase in

mean, whichever is appropriate (supports hypothesis).
z=no significant relationship (supports hypothesis).
a=significant relationship (opposite to hypothesis).
blank space=no significant relationship (not supporting
hypothesis).

 

125

The logistics mission statement and logistics strategic
plan elements of formalization (H3) show a high degree of
consistency across subgroups with respect to the positive
relationship to all types of performance measurement. The
third dimension of formalization, the length of time that
logistics has been a formal function (H3), is far more related
to performance measurement in wholesalers than in
manufacturers or retailers.

Another variable that shows a high degree of consistency
across subgroups in terms of its relatedness to performance
measurement is the number of activities that the logistics
function has line responsibility over (H8).

Participation in business strategic planning by the
senior logistics executive (H5) and title level of the senior
logistics executive (H9) show sporadic relatedness to selected
indicators of performance measurement across subgroups.

Lastly, the pictorial configuration variables of whether
or not logistics controls customer service (H6) or logistics
systems planning (H7) are related to some indicators of
performance measurement, but the pattern shown in Table 27 is

insufficient to fully support either H6 or H7.

SUMMARY OF RESULTS

Chapter IV presented the results of the statistical tests

that were undertaken. The substantive implications and

126

interpretations of the results are explicated in Chapter V.
In summarizing the results, the following appear to be in
order. The channel position variable, for the most part,
impacts the level of performance measurement, but not the
manner in which the independent variables are related to
performance measurement. While the subgroup analysis pointed
out some interesting differences, the majority of these were
not detected by the interaction terms in the two-factor ANOVA
and GLM models.

The variables that are the most related to performance
measurement are: (1) a logistics mission statement, a
dimension of formalization: (2) a logistics strategic plan,
another dimension of formalization; (3) whether logistics has
line control over customer service; (4) whether logistics has
line control over logistics systems planning; and (5) the
total number of activities that logistics controls in a line
fashion.

The variables that are least related to performance
measurement are: (1) the frequency of the logistics strategic
plan update: and (2) the number of times that the logistics
function has been reorganized during the past five years.

A third set of variables can be identified. These
variables are sporadically related to different dimensions of
performance measurement for different types of organizations.
Not all these differences were detected by the ANOVA and GLM

model interaction terms. These variables are: (1) the length

127
of time that logistics has been a formal function; (2)
participation in business unit strategic planning by the
senior logistics executive: and (3) the title level of the

senior logistics executive.

CHAPTER V: DISCUSSION AND CONCLUSIONS

Chapter V contains five sections. The first section
discusses each independent. variable, its relationship to
performance measurement and managerial implications. Then,
implications of the research for leading edge logistics is
discussed. Next, suggestions for future research are
presented. A limitations section follows. Lastly, the

dissertation is summarized.

DISCUSSION

The discussion section is organized as follows. First,
channel position is discussed. Then relationships among
performance measurement variables are discussed. This is
followed by separate sections on logistics strategy and

structure.

CHANNEL POSITION

Three channel positions were examined: manufacturers,
wholesalers and retailers. It was hypothesized that channel
position would have no impact on the how the independent
variables related to the dependent variables (H10). This

means that channel position does not moderate the relatedness

128

129
of logistics strategy and structure to performance
measurement. With the exception of the relationship between
the length of time that logistics has been a formal function
and internal performance measurement, this hypothesis was
supported.

Hypothesis ten does not, however, mean that channel
position has no impact on the level of performance
measurement. For example, manufacturers generally use more
performance measures than wholesalers or retailers. This
finding suggests that channel position impacts the level of
performance ‘measurement, but not the relatedness of the
independent variables to the dependent variables.

This finding presents detailed supporting evidence that
"best" logistical practices are nearly identical across
channel positions (Bowersox s; Q1. 1989). The practice of
logistics management is very similar across manufacturers,
wholesalers or retailers. Managerial practices and experience
in one type of firm, say a manufacturer, can with minimal
alteration, be applied to other types of firms independent of
channel position.

This conclusion if of importance to logistical
performance. If logistics managers transfer from a
manufacturer to a retailer they can be expected to transfer
relevant logistical management skills. A manager can
anticipate that a formal logistics mission statement developed
for a retailer will have much the same impact upon

organization practice as it did within a manufacturing

130

organization.

INTERRELATIONSHIPS AMONG PERFORMANCE MEASURES

Interrelations among the performance measurement
variables were examined. Relations were expected between the
following pairs of variables: (1) total internal and
benchmarking measurement (H1): (2) internal cost and cost
benchmarking measurement (Hla); and (3) internal
differentiation and differentiation benchmarking measurement
(Hlb).

The data supported these expectations, at an overall
level, and for manufacturers, wholesalers and retailers
separately. While all types of performance measurement are
highly related, the strength of the relationships between
internal cost and cost benchmarking and between internal
differentiation and differentiation benchmarking generally
exceeded. the relatedness across cost. and. differentiation
measures.

While organizations do measure both cost and
differentiation dimensions of performance, they lean toward
one or the other. Overall organizational strategy might
explain such an orientation. The firm leaning toward a low
cost strategy may be more likely to focus on cost performance
measures, be they internal or benchmarking. The firm relying
on a differentiation corporate level strategy may be more
likely to focus on differentiation-related performance

measures. That cost and differentiation performance

131
measurement are related, demonstrates that cost and
differentiation strategies are not mutually exclusive. Firms
pursue both strategies simultaneously, but appear to focus
more on one than another. These findings apply to all three
organizational types.

In summary, performance measurement can be internal or
benchmarking and can focus on costs or on factors that are or
can be differentiators. The pattern of relationships provides
evidence of how organizations focus performance measurement
efforts. While concentrating on both cost and differentiation
measurement, firms appear likely to rely on measures of one

more than the other.

LOGISTICS STRATEGY

The dimension of logistics strategy examined was the
frequency of updating the logistics strategic plan. Because
environmental uncertainty may lead to more frequent updating
of the strategic plan, the frequency of plan updating was
hypothesized to be inversely related to performance
measurement (H2). In the case of differentiation
benchmarking, the hypothesis was contradicted, while for the
remaining indicators of performance measurement, no meaningful
relationship was isolated.

Those organizations engaging in differentiation
benchmarking appear to be more sensitive to their competitive
environment. Environmental sensitivity can result in more

frequent plan updating since the organization is constantly

132
customizing the plan to the "latest differentiation
information."

Only those firms with a logistics strategic plan were
examined. Since these firms tend to use more performance
measures (see H3), the testing of this hypothesis was
performed on a subset of highly formalized organizations.
Thus, among formalized organizations, the frequency of
strategic plan updating is not related to performance
measurement. This does not mean that environmental
uncertainty' is unrelated. to performance 'measurement. .A
battery of scales measuring uncertainty, that is applicable
to all firms, would better serve the purpose of examining

relationships between uncertainty and performance measurement.

STRUCTURING VARIABLES

A total of five structuring variables were examined in
this research. Three are dimensions of formalization and one
representing reorganization and participation in business unit

strategic planning.

FORMALIZATION

Formalized organizations were hypothesized to use a
greater number of performance measures (H3) . Three indicators
of formalization ‘were examined: (1) a ‘written logistics
mission statement; (2) a written logistics strategiijlan: and
(3) the length of time that logistics has been a formal

function.

133

The effects of a written logistics mission statement and
a written logistics strategic plan on performance measurement
are similar and pervasive. The existence of these two
integrative devices are associated with a more intense effort
to monitor performance. This holds for all four types of
performance measurement and for total internal and total
benchmarking performance measurement. This finding holds when
all firms were considered simultaneously and for each of
manufacturers, wholesalers and retailers individually. A'
formalized logistics function in terms of formal mission and
strategy is more deeply involved in performance measurement
than less formalized logistics functions.

Managers in all three types of firms use formal
integrative devices to promote various types of performance
measurement. Potentially, a mission statement might be a
better predictor of performance measurement than a logistics
strategic plan since a mission statement is more likely to
define important performance criteria and to set performance
standards. There appears to be little difference between a
mission statement and a strategic plan in terms of their
relationship to performance measurement. A firm wishing to
promote performance measurement could generate a formal
logistics mission statement or a formal logistics strategic
plan.

The third formalization variable examined is the length
of time that logistics has been a formal function. Mature

functions were hypothesized to have had more time to implement

134

sophisticated performance measurement systems, and hence to
use a larger number of performance measures. For this
variable alone, and only in conjunction with internal
performance measurement, significant differences in the nature
of relationships across channel positions were isolated.

Within wholesalers, but not manufacturers or retailers,
the length Of time that logistics has been a formal function
is positively related to internal cost and differentiation-
based and total internal performance measurement. Unlike the
remaining'structure'variables.examined.in'this research, which
are under a high level of management control, the length of
time that logistics has been a formal function is beyond
functional management control. It is controllable only if a
formal function does not exist, in which case one can be
initiated. Mature functions, in wholesalers alone, have an
internal performance measurement advantage that cannot be
readily duplicated by competitors. Competitors can design a
formal mission statement to enhance performance measurement,
but they cannot make their logistics function equally mature.
A.logistics mission statement, a logistics strategiijla , the
number of reorganizations, and participation by the senior
logistics executive in business unit strategic planning can
be altered by the firm in isolation or in conjunction with
other organizational adjustments. Nothing can be done by
functional management about longevity.

Within the manufacturing and retailing subgroups no

relationship was found between the maturity of the logistics

135
function and internal performance measurement. This implies
that. manufacturing and retailing executives need. not be
concerned about internal performance measurement advantages
arising from the maturity of a competitor's logistics
function.

Precisely why these differences in relationships between
the maturity of the logistics function and internal
performance measurement exists across channel positions is
difficult to explain. IIf, within wholesalers, the
relationship held just for internal cost and not internal
differentiation performance measurement, or vice versa, then
it might be explained as a statistical artifact. But the
relationship holds for both types of internal performance
measurement suggesting that some underlying differences exist.
Nevertheless, it can be stated that mature wholesaling
logistics functions hold a performance measurement advantage,
and that this advantage is virtually impossible for
competitors to duplicate.

The maturity'of the logistics function.also impacts.cost,
differentiation and total benchmarking performance
measurement. While the relationships between the maturity of
the logistics function and benchmarking performance
measurement did not differ across the channel positions, at
the subgroup level these relationships exist sporadically.
For example, within retailers, maturity of logistics and
performance measurement are not related, but within

:manufacturers, maturity of the logistics function.and cost.and

136

differentiation benchmarking are positively related.

REORGANIZATIONS

The next structuring variable examined is the number of
times that the logistics function was reorganized during the
past five years. It was hypothesized to be positively related
to performance measurement (H4). The number of reorganiza-
tions is unrelated to performance measurement. Fluid
structures neither enhance nor detract performance

measurement.

PARTICIPATION IN STRATEGIC PLANNING

The last structuring variable examined is participation
in business unit strategic planning by the senior logistics
executive. Increased participation in business unit strategic
planning by the senior logistics executive was hypothesized
to be positively related to performance measurement (H5).
This was in part expected because an executive participating
in overall strategic planning indicates enhanced importance
of that executive's function. In addition, it could also be
indicative of the logistics function being used strategically.
Since benchmarking has strategic implications, the
relationship between participation by the senior logistics
executive in business unit strategic planning and performance
measurement was expected to be positive. At the overall
level, the data supported this hypothesis for total internal

and total benchmarking performance measurement. Logistical

137
performance measurement is enhanced when the senior executive
participates in overall strategic planning.

At the subgroup level, however, the relationships dissi-
pated. Within manufacturers and wholesalers, but not retail-
ers, participation is related to internal cost performance
measurement. Cost and differentiation benchmarking are unre—
lated to title level within the manufacturing, wholesaling
retailing subgroups. The conclusion is drawn that participa-
tion is sporadically related to performance measurement.

In summary, a total of five structuring variables were
examined. Integrative devices such as a logistics mission
statement or a logistics strategic plan strongly influence
performance measurement. The maturity of the logistics
function and participation of the senior logistics executive
in business unit strategiijlanni g fail to strongly influence
performance measurement. Reorganizations plays little or no

role in performance measurement.

PICTORIAL CONFIGURATION VARIABLES

A total of four pictorial configuration variables were
examined. Three are related to activity allocation and one
represents the title level of the senior logistics executive.

These two descriptors of organization are discussed.

ACTIVITY ALLOCATION

The three activity allocation variables are whether

logistics has line control over customer service, whether

138
logistics has line control over logistics systems planning and
the span of logistics control.

Whether logistics controls customer service was hypothe-
sized to be positively related to differentiation performance
measurement (H6a), but unrelated to cost performance
measurement (H6b). Whether the logistics function has line
authority over logistics systems planning was hypothesized to
be unrelated to differentiation performance measurement (H7a) ,
but positively related to cost performance measurement (H7b).

All types of performance measurement were enhanced when
the logistics function Ihas line authority over' customer
service or logistics systems planning. Revealed strategy
through activity allocation does not differentially impact the
type performance measurement. It does, however, impact all
types equally. Given the close relationship between the
logistics function controlling a specific activity and the
span of logistics control, additional implications are
discussed in the following paragraphs.

Logistics span of control was hypothesized to be related
to all types of performance measurement (H8). When all firms
were considered simultaneously, this hypothesis was strongly
supported. At the subgroup level, this hypothesis was also
strongly supported. One exception is that the number of line
activities controlled by the logistics function and total
benchmarking performance measurement are related in
manufacturers and wholesalers, but unrelated in retailers.

The hypothesis was supported for total internal performance

139
measurement for all three types of firms.

The relationship is that as the logistics function
accumulates line responsibility, in general, and of customer
service and logistics systems planning in particular, perfor-
mance measurement of logistical activities are enhanced and
expanded. The actual explanation for why this occurs rests
in part on the notion of horizontal differentiation: that is,
the formation of distinct differentiated departments. Not all
of the activities comprising the "total number of activities"
scale are performed by all organizations. Consider the case
of the "logistics systems planning" activity. Some firms may
simply not perform the logistics systems planning activity.
This activity, if performed, would be conducted by role
specialists examining the logistics "system." The actual
designing of systems requires, among others, highly trained
engineers, management scientists and efficiency experts. Once
a system is designed, then logistics systems planning
specialists might "demand" a performance measurement system
to monitor inherent input-output conversion processes. Not
only would this serve the purpose of controlling and
monitoring operations, but the information would be used to
guide the development and design of new systems.

The relationship between span of logistics control and
performance measurement does not rest on the simple argument
of "more is better." Much the same can be said for specific
activities such as customer service and logistics systems

planning. Some activities not controlled by logistics might

140
not performed in the organization” This could be the case for
a number of the activities examined in the dissertation
including facilities design, logistics system planning and
logistical administration. Increasing span of logistics
control could be indicative of both increasing horizontal
differentiation and increasing consolidation of logistics

activities in the logistics function.

TITLE LEVEL OF THE SENIOR LOGISTICS EXECUTIVE

The last.pictorial configuration variable examined is the
title level of the senior’ logistics executive. II: was
hypothesized that an increasing title level would be
positively related to performance measurement (H9). At the
overall level, the data contradicts this hypothesis. As the
senior logistics.executive title level increases, internal and
benchmarking'differentiationgperformance‘measurement.actually
decline, but the remaining types of performance measurement
are unaffected.

However, at the subgroup level, fourteen of eighteen
correlations between title level and performance measurement
are positive and four of these are significant" This suggests
that a weak positive relationship exists between title level
and performance measurement in the subgroups.

The senior executive need not be visible to enhance
performance measurement. Conversely, a senior logistics
executive not having high visibility is not a deterrent to

organizational benchmarking of logistics. The notion of high

141
"visibility" as a requirement of strategic use of a function
is not supported. In conclusion, benchmarking, being an
indicator of environmental scanning and hence a strategic
logistics approach, is not enhanced as the title level of the
senior logistics executive increases.

In summary, a total of four pictorial configuration
variables were examined. The three variables reflecting
activity allocation strongly influence performance
measurement. The activities that are allotted to logistics
affects the extent to which logistical performance is
measured. The title level of the senior logistics executive
is related to performance measurement. But the pattern of
relationships that do exist fail to provide strong evidence
of the senior executive influencing performance measurement

by the position they occupy.

IMPLICATIONS FOR LEADING EDGE LOGISTICS

In the Bowersox s; s1. (1989) study, the leading edge
logistics concept was introduced. Organizations on the
"leading edge" were positioned to use logistical competency
strategically. They achieve competitive superiority through
logistical competency, by adding value cost effectively and
by forging strategic. alliances ‘with 'third. party service
providers. A critical component of leading edge logistics is
that organizations use logistics to stimulate demand. In

other words, leading edge organizations use logistics to

142
become preferred suppliers.

A common attributes index (CAI) was developed
distinguishing leading edge from other less advanced
organizations. A delphi panel consisting of industry
executives was convened, and fifteen attributes indicative of
a leading edge philosophy were selected. The panel then
assigned a weight to each attribute. In the subsequent
empirical study, responding firms were given a CAI score out
of one hundred indicating the level of logistical excellence.

Three major attributes characterize leading edge
organizations. The first is formalization. Leading edge
firms have a logistics mission statement, a logistics
strategic plan, frequently update their logistics strategic
plan, have more mature logistics functions, a larger span of
logistics control, frequent reorganizations of logistics, a
higher title level for the senior logistics executive, and
participation in strategic planning by the senior logistics
executive.

The second attribute is flexibility at accommodating
various non-routine events. This flexibility is achieved
through the strategic use Of technology and through strategic
alliances with service suppliers.

The last attribute is measurement in the form of
monitoring internal performance and competitive benchmarking.

The dissertation examined how two of the three attributes
of leading edge logistics are interrelated: formalization and

measurement. The results detail how formalization and

143
measurement are related and how these relationships differ
across channel positions.

A major finding in the dissertation is that the
relatedness of logistics strategy and structure to performance
measurement do not differ across manufacturers, wholesalers
and retailers. This provides additional evidence in support
of the notion that leading edge attributes and.best logistical
practices are nearly identical regardless of where in the
channel an organization operates (Bowersox and Droge 1989).

The significant relationships between formalization and
performance measurement that were isolated provide insight
into which specific elements of formalization are more
important than others in terms of measurement practices.

For example, the frequency of updating the logistics
strategic plan and the number of reorganizations during the
past five years are unrelated to performance measurement.
These variables are of lessor importance in relation to
measurement practices, but this does not mean that they are
unimportant. These two variables are perhaps more indicative
of' organizational flexibility. A. large number' of
reorganizations, for example, demonstrates that the firm is
willing and capable of altering the structure of logistics in
response to changing markets requirements. The structure of
logistics is flexible and management is constantly willing to
adjust to accommodate changing customer requirements. Thus
the number of reorganizations, while unrelated to the

measurement attribute of leading edge logistics, might play

144
an important role in the flexibility attribute» Much the same
can be said for the frequency of updating the logistics
strategic plan. The organization that frequently updates its
logistics strategic plan could be flexible and less rigid.
Thus frequent updating may be more related to the flexibility
attribute than to the measurement attribute.

The dissertation identified a set of variables that were
highly related to the measurement attribute: a formal
logistics mission statement, a formal logistics strategic
plan, whether logistics controls customer service, whether
logistics controls logistics systems planning, and the span
of logistics control. These variables are highly related to
the measurement attribute, not only for all organizations
together, but also for the manufacturing, wholesaling and
retailing subgroups.

The activities comprising the span of logistics control
such as logistics systems planning, facilities design, and
logistical administration play a critical role in the
development of measurement systems. These differentiated
activities are more likely to employ role specialists who
monitor performance and costs, design various systems, signal
upper management when problems arise and identify specific
positional advantages through benchmarking.

A logistics mission statement and a logistics strategic
plan are also important since they are under a high degree of
management control. A mission statement is used to identify

key performance criteria and performance standards, and to

145
signal employees and customers as to what to expect from
logistics. A logistics strategic plan can be used in
conjunction with competitive benchmarking. If benchmarking
does identify a positional advantage in a particular area,
then the information can be incorporated into the logistics
strategic plan.

The variables just identified that are highly related to
the measurement attribute might be unrelated to the
flexibility attribute. This is contrasted with the variables
that are unrelated to the measurement attribute, such as
reorganizations, which might be highly related to the
flexibility attribute.

A third set of variables were identified in this
research. They are sporadically related to performance
measurement and include the length of time that logistics has
been a formal function, participation in business unit
strategic planning by the senior logistics executive and the
title level of the senior logistics executive. These
variables were found to be sporadically related to performance
measurement depending on the particular type of measurement
being examined and the particular channel position at which
the organization Operated.

This leads to an interesting conjecture about the
variables that are sporadically related to performance
measurement, especially the two variables describing the
"position" of the senior logistics executive. A creative

tension may exist in organizations between the measurement and

146

flexibility attributes and. between. the formalization and
measurement attributes. On the one hand, organizations need
to implement bureaucratic rules, plans, policies and control
measures. The measurement attribute and certain elements of
the formalization attribute of leading edge organizations are
integrative, create jpredictable internal reporting
relationships, create expected rules of behavior and lower
costs.

(hi the other hand, organizations require flexibility.
Technology, regulation. and (customer' requirements. are
constantly changing. But flexibility requires fluid internal
rules, fluid internal reporting' relationships, fluid
behavioral expectations and.a fluid logistics structure. 'Fhus
the need for predictability can conflict with the need for
flexibility. It is at this confluence that creative tension
results in leading edge logistics. Leading edge logistics
does not require a trade-off between flexibility and
predictability. Rather it requires the simultaneous
enhancement of both.

Leading edge logistics requires senior executives to
formalize an organization capability that both is flexible and
predictable. Management is positioned at the confluence of
flexibility and predictability where contradictory demands
between these two "forces" are the greatest. Elements Of the
formalization attribute that are highly related to the
measurement attribute could be unrelated to the flexibility

attribute. Other elements of formalization that are unrelated

147

to the measurement attribute could be highly related to the
flexibility attribute. The variables describing the position
of the senior logistics executive are sporadically related to
the measurement attribute and could also be sporadically
related to the flexibility attribute. The senior logistics
executive can be viewed as existing in the interior portion
of a triangle bounded by the measurement, flexibility and
formalization attributes.

In summary, leading edge logistics involves three
attributes: formalization, flexibility and measurement. This
research has examined how the formalization and measurement
attributes of leading edge logistics are related" The
relatedness of variables when viewed from the perspective of
these three attributes demonstrates that not all elements of
formalization impact measurement. Rather, those elements of
formalization unrelated to measurement may actually be
important toward enhancing logistical flexibility. Management
is charged with enhancing flexibility, measurement and
formalization. Given that flexibility and formalization are
traded-off in most organizations, rather than simultaneously
enhanced, it is not surprising that leading edge logistics

requires a long-term management commitment.

SUGGESTIONS FOR FUTURE RESEARCH

The dissertation suggests two distinct streams of future

research. They are: (1) future research from further

148
examination of the dissertation data; and (2) future research

from a new study. Each is discussed separately.

RESEARCH FROM DISSERTATION DATA SET

The questionnaires shown in Appendices A, B and C contain
variables over and above those examined in the dissertation.

The first variable that might be related to performance
measurement is size. Size can be operationalized as the
natural logarithm of annual sales. Larger organizations have
been found to use more formal controls (Khandwella 1974).
Future research could concentrate on ‘whether size is a
significant covariate. For example, both size and a mission
statement could be positively related to internal performance
measurement. The exact nature Of this relationship could be
determined by an analysis of covariance. It may be that the
strength of the relationship between size and internal
performance measurement is the same for organizations with
versus without a mission statement. Organizations with a
formal mission might consistently measure more elements of
internal performance, regardless of size.

Another construct that should be examined is technology
including the total number of internal computer applications
(Appendix B, question 13), the total number of electronic data
interchange linkages (Appendix B, question 14) and use of
automated product handling equipment (Appendix B, question 14,
items 5 and 6). Data on similar scales were also collected

in the wholesalers/retailer study.

149

The performance measurement variables plus other
structure variables should be examined simultaneously with
size and technology. Size and technology, according to
contingency theory, are elements of an organizations
environment. The structure variables that are available in
the data set can be split into: (1) those that describe the
structure of logistics such as span of logistics control, the
use of performance measures and formalization in the form of
a mission statement and strategic plan: and (2) those that
describe the "position” of the senior logistics executive such
as the title level of the senior logistics executive,
participation by the senior logistics executive in business
units strategic planning, and the tenure of the senior
executive in the current position.

A simultaneous examination of these variables could
provide insight into how logistics is organized, This
approach might identify whether size has a direct impact on
structure or whether technology intervenes the relationship
between size and structure. Consider the following situation.
Sales and internal performance measurement are highly
correlated. and. internal computerization. and. internal
performance measurement are highly correlated, but sales and
the technology variables are not. This pattern would support
the notion that size has a direct impact on structure. If
technology intervened the relationship between size and
structure, then the following pattern might exist. Size and

internal computerization would be highly correlated, internal

150
computerization and internal performance measurement would be
highly correlated, but size and internal performance
measurement would be uncorrelated.

The same issues should be examined for the "position" of
the senior logistics executives Is the position of the senior
logistics executive affected by size and/or technology? If
so, does size have a direct effect, or does technology
intervene the relationship between size and "position"? It
could be that the "position” of the senior logistics executive
is independent of size and technology and that these variables
are related only to other variables describing the structure
of logistics such as internal performance measurement and
formalization in the form of a logistics mission statement and
a logistics strategic plan.

A simultaneous approach.might also provide insights into
how the "position" variables are interrelated.) Is title level
correlated with participation in business unit strategic
planning? Is the tenure of the senior logistics executive
related to participation, formalization, or both?

The opportunity exists to explore these relationships for
all organizations together or to explore these relationships
for manufacturers, wholesalers and retailers separately.

Some of the literature suggests that various
relationships are nonlinear. For example, Argarwal (1979)
found a nonlinear relationship between size and structure.
One way to explore nonlinearities is to split the sample into

large and small firms and to examine correlations across

151

groups. For example, the retailers could be split into large
and small firms, and the correlation between size and, for
example, the span of logistics control could be compared
across groups. If a difference exists in this correlation
across small versus large retailers, then a nonlinearity has
been identified. This might lead to insights concerning how
the organizational design of logistics differs across small
versus large organizations.

In summary, future research from existing data set should
focus on examining size, technology and structure
simultaneously. The structure variables should include
internal performance measurement, benchmarking, formalization
in the form of a logistics mission statement and a logistics
strategic plan, and variables describing the "position" of the
senior logistics executive. The relationships among these
variables could be examined separately for manufacturers,
wholesalers and retailers. Additionally, nonlinearities could

be examined by comparing large versus small organizations.

RESEARCH FROM A NEW STUDY

A second stream of research questions can be considered
if a new study is undertaken. The major constructs that
should be examined are environment, strategy, structure and
possibly performance. First, specific operationalizations of
these variables are discussed. Then potential relationships
are presented.

Environment can explored through size and technology.

152

Size variables that should be collected include annual sales
and the number of employees. Other indicators of size that
are important to the organizational design of logistics
include the number of stock keeping units, the number of
warehouses, and the number of retail outlets, if the
organization is a retailer. The contextual complexity of the
logistics system could also be explored by the number of
suppliers and the number of customers.

An important element of technology that should be
explored is the use of electronic data interchange (EDI)
linkages. This can be operationalized as the proportion of
outbound shipments from EDI linkages and the percent of
inbound purchases conducted using EDI.

Several elements of structure should be explored. First,
the number of layers in, or the vertical differentiation of,
organizations should be collected.

Second, the number of departments that exist should be
examined. The span of logistics control, while measuring the
consolidation of activities within the logistics function,
failed to fully indicate the level of horizontal
differentiation. Consider the case of the facilities design
activity. It might be more important that such an activity
is performed rather than whether logistics controls the
activity. Some organizations may simply not perform such an
activity while others do. The critical factor may be whether
the activity is conducted somewhere in the organization, not

whether it is conducted under the guidance of the logistics

153
function in particular.

A third structure variable that should be explored are
various spans of control. The number of subordinates
reporting directly to the senior logistics executive or the
CEO has important implications for how logistics.is organized.

A fourth structure variable that should be explored is
the vertical locus of decision-making authority. This
construct is not the same as the extent to which existing
logistical activities are consolidated in a single department,
which may or may not be related to the vertical locus of
decision-making authority; Centralization of decision-making
can be measured by the vertical level in the firm at which,
for example, employment, transportation carrier, vehicle
routing and scheduling, and technology deployment decisions
are made.

The next major construct that should be examined in
future research is strategy. In this case, a strategic
typology that is relevant to the unit of analysis should be
selected. For example, a strategic typology describing the
logistical posture of wholesalers can be examined. The
typology selected for wholesalers may not be the same as the
typology selected for manufacturers.

The last major construct that should be examined, and
certainly the most difficult to collect, is performance.
Market share and market share growth rate, return on
investment and inventory turnover are important descriptors

of organizational performance.

154

Future research based on a new study might explore how
these variables are related to one another. For example, does
increasing logistical technology lead to flattening of the
organization in conjunction with increasing spans of control?
This might especially be the case for wholesalers and
retailers where logistical technology forms the "core"
technology of the organization.

Another question is: does increasing technology promote
horizontal differentiation of logistical activities? Are
specialists hired and grouped together to create new
departments as logistical technology increases? Horizontal
differentiation has been found to be related to size (Argarwal
1979), but in the case of logistics, it could. be that
technology is a primary factor driving the process.

The relationship between centralization of decision-
making authority and technology can also be explored. Larger
organizations are generally more formalized and decentralized
(Khandwella 1974; Ford and Slocum 1977). It may be that
decentralization plays an important role in determining the
extent of the internal performance measurement system. The
more decentralized the organization, the greater the extent
of performance measurement. In addition, technology might
facilitate the centralization of certain policy decisions such
as fill rate and order cycle variance targets, and facilitate
the (decentralization of other' decisions such as “vehicle
routing and scheduling.

Another entire set of research questions can be generated

155

by considering performance. The inclusion of this construct
offers the potential to generate significant managerial
guidelines. It also presents the greatest difficulty from a
data collection perspective. Correlational evidence between
constructs could be explored. Internal performance
measurement could be positively related to actual return on
investment. But an analysis of "fit" might prove even more
enlightening. It could be that only under specific contexts
are performance measurement and profitability related. ‘Under
the context of "large and highly computerized," internal
performance measurement and profitability could be positively
related, but under the context of "large and less
computerized," internal performance measurement and
profitability could be inversely related. Why should such a
contingency relationship exist? A large less computerized
firm could potentially implement a internal logistical
performance measurement system only at great cost. The cost
of the system, without computers, would exceed the savings,
and profits would be pushed downward. In contrast, a large
highly computerized firm may be able to implement an internal
performance measurement system at low cost, and the increased
operational savings could exceed the associated costs and
hence lead to improved profitability.

In summary, a new study should examine a wide range of
organizational design variables associated with logistics.
The dissertation and other research out of the Bowersox: 3 s1.

(1989) data set has provided important insights about which

156
variables are worthy of further exploration. A new study
would build upon this research base and further describe how

organizations design logistical systems.

LIMITATIONS

The primary limitation of this research is related to the
sampling procedure. The majority of respondents included in
the first manufacturer's survey were members of a logistics
professional organization. As stated by Daugherty (1988, p.
166): "The firms selected were believed to be logistically
sensitive."

For the wholesaler-retailer study, the response rate was
low, which could be a function of the questionnaire length.
The questionnaire was twelve pages long and pretesting
demonstrated that it required about forty minutes to complete.
The sample.may'be.biased toward organizations or managers that
are favorable to or sensitive of logistics. Another
interpretation. is that logistics has a large impact on
profitability in those organizations that participated. In
general, the external validity of the sample can be questioned
and the following question can be asked. To what extent can
the findings be extended to the general population Of
manufacturers, wholesalers and retailers?

A second limitation of this research is that only one
element of logistics strategy was examined. Perhaps an

examination of generic strategies such as Porter's (1980) low

157
cost versus differentiation strategies would better serve the
purpose of examining how strategy and performance measurement
are related.

An important strength of many Of the scales used in the
dissertation is that they rely minimally on perceptions. For
example, performance measurement was operationalized as the
sum of a series of yes/no items. Certainly the problem of
bias toward organizations sensitive to logistics would be
greater if Likert or semantic differential scales were

employed.

SUMMARY OF DISSERTATION

This dissertation demonstrated that performance
measurement of logistical activities ‘varies with. a ‘wide
variety of organizational design variables. In addition, the
manner in which these organizational design variables are
related. to jperformance ‘measurement are similar for
manufacturers, wholesalers and retailers.

The organizational design variables can be grouped in
terms of their relationships with performance measurement.
The first group of variables consists of those having a more
or less consistent and positive effect on performance
measurement. They are: (1) the existence of a formal
logistics mission statement; (2) the existence of a formal
logistics strategic jplan: (3) whether logistics controls

customer service: (4) whether logistics controls logistics

158
systems planning; and (5) the number of line activities
reporting to the logistics function.

The second group of independent variables are those that
are sporadically related to different dimensions of
performance measurement for firms operating at different
channel positions. These variables are: (1) the length of
time that logistics has been a formal function: (2)
participation by the senior logistics executive in overall
business unit strategic planning: and (3) and the title level
of the senior logistics executive.

The third and last group of variables are those that are
basically unrelated to performance measurement. They are: (1)
the frequency Of updating the logistics strategic plan: and
(2) the number of times that the logistics function has been
reorganized during the past five years.

The dissertation examined empirically a number of
organizational design variables in a contingency theory
framework» Future research. endeavors should incorporate
elements of environment such as size, technology and
uncertainty, strategy, other elements of structure such as
horizontal and vertical differentiation and, most importantly,

performance.

APPENDIX A

MANUFACTURER QUESTIONNAIRE

159

 

 

Thank You

We Sincerely appreCIate your advance agreement to complete this questionnaire.
Your rapid response IS critical to completing this research and Will help develop
a better understanding of rapidly changing logistics organization.

 

Logistics
Organization and Strategy
Research

 

A third survey
of factors and trends
influencing logistics organizations

Conducted by Michigan State University
in cooperation with:

A. T. Kearney Management Consultants
Traffic Management Magazine
Gateway Systems Corporation
Council of Logistics Management
Canadian Association of PhySical Distribution Management

sponsored by:

Digital Equipment Corporation

 

 

160
MICHIGAN STATE UNIVERSITY
LOGISTICS ORGANIZATION AND STRATEGY RESEARCH PROJECT

This questionnaire is the third in a continumg effort to understand how logistics (also called physical
distribution or materials management) activities are organized. Your responses Will help develop a better
understanding of the rapidly Changing logistics organization.

DIRECTIONS

1. The overall questionnaire contains 25 questions. Pre-testing indicates it can be completed in
approximately 90 minutes.

2. Please answer only for your divi5ion or busmess unit. A busmess unit is typically a division or sub-
sidiary of a larger corporation. Copies of this questionnaire may be prOVided to other business units

in your corporation. If you are at corporate headquarters. please answer the questions from a cor-
porate perspective.

3. If a particular question does not apply, skip it and go on. Please return the questionnaire even if you
cannot complete it.

4. if you wish to expand on a question. use either blank space on the questionnaire or attach comments
on a separate sheet of paper.

5. Throughout this questionnaire the terms logistics. physical distribution and materials management
are used interchangeably. The Council of Logistics Management has defined logistics as a term
describing the integration of two or more activities for the purpose of planning. implementing and
controlling the efficient flow of raw materials. in-process inventory and finished goods from point of
origin to point of consumption.

6. Please return questionnaire as soon as possmle to:

Dr. Donald J. Bowersox

Professor of Marketing and Logistics
309 Eppley Center

Michigan State Universuty

East Lansmg, MI 48824

(517) 353-6381

161

. What were the annual dollar sales of your business unit (division or subsidiary) and the total corpora-
tion (if different) during the most recent fiscal year?

(A) Business Unit S

 

(B) Total Corporation :5

 

.What are the primary products manufactured by your business unit?

 

 

. In finished product logistics. do you use:
Yes No

(A) Dealers

(8) Distributors

.What percentage of your manufacturing is:
(A) To Customer Order __°/o

(8) Sold from inventory __°/o

. (A) What is the title of your senior logistics executive?

 

 

 

(B) How many years has this executive been in this position?

 

 

(C) What was the senior logistics executive's previous assignment?

 

 

. (A) What is the title of the person that senior logistics executive reports to?

 

(B) What was this executive’s previous assignment?

 

 

. 00 you have a formal logistics mission statement?

Yes _ No

If yes. please provide below or attach a separate sheet.

 

 

 

 

162

8.00 you have a formal logistics strategic plan?
(A) Yes No

(B) If yes. what is the time horizon of the plan?

 

 

(C) If yes. how often is your plan reviewed or updated?

 

 

9.00 logistics executives formally participate in overall Strategic planning for your business unit?

Yes No

10. (A)Within the last five years. how many times have all or parts of logistics been reorganized?

 

 

(B)What parts of logistics have been reorganized?

 

 

 

11. How many years has logistics been a formal organization?

 

12. In some companies the senior logistics executive manages activities that are not typically part of
a logistics organization. For example, some logistic organizations have responsibility for data pro-
cessing, real estate. dealer services. and/or facilities.

(A) Does your logistical organization have responsibility for any such non-typical activities?
Yes NO

(8) If yes. please list the responsibilities.

 

 

 

 

163

13. Please indicate which activities have been. are currently, or are expected to be part of the formal
responsibility of logistics. In addition, identify whether the logistics organization has line responsibility
or functions in an advisory role. Space is available to include additional activities not listed.

How Long Part Likely to be Nature of
of Logistics Added in Responsibility
Activity (years) Future
Yes NO Line Advisory

 

 

 

Sales Forecasting

 

 

Production Planning

 

 

Sourcing/Purchasing

 

 

Inbound Transportation

 

 

Raw Materials/Work
in Process inventory
Management

 

 

Finished Goods inventory
Management

 

 

Infra-Company Transportation

 

 

Finished Goods Field
Warehousing

 

 

Order Processing

 

 

Customer Service

 

 

Outbound Transportation

 

 

Logistics Systems Planning

 

 

Logistics Engineering

 

 

Logistics Administration

 

 

International Logistics

 

 

 

 

 

 

 

 

164

14. Five types of organizations are illustrated below. These organizations vary in the degree to which
logistics activities are dispersed or consolidated. Consider which chart is most similar to your organiza-
tion and respond to the question at the end of the charts.

Type A - Functional Organization:
Dispersed Responsibilities for Logistics Activities

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ORPORATE
MANAGEMENT
:‘NAF'BASSAIIEONJ ACCOUNT"IG/ MANUFACTURING! SALES!

SYSTEMS FINANCE OPERATIONS MARKETING
r----~ --------------- 4 --------------- o --------------- [ ———————— q
l — Systems — Qvoar — Production —- Sales Forecasting I
I Planning Drocossir'tg z"annvrtt: — Customer Service I
I — Logistics — Enginoornq — Finid Warehousing :
I Central — Sourc not — OuIDOund '
: Purtnasing ’ransoortation I
' _. inOoUnd —- Finished Goods I
I 'ransoortation Inventory I
I — Plant Ware Management I
I 1300st I
I -— lntracomoany :
I Transport .
I — Raw Materials! '
' Wmeww I
I unalmma Mumwmm I
I Aammm :

Type B - Functional Organization:
Consolidated Logistics Responsibilities
CORPORATE
MANAGEMENT
------------- '
MANAGEMENT
ACCOUNTING! MANUFACTURING/ SALES]
iNFORMATlON LOGISTICS
SYSTEMS FINANCE OPERATIONS MARKETING

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

(Mapr Logistics
Actmtiu)

--——---——-—-—-—»1

L------_------_--_--t.

r
I
I
I
I
I
I
I
I
I
I
I
I

Type C - Divisional Organization:

165

Logistics Functions Consolidated within Business Units

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CORPORATE
MANAGEMENT
BUSINESS BUSINESS BUSINESS
UNIT A UNIT 8 UNIT C
r- ....... ‘ r---------: :------ 1
5 : I I r I
I
I : I : I i I _. I I I I I
I
Marketing Manufacturing l LOGISTICS I Marketing Manufacturing LOGISTICS : Mediating Manufacturing I LOGISTICS I
:(Mwmm: (MaprLogistics I "mm:
' Amwmen . Aawmos I : NmWMN) '
L--- ----- I '---------J ----------
Type D - Divisionaiized Organization:
Centrally Consolidated Logistics Responsibilities
CORPORATE
MANAGEMENT
I 1
I
I I I '
I
l
I
BUSINESS BUSINESS BUSINESS |
UNIT A UNIT B UNIT C l LOGISTICS :
I i
g I
| I
g I
' I
lJ—I H—I . :
I
Manufacturing Sales Manufacturing Sales Manufacturing Sales : :
I (Major Logistics :
: Activities) .

Type E - Divisionalized Organization:
Corporate Staff Logistics Function with Line Logistics Functions
in the Business Units (essentially a combination of hype C and D)

I

 

 

 

 

 

I

 

 

CORPORATE
MANAGEMENT

 

 

I

 

 

 

 

 

 

 

BUSINESS BUSINESS
UNIT A UNIT 8
I I I l l
I I I
am Leo-Ilia : “M “m m :
Lino ' Lino '
I m ' m '

L------------—=

I
)-'
I
I

I

 

 

 

BUSINESS
UNIT C

 

 

I

1.1..

l
I
I
I
I
.I

 

5-----

i—

r-i isiili

 

‘P-——‘_

 

 

 

 

 

 

 

166

Although your situation may not exactly fit one of the five models, indicate which most closely resembles
your organization.

Model A __ Model 8 Model C
Model D __ Model E Other

If ”other" please explain or attach a separate organization chart.

 

 

 

 

15. Which of the following statements provides the most accurate description of the primary strategy
of your logistics operation? Please check most applicable.

Type A - Process
A process-based strategy is concerned with managing a broad group of logistics
activities as a value added chain. Emphasis is on achieving efficiency from managing

purchasing, manufacturing, scheduling, and physical distribution as an integrated
system.

Type B - Market
A market-based strategy is concerned with managing a limited group of logistics
activities for a multidivision single business unit or across multiple business units.
The logistics organization seeks to make joint product shipments to common
customers for different product groups and seeks to facilitate sales and logistical
coordination by a single order-invoice. Often the senior sales and logistics executives
report to the same manager.

Type C - Channel
A channel-based strategy is concerned with managing logistics activities performed
jointly with dealers and distributors. The strategic orientation places a great deal of
attention on external control. Significant amounts of finished inventories are typically
maintained forward or downstream in the distribution channel.

__ Type D - Other
If your strategy does not fit into one of the above. please describe it below.

 

 

 

167

16. Rank the following events in terms of your ability to accommodate. Use a five point scale where:

not applicable

current logistics system cannot handle

current logistics system has difficulty handling
current logistics system handles with few problems
current logistics system handles easily.

(flwa-‘I
II

Special customer service 1 2 3 4 5
requests
Sales and Marketing 1 2 3 4 5

incentive programs

Product Introduction 1 2 3 4 5
Product Phase Out 1 2 3 4 5
Disruption in supply 1 2 3 4 5
Computer breakdown 1 2 3 4 5
Unexpected production 1 2 3 4 5

schedule changes

Product recall 1 2 3 4 5

Customization of service 1 2 3 4 5
levels to specific markets
or customers

Product modification or 1 2 3 4 5
customization while in the
logistics system

168

17. Please rate each of the followmg in terms of their impact on your logistical operations. Use a five
point scale where:

1 = not applicable

2 = no impact

3 = slight impact

4 = high impact

5 = very high impact
Forecast accuracy 1 2 3 4 5
Computer support 1 2 3 4 5
Excessive end-of-month or 1 2 3 4 5

end-of-quarter surges

Communications with customers 1 2 3 4 5
Production planning and scheduling 1 2 3 4 5
Interface with manufacturing 1 2 3 4 5
Availability of trained logistics personnel 1 2 3 4 5
Vehicle routing and scheduling 1 2 3 4 5
Computer applications backlog 1 2 3 4 5
Transportation cost 1 2 3 4 5
Communication with material vendors 1 2 3 4 5
Communication with dealers. distributors. 1 2 3 4 5
or brokers
Warehouse productivity 1 2 3 4 5
Measurement tools and methods 1 2 3 4 5
Communication with internal non-logistics 1 2 3 4 5

organizational units

Timely information 1 2 3 4 5
Supplier performance 1 2 3 4 5
Communications with external logistical 1 2 3 4 5

service suppliers

Incompatibility of computer equipment 1 2 3 4 5
and/or software

169

18. Please list primary performance measurements that the senior logistics management uses to monitor
and identify problems.

A) Cost

 

 

 

 

 

8) Customer Service

 

 

 

 

 

C) Productivity

 

 

 

 

 

D) Quality

 

 

 

 

 

170

19. Below is a list of logistic computer applications. Please check the appropriate box for each application.

Will be Will be
revised installed
Currently in next in next

m m _3'12'1
Freight Audit and Payment
Purchasing
Sales Forecasting
MRP
DRP
Raw Material Inventory Control
Finished Goods Inventory Control
Warehousing
Order Processing
Order Entry
Vehicle Routing and Scheduling
Inbound Freight Consolidation
Outbound Freight Consolidation
Supporting Financials
Performance Measurements

Distribution Modeling

20. Are you currently using, or planning in the next three to five years to use EDI (Electronic Data
Interchange) in the following applications?

Application Current Planned

Raw Materials Vendors
Copackers/ Contractors
Public Warehouses
Carriers
Customers/Brokers

Other (please specify)

 

 

21.

22.

23.

171

Are you currently using knowledge-based expert systems (Artificial Intelligence) in logistics operations?
(A) Yes No

(B) If yes, please elaborate how you are using such systems?

 

 

(C) If no, do you have specific plans to install such a system within the next three years?

Yes No

Do you have the following information available, either from your own computer system or from car-
riers or other logistical service providers?

lei No
Freight tracking
Real-time freight rating systems
Accurate freight delivery time

Please indicate the characteristics of information used to manage logistics. Use a five point scale
where:

1 = Never

2 = Not usually

3 = Sometimes

4 = In most instances

5 = Always
Timely 1 2 3 4 5
Accurate 1 2 3 4 5
Readily Available 1 2 3 4 5
Formatted on an Exception Basis 1 2 3 4 5

Appropriately Formatted to Facilitate Use 1 2 3 4 5

172

24. What do you anticipate will happen in regard to your company's usage of outside service vendors
in the next three to five years? Rank the following on a scale of 1 to 5, where:

1 Will not use outside logistical services

2 Will use less than current levels

3 = Usage will remain constant

4 = Will use more outside logistical services

5 = Will use many more outside logistical services

Transportation 1 2 3 4 5
Warehousing 1 2 3 4 5
Order Entry and Processing 1 2 3 4 5
Inventory Management 1 2 3 4 5
Freight Audit and Payment 1 2 3 4 5

25. In addition to cost. which of the following factors influence the decision to use outside service vendors?
Please rank them on a scale of 1 to 5. where:

1 not applicable

2 not important

3 = neutral

4 = somewhat important
5 = very important

Deregulation 1 2 3 4 5
Services available 1 2 3 4 5
Quality of services 1 2 3 4 5
Data processing/ Communications services 1 2 3 4 5
Management quality 1 2 3 4 5
Customer attitudes 1 2 3 4 5

Other (please specify)

 

 

173

Please indicate below:

This information WlII be handled in a confidential manner.

Name:

 

Title:

 

Parent Company:

 

Divi5ion or Busmess Unit:

 

Street Address:

 

City: State/Province: __ Zip/Postal Code:

Telephone: ( )

 

Are you a member of the CounCIl of Logistics Management?

5 Yes 2 No

Are you a member of the Canadian Assomation of Phy5ical Distribution Management?

I Yes I No

Would you be Willing to partICIpate in a short telephone follow-up interVIew to this questionnaire?

C Yes I No

Thank You

APPENDIX B

UPDATE MANUFACTURER QUESTIONNAIRE

174

 

THANK YOU

Ue sincerely appreciate your help in filling out this questionnaire.
Your rapid response is critical to completing this research and will help
develop a better understanding of dynamic logistics organizations.

 

1988 Special Edition Manufacturer
Logistics Organization and
Strategy Research

 

' A fourth survey
of factors and trends
influencing logistics organizations

Conducted by Michigan State University
in cooperation with:
A.T. Kearney, inc.
Traffic Management Magazine

Council of Logistics Management
Canadian Association of Physical Distribution Management

sponsored by:

Digital Equipment Corporation

 

1.7.5

MICHIGAN STATE UNIVERSITY
LOGISTICS ORGANIZATION AND RESEARCH PROJECT

This questionnaire is part of a continuing effort to understand how logistics activities are organized. qur DaFtICIDatlon

will help develop a better understanding of rapidly changing logistic organizations. All responses will be handled with
strict confidence.

OBJECTIVE
This questionnaire is intended to extend the scope of the logistics organization research project you helped us with last

year. in order to examine major changes in your logistics operation since you completed the 1987 questionnaire, we are
asking you to conpnete a short follow°uo survey. He appreciate your assistance.

DIRECTIONS
I. The overall questionnaire contains 16 questions. it should take approximately 20 minutes to complete.
2. Please answer for your division or business unit. A business unit is defined as a division or SUDSIdIarv of a

larger corporation that is operated independently.

3. if a particular question does not apply to your situation, skip it and go on. Please return the questionnaire even
if you have not coirpleted all of the questions.

‘\

the answer "not applicable" (N.A.) should be used when specific activities, tasks or terms are not relevant to you.
Not applicable should not be confused with “never“ or "do not use“ which refer to activities, tasks or terms that
may be relevant to your business but are not used.

5. Please return the questionnaire as soon as possible to:

Dr. Donald J. Bowersox

Professor of Marketing and Logistics
311 Eppley Center

Michigan State University

East Lansing, MI 48824

(517) 353—6381

[EAPPRECIATE mun CCDPERATIOLI

TU

L4

176

what were the annual gross dollar sales of your business unit and total corporation (if different) during the most
recent fiscal year?

Business Unit S Total Corporation S

Are the logistics activities within your business unit:

[I [l [I I] i]
CONDIOTEIY somewhat combination somewhat completely
centralized centralized decentralized decentralized

Has the trend over the last three years been toward:
[1 centralization of logistics activities, [1 decentralization of logistics aCTTVITIeS. 0" I? ”0 change

The set of relationships defined by manufacturers, wholesalers. 83¢ retailers ‘5 referred ‘0 35 3 Tir'ei‘”9 5‘3"”el-
who is the most Influential renter of yOur channel in terms of coordinating logistical relationsnios:

(1 your business unit, or [I wholesaler [1 retailer
T] broker [1 other, please describe
I) manufacturer
within the last year, how many times have all or parts of logistics been reorganized?
Does your senior logistics executive formally participate in overall strategic planning for your DUSIness unit?
[1 Yes i] No, but provides input through other executives.
[J No, provides no input.

Benchmarking is a management process used to monitor and measure performance against competitors. Please indicate
whether you use competitive benchmarking in each of the following areas:

 

 

Area is; 52 Area 1_§ E2
Cost T) [1 Logistics strategy [1 i

Asset management [1 [1 Technology deployment U U
Customer service [1 (1 Transportation operations U U
Productivity l] l] warehouse operations [I [1

Quality [I [I Order procesSIng operations 0 U

177

Below is a list of logistics related performance measurements. Please indicate if you use these measures and if
Specific measurement information is available. Also, on a scale of t to 5, please indicate the importance or these
measurements in monitoring operations or identifying problems. (Circle the numer or N.A. if not applicaole.)

\O

00 you information 00" 63. x"?
Measurement Use? Available? 4 c- g \

m 5.0 Les 9.0 «“0 8° 4° 4‘9
COST (logistics cost only) 0° \
Total cost analysis [1 [1 [J [1 1 2 3 4 5 ILA.
Cost per wit [1 [1 (1 [1 I 2 3 4 5 ILA.
Cost as a percentage of sales [1 t] t] [J l 2 3 4 S N.A.
InbOInd freight costs [1 [1 [1 (I 1 2 3 4 5 N.A.
Outbound freight costs [1 [1 [1 [J l 2 3 4 5 NA.
warehouse costs [1 t] [1 [l 1 2 3 4 5 NA.
Adninistrative costs [1 [J [1 [1 T 2 3 4 5 NJ.
Order processing costs [1 (l I] i] 1 2 3 4 5 NA.
Direct labor costs [1 [1 [1 (J 1 2 3 4 5 N.A.
Corrparison of actual versus budget [1 [1 [J [1 1 2 3 4 5 N.A.
Cost trend analysis [1 [l [1 T] 1 2 3 4 5 NA.
Direct product profitability [1 t [1 [1 1 2 3 4 5 MA.
ASSET MANAGEMENT
Inventory turns [1 t] [1 I) I 2 3 4 S N.A.
Inventory carrying costs T] [J I] [I 1 2 3 4 S N.A.
Inventory levels, umber of days simply i) (i l] T] 1 2 3 4 5 N.A.
Obsolete inventory (1 [1 T] I] 1 2 3 4 5 NA.
Return on net assets T) T] [J i] l 2 3 4 5 N.A.
Return on investment [1 I] [l U 1 2 3 4 S N.A.
CUSTOIER SERVICE (to m customer)
Fill rate [1 l] l] T] T 2 3 4 S N.A.
Stockouts i) l] l] [1 I 2 3 4 S N.A.
Shipping errors [1 t] [1 [J 1 2 3 4 5 N.A.
On time delivery (I (J l] t] I 2 3 i. s N.A.
Backorders l) T] (l T) 1 2 3 4 S N . A .
Cycle time i) l) [1 T] 1 2 3 4 S N A.
Customer feecback u [1 t] t] 1 2 3 4 S N.A.
Sales force feedback (1 i] l] [1 I 2 3 4 S N.A.
Customer surveys n [1 (J [1 l 2 3 4 S N.A.
PRCDUCTIVITY
Units shipped per eiiployee i] i] l] l] 1 2 3 4 5 salt.
Units per labor dollar [1 [1 I] [J I 2 3 4 5 NA.
Orders per sales representative [1 [1 [1 [l 1 2 3 4 S fut.
Coriparison to historical standards (1 I] T] [J 1 2 3 4 5 NA.
Goal program I] I] I] [l I 2 3 4 5 NA.
Productivity index i) I] [1 [l I 2 3 4 S ii.A.
QUALITY
Premency of dense I) u l] t) I 2 3 I. s N.A.
Dollar anoint of damage T) [l I] [1 I 2 3 4 5 ".4-
liuflaer of credit claims i] l] I] [l 1 2 3 4 5 NJ‘-
liuiber of customer returns (1 [l l] [I 1 2 3 4 S N.A.
Cost of returned goods i] i] T] I] I 2 3 4 5 N-A-
Other, please specify [I T) (I [1 I 2 3 4 5 N A

 

T) T] t) T] 1 2 3 4 S ii.A.

 

178

Point of sale (UPC) scanning has given retailers a unique advantage over other «enters of the distribution channel.

(I [l [J l] l]
Strongly Disagree Disagree Neutral Agree STTOOQIY Agree

Information derived from point of sale scanning has made your business unit concenitively stronger in comparison to
other makers of your distribution channel.

[1 i] [I [l l]
Strongly Disagree Disagree Neutral Agree Strongly Agree

Point of sale scanning information is more important to retailers than wholesalers.

[1 l] [J [l T]
Strongly Disagree Disagree Neutral Agree Strongly Agree
How do your company's logistics management information systems couture overall to the management information systems
designed to support other areas of the business? (I.e. Accounting, Finance, Sales, etc.)

[1 [l l) [T [1

much worse worse same better much better

what do you anticipate will happen to your conpeny's usage of outside service vendors in the next three years?
Please rate the following on a scale of f to 5. (Circle the rurber or N.A. if not applicable.)

 

.5? .t’ if .3? 4? a?
a," (3 #0! es ‘ ‘0 6‘ (5 38‘ 0‘0
go ‘9 ;‘ a: (T g S; 5' 3' 4; .t?
a‘ o \
5" 3 8' * 6‘ 3’ I" 9" z? (5?
g s 0 g, s Q ‘

. . 9 ~ e «3 e " e «1' ~ 3 ‘
OutSIde Service Vendor 3 d) 3 6’ Qt, o $ 6) £- 6) *0“
Transportation 1 2 3 4 S N.A.
warehousing f 2 3 4 5 N.A.

Order Entry and Processing 1 2 3 4 S N.A.
Inventory Management 1 2 3 4 S N.A.
Freight Audit and Payment 1 2 3 4 S N.A.
Consolidators t Z 3 4 5 N.A.
international Freight Forwarders T 2 3 4 5 N.A.
Domestic Freight Forwarders 1 Z 3 4 S N.A.
Order Fulfillment and Support 1 2 3 4 5 N.A.

Other, please describe 1 2 3 4 S N.A.

179

Below is a list of logistics computer applications. Please check the appropriate box for each application.

Currently

 

Currently installed Not currently Not currently
LOQISTICS installed but will be installed but installed and
CW!" and no plans revised in will be in no plans
Application for revision Qext 3 yrs next 3 yrs W
Freight Audit and Payment [1 l] I] [1
Purchasing t] [l i] [1
Sales Forecasting [1 l] i] (1
Raw Material inventory Control [1 I] [I U
in Process inventory Control [1 U U U
Finished Goods inventory Control [1 U U U
warehouse Order Selection l] U U U
warehouse On-Line Receiving l I] U I]
warehouse Merchandise Locator T] l] l] i]
warehouse workload Balancing l] l] U U
warehouse Short interval Scheduling [i T] [J {I
Order Processing [1 I] l) [1
Order Entry (1 [l l] [1
Vehicle Routing and Scheduling [] [i l] U
inbound Freight Consolidation [i U I] [1
Outbound Freight Consolidation T] U l] l)
Supporting Financials [1 [J U U
Performance Measurements (I l] U U
Distribution Modeling [1 l] [1 [1
Direct Product Profitability [1 U U U
Direct Store Delivery [1 l] i] U
Shelf Management i] l] l] U
MRP T) l] l] U
DRP i] U [i [1
Other [1 l] T] l]

 

Please indicate whether you are currently using, or planning to use Electronic Data interchange (EDI) to facilitate
order, financial or other cormuiications with the following?

Plan to
install
Currently in next No plans

Entity installed 3 years ml; [QM
Manufacturers [1 l] U T]
wholesalers U l] U U
Public warehouses l] i] i] [J
Carriers U T] U (1
Financial institutions [1 l] i] [1
Retailers (1 i] i) [1
Customers l] i] i] l)

Copackers/Contractors l] l] l] U

180

Are financial and personnel resources available to install and implement suitable lOQiStiCS computer applicatiOOS7

(1 Yes i] No

Listed below are several technologies. with respect to your business unit, please check each one that is:

Technology

Bar codes

Optical Scanning

Robotics

Artificial intelligence/Knowledge Based Systems
Automated Storage and Retrieval Systems
Automated Material Handling Equipment

Local Area Networks

Consumer-aided warehouse design

Handheld Data Entry Devices

Electronic Order Transmission

On Board Couputers~Delivery Vehicles

On Board Computers-Lift Trucks

Voice Data Capture

iBM PC or PC Xi Caipatible

80286 Microcomputers (iBM A1 or Compatible)
80386 Microcomputers

CD-ROM

wORM (write once, read many) discs
68020-Based Microcomputers (Macintosh 2 or Sm)
Fiber Optics

Other, please describe

 

 

 

Currently
used in
logistics

[l
U
[1

Currently
used in
fire but not

in logistics

U
I]
[1

[l
[l
(l
[1

Planned to
be installed
in logistics
next 3 yrs

[1

[i

[i

U

U

(i

l]

i)

[i

[i

l]
[l
[J
l)
[l
[l
(1

Not planned to
be installed

in logistics
U

Have not
gauged

181

Please indicate below:

This information will be handled in a confidential manner.

 

Title:

 

Parent Company:

 

Division or Business Unit:

 

Street Address:

 

City: State: Zip:

Telephone:( )

 

Are you a member of the Council of Logistics Management?

(1 Yes [i No

Are you a member of the Canadian Association of Physical Distribution Managewent?

[I Yes (I No

would you be willing to participate in a short telephone follow-up interview to this questionnaire?

[1 Yes [1 Mo

THANK YOU

APPENDIX C

WHOLESALER- RETAILER QUESTIONNAIRE

182

 

Thank You

We sincerely appreciate your help in filling out this questionnaire.
Your rapid response is critical to completing this research and will help
develop a better understanding of dynamic logistics organizations.

 

1988 Retail/Wholesale
Logistics Organization and
Strategy Research

 

A fourth survey
of factors and trends
influencing logistics organizations

Conducted by Michigan State University
in cooperation with:

A.T. Kearney. lnc.
Traffic Management Magazine
Council of Logistics Management
Canadian Association of Physical Distribution Management

sponsored by:
Digital Equipment Corporation

 

183

MICHIGAN STATE UNIVERSITY
LOGISTICS ORGANIZATION AND RESEARCH PROJECT

This questionnaire is part of a continuing effort to understand how logistics activities are organized. Your participation

will help develop a better understanding of rapidly changing logistic organizations. All responses will be handled WlIh
strict confidence.

SENIOR LOGISTICS EXECUTIVE RESPONSIBILITIES

This questionnaire concerns the scope of authority and resoonsibility of the Senior Logistics Executive in your organization.
Throughout this questionnaire the terms logistics. phySical distribution and materials management are used interchangeably.
Because organizations and titles vary extensively among manufacturers. wholesalers and retailers. further clarification is
appropriate. Logistics can be divided into two broad areas - Operations and Support. Typical Logistics Operations respon-
sibilities include but are not limited to warehousing. transportation. inventory and order processing. Logistics Support respon-
sibilities include such activities as faculty planning and design, industrial engineering. computer support and administration.
While the above list of logistical responsibilities is not exhaustive. the items reflect the type of duties that typically report
to the senior logistics executive. In selected wholesale and retail firms one could conclude that the total business is logistics.
Please distinguish between logistics as a total busmess and the scope of authority and responSibility of the senior executive
responsible for logistics operations and support.

DIRECTIONS

1. The overall questionnaire contains 36 questions. it should take approximately 40 minutes to complete.

2. Please answer for your division or business unit. A business unit is defined as a division or subsidiary of a larger
corporation that is operated independently.

3. If a particular question does not apply to your situation. skip it and go on. Please return the questionnaire even if
you have not completed all of the questions.

4. The answer “not applicable” (N.A.) should be used when specific activities. tasks or terms are not relevant to you.

Not applicable should not be confused with "never" or ”do not use" which refer to activities. tasks or terms that may
be relevant to your business but are not used.

5. Please return the questionnaire as soon as possible to:

Dr. Donald J. Bowersox

Professor of Marketing and Logistics
311 Eppley Center

Michigan State University

East Lansing. MI 48824

(517) 353-6381

We appreciate your cooperation

184

Is your company and/or business unit a:

B manufacturer Ci wholesaler [3 retailer '3 other. please describe

 

What were the annual gross doilar sales of your busmess unit and total corporation (if different) during the most
recent fiscal year?

Business Unit S Total Corporation 5

 

Check the one industry classification listed below that best describes products sold by
your business unit.

CI Building Materials. Hardware. and Garden Supply
C] General Merchandise

Ci Food

Ci Automotive

Ci Apparel and Accessory

[3 Furniture. Home Furnishings. and Equipment

I] Eating and Drinking Establishments

Ci Drugs. Health and Beauty Aids

C] Other. please describe

 

Please list your primary product.

 

Do you have a written logistics mission statement? Ci Yes D No

If yes. please prowde below or attach a separate sheet.

00 you have a written logistics strategic plan? - [:1 Yes [:1 No

if yes. what is the time horizon of your plan?

 

If yes. how often is your plan reviewed or updated?

 

Are the logistics activities within your business unit:

Ci [3 C] D C]
completely somewhat combination somewhat completely
centralized centralized decentralized decentralized

Has the trend over the last three years been toward:

C] centralization of logistics activities. D decentralization of logistics activities. or C] no change

The set of relationships defined by manufacturers. wholesalers. and retailers is referred to as a marketing chan-
nel. Who is the most influential member of your channel in terms of coordinating logistical relationships:

Ci your business unit. or C] wholesaler Ci retailer
Ci broker C] other, please describe
[3 manufacturer

 

12.

13.

15.

17.

18.

185

What is the title level of your most senior logistics executive? tSee age 1 for clarification.)

CJ Pre5ident D Vice Pre5ident I3 Supervisor
:1 Executive Vice PreSident C] Director [2 Manager
C] Senior Vice PreSident Ci Other. please describe

 

What is the functional title of your most senior logistics executive?

Ci Administration C] Logistics [3 Sales/Marketing

Ci CEO/COO C] Merchandising [_‘J Traffic/Transportation
Ci Data Processmg 8 Operations Cl Warehousmg

C Distribution Ci Purchasmg/Materials Management

if not listed above. please provide specific title

 

How many years has the most senior logistics executive been in this position?

 

What was the prayious title level of the person who is now your most senior logistics executive?

Ci President Ci Vice President D Supervisor
Ci Executive Vice President Ci Director Ci Manager
[3 Senior Vice Pre3ident Ci Other. please describe

 

What was the previous functional title of the person who is now your most senior logistics executive?

Ci Administration Ci Logistics Ci SaleslMarketing

i3 CEO/COO Ci Merchandising l3 Traffic/Transportation
CI Data Processmg [1'] Operations Ci Warehousing

Ci Distribution Ci Purchasing/Materials Management

If not listed above. please provide specific title

 

What is the title level of the person the most senior logistics executive reports to?

Ci Chairman D Senior Vice President [3 Manager
Ci President [3 Vice President Ci Supervisor
Ci Executive Vice President Ci Director Ci Other. please describe

What is the functional title of the person your most senior logistics executive reports to?

CI Administration Ci Logistics . l3 Sales/Marketing

[Ii CEO/COO C] Merchandising Ci Traffic/Transportation
Ci Data Processing Ci Operations C] Warehousing

Ci Distribution Ci Purchasing/Materials Management

If not listed above. please provide specific title

 

How many years has this executive been in this position?

How many years has logistics been a formal organization?

Within the last five years. how many times have all or parts of logistics been reorganized?

Does your senior logistics executive formally participate in overall strategic planning for your business unit?

Ci Yes D No. but provides input through other executives.
Ci No. provides no input.

186

 

 

 

 

 

 

 

 

 

 

 

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20.

21.

22.

187

If the following activities are currently part of the formal responsmiiity of logisncs. please indicate how long they
have been part of logistics. and the nature of responsrbility iline cr s'aff). If the actiwties are not part of logistics.
please indicate whether they are likely to be added in the future.

 

Likely to

How long part Nature of be Added

of Logistics Responsibility in Future
Actiwty (in year5) Line Staff Yes No
Sales Forecasting C.- D C [3
Purchasing C C] C] C]
Inbound Transportation [3 I: D E]
Inventory Management [3 D C] D
Intra-Company Transportation U D C] D
Warehousing D C] [3 C]
Order Processing :1 I] I: D
Customer Servrce B G C C]
Outbound Transportation C D D C]
Logistics Systems Planning D C] 3 CI
Facilities Design E] CI [3 C]
Materials Handling C] C] C} C]
Logistics Administration C] C] I3 C]
International [3 Ci [:1 [3
Capital Equipment Procurement C3 3 [3 CI
Data Processing for Distribution Applications C] S D C]

 

 

In some companies the senior logistics executive manages activities that are not typically part of a logistics
organization. For example. some logistics organizations have responsibility for real estate and facilities. (These
are activities not listed in question 20.)
Does your logistical organization have responsibility for any such non-typical activities? 0 Yes D No
If yes. please list the responsibilities.
Below is a list of logistics related performance measurements. Please indicate if you use these measures and it
specrfic measurement information is available. Also. on a scale of t to 5. please indicate the importance of these
measurements in monitoring operations or identifying problems. (Circle the number or N.A. if not applicable.)
Do you Information «9‘ \ Q\e
Measurement Use? Available? 0“ '2? . (:0
—--— _— _—_.. Q 6 \\
Yes No Yes No .6‘ q° ox QQ
ASSET MANAGEMENT —— —— Qs \6‘ e v-
lnventory turns C] CI C] C] 1 2 3 4 5 N.A.
Inventory carrying costs Cl C] C] C] 1 2 3 4 5 N.A.
Inventory levels. number of days supply [3 C] C] D 1 2 3 4 5 N.A.
Obsolete inventory D C] C] CI 1 2 3 4 5 N.A.
Return on net assets [3 I] C] CI 1 2 3 4 5 N.A.
Return on investment CI [3 C] III 1 2 3 4 5 N.A.

Please continue questlon 22 on next page.

188

Do you Information
Measurement Use? Available?

.<
(D
M
Z
0

COST (logistics cost only)

Total cost analy3is

Cost per unit

Cost as a percentage of sales
Inbound freight costs
Outbound freight costs
Warehouse costs
Administrative costs

Order processmg costs

Direct labor costs
Comparison of actual versus budget
Cost trend analy5is

Direct product profitability

CUSTOMER SERVICE (to your customer)
Fill rate

Stockouts

Shipping errors

On time delivery

Backorders

Cycle time

Customer feedback

Sales force feedback

Customer surveys

PRODUCTIVITY

Units shipped per employee

Units per labor doilar

Orders per sales representative
Comparison to historical standards

Goal programs
Productivity index

QUALITY
Frequency of damage
Dollar amount of damage
Number of credit claims
Number of customer returns
Cost of returned goods
Other. please specify

 

 

 

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189

23. Benchmarking is a management process used to monitor and measure performance against competitors. Please
indicate whether you use competitive benchmarking in each of the ‘ollowmg areas:

Area

Cost

Asset management
Customer service
Productivity

Quality

.<
m
(I,
Z
0

Area Yes No

Logistics strategy [3 C]
Technology deployment B [3
Transportation operations [1 D
Warehouse operations D [3
Order processmg operations [I D

[It'll-JUL]
[ILHHH'I

24. On a scale of 1 to 5. rate the factors you use to evaluate suppliers. (Circle the number or N.A. if
not applicable.)

 

09 6° .°\e
Factor Used To 0‘ 0960 e“ 0966 64°39 00966 3‘9 09 $9ch6
Evaluate Supplier: $34 «5‘ {506‘ 016" P3" 99‘
On time delivery 1 2 3 4 5 N.A.
Percentage of complete orders 1 2 3 4 5 N.A.
Automatic substitution rates 1 2 3 4 5 N.A.
Customer support 1 2 3 4 5 N.A.
Easy to work with 1 2 3 4 5 N.A.
Maintains short order cycle 1 2 3 4 5 N.A.
Good communication 1 2 3 4 5 N.A.
Early notification of disruptions 1 2 3 4 5 N.A.
Flexibility 1 2 3 4 5 N.A.
Willing to customize service 1 2 3 4 5 N.A.
Management quality 1 2 3 4 5 N.A.
Service quality 1 2 3 4 5 N.A.
Positive attitude 1 2 3 4 5 N.A.
Master carton packaging quality 1 2 3 4 5 N.A.
Shelf unit packaging quality 1 2 3 4 5 N.A.
Price 1 2 3 4 5 N.A.
Consistency of Order Cycle 1 2 3 4 5 N.A.
Other. please describe 1 2 3 4 5 N.A.

 

25. Please indicate whether you are currently using. or planning to use Electronic Data Interchange (EDI) to facilitate
order. Manual or other communications with the followmg.

 

Plan to
install
Currently in next No plans
Entity installed 3 years to install Not applicable
Manufacturers C] U C] D
Wholesalers D C] D 0
Public Warehouses C] D D D
Carriers D D [3 CI
Financial Institutions [:1 Cl C] U
Retailers CI CI [3 CI
Customers C] C] C] C]
CopackersIContractors [3 CI C] D

I90

26. Below is a list of logistics computer applications. PIease check the appropriate box for each application.

27.

28.

30.

f

: ntly
Currently nSlalled Not currently Not Currently
Logistics installed and but WIN be installed but installed and
Computer no plans reVised in will be in no plans
Application for reViSion next 3 yrs next 3 yrs to install

 

Freight Audit and Payment
Purchasmg

Sales Forecasting

Inventory Control

Warehouse Order Selection
Warehouse OnoLine Receiving
Warehouse Merchandise Locator
Warehouse Workload Balancing
WarehOuse Short Interval Scheduling
Order Processing

Order Entry

Vehicle Routing and Scheduling
Inbound Freight ConsoIidation
Outbound Freight Consolidation
Supporting FinanCials
Performance Measurement
Distribution Modeling

Direct Product Profitability
Direct Store Delivery

Shelf Management

Other

 

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Point of sale (UPC) scanning has given retailers a unique advantage over other members of the distribution

channeL

D C]
Strongly Disagree Disagree

:1
Neutral

[3
Agree

[3

Strongly Agree

lnlormation derived from point of sale scanning has made your busmess unit competitively stronger in
comparison to other members of your distribution channel.

CI CI
Strongly Disagree Disagree

C]
Neutral

0
Agree

D

Strongly Agree

Point of sale scanning information is more important to retailers than wholesalers.

E] C]
Strongly Disagree Disagree

CI
Neutral

Cl
Agree

C]

Strongly Agree

How do your company's logistics management information systems compare overall to the management infor-
mation systems designed to support other areas of the business? (is. Accounting, Finance. Sales. etc.)

[3 [3

much worse worse

(3
same

[I]
better

C]

much better

31.

32.

191

On a scale of 1 to 5. please indicate the characteristics of information used to manage logistics.

e
'0\
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« s s6 s ‘8‘”
63’ ”be, 06‘ g? x49 0‘
Characteristic 15‘ Q‘ ‘9 O Y' Q

Timely 1 2 3 4 5 N.A.
Accurate 1 2 3 4 5 N.A.
Readily available 1 2 3 4 5 N.A.
Formatted on an exception basrs 1 2 3 4 5 N.A.
Appropriately formatted to Meditate use 1 2 3 4 5 N.A.

On a scale of 1 to 5. rate the followmg events in terms of your logistical organization‘s ability to accommodate.
(Circle the number or N.A. if not applicable.)

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69° 3 s: a?” a?
e to a,
08 “36$ «)0 sf $0 ca? #36
VS? 69b 6) ob «60 696 «:3b a? x9
C 6‘ G it Q
es 0 s 5 s s ”v ‘9

Event ,§ 6’ go ((0 c? o" (y
* o T x- ? v- e
Special customer service 1 2 3 4 5 N.A.

requests
Sales and Marketing 1 2 3 4 5 N A

incentive programs
Product introduction 1 2 3 4 5 N.A.
Product phase out 1 2 3 4 5 N.A.
Disruption in supply 1 2 3 4 5 N.A.
Computer breakdown 1 2 3 4 5 N.A.
Product recall 1 2 3 4 5 N.A.
Customization of service 1 2 3 4 5 N.A.

levels to speCific markets

or customers
Product modification or 1 2 3 4 5 N.A.

customization while in the
logistics system (9.9. pricing,
packaging. mixing)

Returned goods 1 2 3 4 5 N.A.

192

33. On a scale of 1 to 5. rate the level of impact each of the followmg have on your logistical operations. (Circle the
number or N.A. if not applicable.)

c}
6' 't’ o
0‘} 59% .5" \69 the
C} Q \ Q '9 \9
o 6‘ e 9 Q
Q \ 6 \ 1‘
e e 9 c *9

Item 0‘ \9 ob 9 Q o‘
—— e ‘0 ‘3‘ e A e
Sales forecast accuracy 1 2 3 4 5 N.A.
Computer support 1 2 3 4 5 NA
Excessive end-of-month or 1 2 3 4 5 NA

end-of-quarter Surges
Communications with customer 1 2 3 4 5 N.A.
Availability of trained

logistics personnel 1 2 3 4 5 N.A.
Vehicle routing and scheduling 1 2 3 4 5 N.A.
Computer applications backlog 1 2 3 4 5 NA.
Transportation cost 1 2 3 4 5 N.A.
Communication with suppliers 1 2 3 4 5 N.A.
Warehouse productivity 1 2 3 4 5 N.A.
Measurement tools and methods 1 2 3 4 5 N.A.
Communication with internal 1 2 3 4 5 N.A.

non-logistics organizational units
Timely information 1 2 3 4 5 N.A.
Supplier logistical performance 1 2 3 4 5 N.A.
Communication with external 1 2 3 4 5 N.A.

logistical service suppliers
Incompatibility of computer 1 2 3 4 5 N.A.

equipment and/or software
Inventory reduction programs 1 2 3 4 5 N.A.
Cost reduction programs 1 2 3 4 5 N.A.
Load leveling 1 2 3 4 5 N.A.
Workforce leveling 1 2 3 4 5 N.A.

193

Please rate the lollowmg on a scale of 1 to 5. (Circle the number or N A. if not applicable.)

OutSide Sewice VendOr

 

Transportation

Warehousing

Order Entry and Processing
Inventory Management

Freight Audit and Payment
Consolidators

International Freight Forwarders
Domestic Freight Forwarders
Order Fulfillment and Support

Other. please describe

 

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1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5

34. What do you antiCipate will happen to your company's usage of outsnde sewice vendors in the next three years?

N.A.

N.A.

N.A.

N.A.

N.A.

N.A.

N.A.

N.A.

Price and cost considerations being equal, which of the following factors influence the decision to use outSide

semce vendors? Please rate them on a scale of 1 to 5. (Circle the number or N.A. if not applicable.)

Factor

Services available
Quality of services

Data processing!
communications services

Management quality
Customer orientation
Vendor reputation

Other

 

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194

36. Listed below are several technologies. With respect to your busmess unit. please check each one that is:

 

Currently Planned to
Currently used in be installed Not planned to
used in firm but not in logistics be installed Have not
Technology logistics in logistics next 3 yrs in logistics evaluated
Bar codes

Optical Scanning

Robotics

Artificial Intelligence/Knowledge Based Systems
Automated Storage and Retrieval Systems
Automated Material Handling Equipment
Local Area Networks

Computer-aided warehouse design

Handheld Data Entry Dewces

Electronic Order Transmission

On Board Computers-Delivery Vehicles

On Board Computers-Lift Trucks

Voice Data Capture

IBM PC or PC XT Compatible

80286 Microcomputers (IBM AT or Compatible)
80386 Microcomputers

CDROM

WORM (write once. read many) discs

68020-Based Microcomputers (Macintosh 2
or Sun)

Fiber Optics
Other. please describe

 

 

 

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195

Please indicate below:

This information WI” be handled in a confidential manner.

Name:

 

Title:

 

Parent Company:

 

Division or Business Unit:

 

Street Address:

 

City: State/Provmce: __ Zip/Postal Code:

Telephone: ( )

 

Are you a member of the Council of Logistics Management?

D Yes C) No

Are you a member of the Canadian Assomation of PhySical Distribution Management?

I: Yes D No

Would you be willing to partiCipate in a snort telephone follow-up interView to this questionnaire?

CI- Yes 5 No

Thank You

 

APPENDIX D

STATISTICAL HYPOTHESES

APPENDIX D
STATISTICAL HYPOTHESES

Table 2

Ho: No difference exists among the manufacturer, wholesaler
and retailer means of internal cost performance measurement.

H1: No difference exists among the manufacturer, wholesaler
and retailer means of internal differentiation performance
measurement.

H2: No difference exists among the manufacturer, wholesaler
and retailer means of total internal performance measurement.

H3: No difference exists among the manufacturer, wholesaler
and retailer means of cost benchmarking performance
measurement.

H4: No difference exists among the manufacturer, wholesaler
and retailer means of differentiation benchmarking performance
measurement.

H5: No difference exists among the manufacturer, wholesaler

and retailer means of total benchmarking performance
measurement.

Table 3
H6: Total internal and total benchmarking performance
measurement are not related.

H7: Internal cost and internal differentiation performance
measurement are not related.

H8: Cost benchmarking and differentiation benchmarking
performance measurement are not related.

H9: Internal cost and cost benchmarking performance
measurement are not related.

196

197

H10: Internal cost and differentiation benchmarking
performance measurement are not related.

Table 4
H11: Total internal and total benchmarking performance
measurement are not related among manufacturers.

H12: Internal cost and internal differentiation performance
measurement are not related among manufacturers.

H13: Cost benchmarking and differentiation benchmarking
performance measurement are not related among manufacturers.

H14: Internal cost and cost benchmarking performance
measurement are not related among manufacturers.

H15: Internal cost and differentiation benchmarking
performance measurement are not related among manufacturers.

Table 5
H16: Total internal and total benchmarking performance
measurement are not related among wholesalers.

H17: Internal cost and internal differentiation performance
measurement are not related among wholesalers.

H18: Cost benchmarking and differentiation benchmarking
performance measurement are not related among wholesalers.

H19: Internal cost and cost benchmarking performance
measurement are not related among wholesalers.

H20: Internal cost and differentiation benchmarking
performance measurement are not related among wholesalers.

Table 6
H21: Total internal and total benchmarking performance
measurement are not related among retailers.

H22: Internal cost and internal differentiation performance
measurement are not related among retailers.

198

H23: Cost benchmarking and differentiation benchmarking
performance measurement are not related among retailers.

H24: Internal cost and cost benchmarking performance
measurement are not related among retailers.

H25: Internal cost and differentiation benchmarking
performance measurement are not related among retailers.

Table 7

H26: The frequency of strategic plan update main effect is not
a significant predictor of internal cost performance
measurement.

H27: The channel position main effect is not a significant
predictor of internal cost performance measurement.

H28: The interaction of frequency of strategic plan update and
channel position is not a significant predictor of internal
cost performance measurement.

H29: The frequency of strategic plan update main effect is not
a significant predictor of internal differentiation
performance measurement.

H30: The channel position main effect is not a significant
predictor of internal differentiation performance measurement.

H31: The interaction of frequency of strategic plan update and
channel position is not a significant predictor of internal
differentiation performance measurement.

H32: The frequency of strategic plan update main effect is not
a significant predictor of total internal performance
measurement.

H33: The channel position main effect is not a significant
predictor of total internal performance measurement.

H34: The interaction.of frequency of strategic plan update and
channel position is not a significant predictor of total
internal performance measurement.

H35: The frequency of strategic plan update main effect is not
a significant predictor of cost benchmarking performance
measurement.

H36: The channel position main effect is not a significant
predictor of cost benchmarking performance measurement.

199

H37: The interaction of frequency of strategic plan update and
channel position is not a significant predictor of cost
benchmarking performance measurement.

H38: The frequency of strategic plan update main effect is not
a significant predictor of differentiation benchmarking
performance measurement.

H39: The channel position main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

H40: The interaction of frequency of strategic plan update and
channel position is run: a significant predictor of
differentiation benchmarking performance measurement.

H41: The frequency of strategic plan update main effect is not
a significant predictor of total benchmarking performance
measurement.

H42: The channel position main effect is not a significant
predictor of total benchmarking performance measurement.

H43: The interaction of frequency of strategic plan update and
channel position is not a significant predictor of total
benchmarking performance measurement.

Table 8

H44: The mission statement main effect is not a significant
predictor of internal cost performance measurement.

H45: The channel position main effect is not a significant
predictor of internal cost performance measurement.

H46: The interaction of mission statement and channel position
is not a significant predictor of internal cost performance
measurement.

H47: The mission statement main effect is not a significant
predictor of internal differentiation performance measurement.

H48: The channel position main effect is not a significant
predictor of internal differentiation performance measurement.

H49: The interaction of mission statement and channel position
is not a significant predictor of internal differentiation
performance measurement.

H50: The mission statement main effect is not a significant
predictor of total internal performance measurement.

200

H51: The channel position main effect is not a significant
predictor of total internal performance measurement.

H52: The interacticwtof mission statement.and channel position
is not a significant predictor of total internal performance
measurement.

H53: The mission statement main efiect is not a significant
predictor of cost benchmarking performance measurement.

H54: The channel position main effect is not a significant
predictor of cost benchmarking performance measurement.

H55: The interaction of mission statement and channel position
is not a significant predictor of cost benchmarking
performance measurement.

H56: The mission statement main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

H57: The channel position main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

H58: The interaction of mission statement and channel position
is not a significant predictor of differentiation benchmarking
performance measurement.

H59: The mission statement main effect is not a significant
predictor of total benchmarking performance measurement.

H60: The channel position main effect is not a significant
predictor of total benchmarking performance measurement.

H61: The interaction of mission statement and channel position
is not a significant predictor of total benchmarking
performance measurement.

Table 9

H62: The strategic plan main effect is not a significant
predictor of internal cost performance measurement.

H63: The channel position main effect is not a significant
predictor of internal cost performance measurement.

H64: The interaction of strategic plan and channel position
is not a significant predictor of internal cost performance
measurement.

201

H65: The strategic plan main effect is not a significant
predictor of internal differentiation performance measurement.

H66: The channel position main effect is not a significant
predictor of internal differentiation performance measurement.

H67: The interaction of strategic plan and channel position
is not a significant predictor of internal differentiation
performance measurement.

H68: The strategic plan main effect is not a significant
predictor of total internal performance measurement.

H69: The channel position main effect is not a significant
predictor of total internal performance measurement.

H70: The interaction of strategic plan and channel position
is not a significant predictor of total internal performance
measurement.

H71: The strategic plan main effect is not a significant
predictor of cost benchmarking performance measurement.

H72: The channel position main effect is not a significant
predictor of cost benchmarking performance measurement.

H73: The interaction of strategic plan and channel position
is not a significant predictor of cost benchmarking
performance measurement.

H74: The strategic plan main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

H75: The channel position main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

H76: The interaction of strategic plan and channel position
is not a significant predictor of differentiation benchmarking
performance measurement.

H77: The strategic plan main effect is not a significant
predictor of total benchmarking performance measurement.

H78: The channel position main effect is not a significant
predictor of total benchmarking performance measurement.

H79: The interaction of strategic plan and channel position
is not a significant predictor of total benchmarking
performance measurement.

202

Table 10

H80: The number of years logistics has been a formal function
main effect is not a significant predictor of internal cost
performance measurement.

H81: The channel position main eff ct is not a significant
predictor of internal cost performance measurement.

H82: The interaction of number of years logistics has been a
formal function and channel position is not a significant
predictor of internal cost performance measurement.

H83: The number of years logistics has been a formal function
main effect is not a significant predictor of internal
differentiation performance measurement.

H84: The channel position main effect is not a significant
predictor of internal differentiation performance measurement.

H85: The interaction of number of years logistics has been a
formal function and channel position is not a significant
predictor of internal differentiation performance measurement.

H86: The number of years logistics has been a formal function
main effect is not a significant predictor of total internal
performance measurement.

H87: The channel position main effect is not a significant
predictor of total internal performance measurement.

H88: The interaction of number of years logistics has been a
formal function and channel position is not a significant
predictor of total internal performance measurement.

H89: The number of years logistics has been a formal function
main effect is not a significant predictor of cost
benchmarking performance measurement.

H90: The channel position main effect is not a significant
predictor of cost benchmarking performance measurement.

H91: The interaction of number of years logistics has been a
formal function and channel position is not a significant
predictor of cost benchmarking performance measurement.

H92: The number of years logistics has been a formal function
main effect is not a significant predictor of differentiation
benchmarking performance measurement.

H93: The channel position main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

203

H94: The interaction of number of years logistics has been a
formal function and channel position is not a significant
predictor of differentiation benchmarking performance
measurement.

H95: The number of years logistics has been a formal function
main effect is not a significant predictor of total
benchmarking performance measurement.

H96: The channel position main effect is not a significant
predictor of total benchmarking performance measurement.

H97: The interaction of number of years logistics has been a
formal function and channel position is not a significant
predictor of total benchmarking performance measurement.

Table 11

H98: The number of times logistics has been reorganized main
effect is not a significant predictor of internal cost
performance measurement.

H99: The channel position main effect is not a significant
predictor of internal cost performance measurement.

H100: The interaction of number of times logistics has been
reorganized and channel position is not a significant
predictor of internal cost performance measurement.

H101: The number of times logistics has been reorganized main
effect is not a significant predictor of internal
differentiation performance measurement.

H102: The channel position main effect is not a significant
predictor of internal differentiation performance measurement.

H103: The interaction of number of times logistics has been
reorganized and channel position is not a significant
predictor of internal differentiation performance measurement.

H104: The number of times logistics has been reorganized main
effect is not a significant predictor of total internal
performance measurement.

H105: The channel position main effect is not a significant
predictor of total internal performance measurement.

H106: The interaction of number of times logistics has been
reorganized and channel position is not a significant
predictor of total internal performance measurement.

204

H107: The number of times logistics has been reorganized main
effect is not a significant predictor of cost benchmarking
performance measurement.

H108: The channel position main effect is not a significant
predictor of cost benchmarking performance measurement.

H109: The interaction of number of times logistics has been
reorganized and channel position is not a significant
predictor of cost benchmarking performance measurement.

H110: The number of times logistics has been reorganized main
effect is not a significant predictor of differentiation
benchmarking performance measurement.

H111: The channel position main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

H112: The interaction of number of times logistics has been
reorganized and channel position is not a significant
predictor of differentiation benchmarking performance
measurement.

H113: The number of times logistics has been reorganized main
effect is not a significant predictor of total benchmarking
performance measurement.

H114: The channel position main effect is not a significant
predictor of total benchmarking performance measurement.

H115: The interaction of number of times logistics has been
reorganized and channel position is not a significant
predictor of total benchmarking performance measurement.

Table 12

H116: The participation in strategic planning by the senior
logistics executive main effect is not a significant predictor
of internal cost performance measurement.

H117: The channel position main effect is not a significant
predictor of internal cost performance measurement.

H118: The interaction of participation in strategic planning
by the senior logistics executive and channel position is not
a significant predictor of internal cost performance
measurement.

205

H119: The participation in strategic planning by the senior
logistics executive main effect is not a significant predictor
of internal differentiation performance measurement.

H120: The channel position main effect is not a significant
predictor of internal differentiation performance measurement.

H121: The interaction of participation in strategic planning
by the senior logistics executive and channel position is not
a significant predictor of internal differentiation
performance measurement.

H122: The participation in strategic planning by the senior
logistics executive main effect is not a significant predictor
of total internal performance measurement.

H123: The channel position main effect is not a significant
predictor of total internal performance measurement.

H124: The interaction of participation in strategic planning
by the senior logistics executive and channel position is not
a significant predictor of total internal performance
measurement.

H125: The participation in strategic planning by the senior
logistics executive main effect is not a significant predictor
of cost benchmarking performance measurement.

H126: The channel position main effect is not a significant
predictor of cost benchmarking performance measurement.

H127: The interaction of participation in strategic planning
by the senior logistics executive and channel position is not
a significant. predictor’ of cost. benchmarking' performance
measurement.

H128: The participation in strategic planning by the senior
logistics executive main effect is not a significant predictor
of differentiation benchmarking performance measurement.

H129: The channel position main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

H130: The interaction of participation in strategic planning
by the senior logistics executive and channel position is not
a significant predictor of differentiation benchmarking
performance measurement.

H131: The participation in strategic planning by the senior
logistics executive main effect is not a significant predictor
of total benchmarking performance measurement.

206

H132: The channel position main effect is not a significant
predictor of total benchmarking performance measurement.

H133: The interaction of participation in strategic planning
by the senior logistics executive and channel position is not
a significant predictor of total benchmarking performance
measurement.

Table 13

H134: The logistics control customer service main effect is
not a significant predictor of internal cost performance
measurement.

H135: The channel position main effect is not a significant
predictor of internal cost performance measurement.

H136: The interaction of logistics control customer service
and channel position. is not. a significant predictor of
internal cost performance measurement.

H137: The logistics control customer service main effect is
not a significant predictor of internal differentiation
performance measurement.

H138: The channel position main effect is not a significant
predictor of internal differentiation performance measurement.

H139: The interaction of logistics control customer service
and channel position. is not. a significant predictor of
internal differentiation performance measurement.

H140: The logistics control customer service main effect is
not a significant predictor of total internal performance
measurement.

H141: The channel position main effect is not a significant
predictor of total internal performance measurement.

H142: The interaction of logistics control customer service
and channel position is not a significant predictor of total
internal performance measurement.

H143: The logistics control customer service main effect is
not a significant predictor of cost benchmarking performance
measurement.

H144: The channel position main effect is not a significant
predictor of cost benchmarking performance measurement.

207

H145: The interaction of logistics control customer service
and channel position is not a significant predictor of cost
benchmarking performance measurement.

H146: The logistics control customer service main effect is
not a significant predictor of differentiation benchmarking
performance measurement.

H147: The channel position main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

H148: The interaction of logistics control customer service
and. channel position is not a significant predictor of
differentiation benchmarking performance measurement.

H149: The logistics control customer service main effect is
not a significant predictor of total benchmarking performance
measurement.

H150: The channel position main effect is not a significant
predictor of total benchmarking performance measurement.

H151: The interaction of logistics control customer service
and channel position is not a significant predictor of total
benchmarking performance measurement.

Table 14

H152: The logistics controls logistics systems planning main
effect is not a significant predictor of internal cost
performance measurement.

H153: The channel position main effect is not a significant
predictor of internal cost performance measurement.

H154: The interaction of logistics controls logistics systems
planning and channel position is not a significant predictor
of internal cost performance measurement.

H155: The logistics controls logistics systems planning main
effect is not a significant predictor of internal
differentiation performance measurement.

H156: The channel position main effect is not a significant
predictor of internal differentiation performance measurement.

H157: The interaction of logistics controls logistics systems
planning and channel position is not a significant predictor
of internal differentiation performance measurement.

208

H158: The logistics controls logistics systems planning main
effect is not a significant predictor of total internal
performance measurement.

H159: The channel position main effect is not a significant
predictor of total internal performance measurement.

H160: The interaction of logistics controls logistics systems
planning and channel position is not a significant predictor
of total internal performance measurement.

H161: The logistics controls logistics systems planning main
effect is not a significant predictor of cost benchmarking
performance measurement.

H162: The channel position main effect is not a significant
predictor of cost benchmarking performance measurement.

H163: The interaction of logistics controls logistics systems
planning and channel position is not a significant predictor
of cost benchmarking performance measurement.

H164: The logistics controls logistics systems planning main
effect is not a significant predictor of differentiation
benchmarking performance measurement.

H165: The channel position main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

H166: The interaction of logistics controls logistics systems
planning and channel position is not a significant predictor
of differentiation benchmarking performance measurement.

H167: The logistics controls logistics systems planning main
effect is not a significant predictor of total benchmarking
performance measurement.

H168: The channel position main effect is not a significant
predictor of total benchmarking performance measurement.

H169: The interaction of logistics controls logistics systems
planning and channel position is not a significant predictor
of total benchmarking performance measurement.

Table 15

H170: The number of activities controlled by the logistics
function. main effect is not a significant predictor of
internal cost performance measurement.

209

H171: The channel position main effect is not a significant
predictor of internal cost performance measurement.

H172: The interaction of number of activities controlled by
the logistics function and channel position is not a
significant predictor of internal cost performance
measurement.

H173: The number of activities controlled by the logistics
function. main effect is not. a significant predictor of
internal differentiation performance measurement.

H174: The channel position main effect is not a significant
predictor of internal differentiation performance measurement.

H175: The interaction of number of activities controlled by
the logistics function and channel position is not a
significant predictor of internal differentiation.performance
measurement.

H176: The number of activities controlled by the logistics
function main effect is not a significant predictor of total
internal performance measurement.

H177: The channel position main effect is not a significant
predictor of total internal performance measurement.

H178: The interaction of number of activities controlled by
the logistics function and channel position is not a
significant predictor of total internal performance
measurement.

H179: The number of activities controlled by the logistics
function main effect is not a significant predictor of cost
benchmarking performance measurement.

H180: The channel position main effect is not a significant
predictor of cost benchmarking performance measurement.

H181: The interaction of number of activities controlled by
the logistics function and channel position is not a
significant predictor of cost benchmarking performance
measurement.

H182: The number of activities controlled by the logistics
function ‘main effect is not a significant predictor of
differentiation benchmarking performance measurement.

H183: The channel position main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

210

H184: The interaction of number of activities controlled by
the logistics function and channel position is not a
significant predictor of differentiation benchmarking
performance measurement.

H185: The number of activities controlled by the logistics
function main effect is not a significant predictor of total
benchmarking performance measurement.

H186: The channel position main effect is not a significant
predictor of total benchmarking performance measurement.

H187: The interaction of number of activities controlled by
the logistics function and channel position is not a
significant predictor of total benchmarking performance
measurement.

Table 16

H188: The title level of the senior logistics executive main
effect is not a significant predictor of internal cost
performance measurement.

H189: The channel position main effect is not a significant
predictor of internal cost performance measurement.

H190: The interaction of title level of the senior logistics
executive and channel position is not a significant predictor
of internal cost performance measurement.

H191: The title level of the senior logistics executive main
effect is not a significant predictor of internal
differentiation performance measurement.

H192: The channel position main effect is not a significant
predictor of internal differentiation performance measurement.

H193: The interaction of title level of the senior logistics
executive and channel position is not a significant predictor
of internal differentiation performance measurement.

H194: The title level of the senior logistics executive main
effect is not a significant predictor of total internal
performance measurement.

H195: The channel position main effect is not a significant
predictor of total internal performance measurement.

H196: The interaction of title level of the senior logistics
executive and channel position is not a significant predictor
of total internal performance measurement.

211

H197: The title level of the senior logistics executive main
effect is not a significant predictor of cost benchmarking
performance measurement.

H198: The channel position main effect is not a significant
predictor of cost benchmarking performance measurement.

H199: The interaction of title level of the senior logistics
executive and channel position is not a significant predictor
of cost benchmarking performance measurement.

H200: The title level of the senior logistics executive main
effect is not a significant predictor of differentiation
benchmarking performance measurement.

H201: The channel position main effect is not a significant
predictor of differentiation benchmarking performance
measurement.

H202: The interaction of title level of the senior logistics
executive and channel position is not a significant predictor
of differentiation benchmarking performance measurement.

H203: The title level of the senior logistics executive main
effect is not a significant predictor of total benchmarking
performance measurement.

H204: The channel position main effect is not a significant
predictor of total benchmarking performance measurement.

H205: The interaction of title level of the senior logistics
executive and channel position is not a significant predictor
of total benchmarking performance measurement.

Table 17

H206: The frequency of logistics strategic plan update and
internal cost performance measurement are not related.

H207: The frequency of logistics strategic plan update and
internal differentiation performance measurement are not
related.

H208: The frequency of logistics strategic plan update and
total internal performance measurement are not related.

H209: The number of years logistics has been a formal function
and internal cost performance measurement are not related.

212

H210: The number of years logistics has been a formal function
and internal differentiation performance measurement are not
related.

H211: The number of years logistics has been a formal function
and total internal performance measurement are not related.

H212: The number of times logistics nas been reorganized and
internal cost performance measurement are not related.

H213: The number of times logistics has been reorganized and
internal differentiation performance measurement are not
related.

H214: The number of times logistics has been reorganized and
total internal performance measurement are not related.

H215: Participation in strategic planning by the senior
logistics executive and internal cost performance measurement
are not related.

H216: Participation in strategic planning by the senior
logistics executive and internal differentiation performance
measurement are not related.

H217: Participation in strategic planning by the senior
logistics executive and total internal performance measurement
are not related.

H218: The number of activities controlled by the logistics
function and internal cost performance measurement are not
related.

H219: The number of activities controlled by the logistics
function and internal differentiation performance measurement
are not related.

H220: The number of activities controlled by the logistics
function and total internal performance measurement are not
related.

H221: The title level of the senior logistics executive and
internal cost performance measurement are not related.

H222: The title level of the senior logistics executive and
internal differentiation performance measurement are not
related.

H223: The title level of the senior logistics executive and
total internal performance measurement are not related.

H224: The frequency of logistics strategic plan update and
cost benchmarking performance measurement are not related.

213

H225: The frequency of logistics strategic plan update and
differentiation benchmarking performance measurement are not
related.

H226: The frequency of logistics strategic plan update and
total benchmarking performance measurement are not related.

H227: The number of years logistics has been a formal function
and cost benchmarking performance measurement are not related.

H228: The number of years logistics has been a formal function
and differentiation benchmarking performance measurement are
not related.

H229: The number of years logistics has been a formal function
and total benchmarking performance measurement are not
related.

H230: The number of times logistics has been reorganized and
cost benchmarking performance measurement are not related.

H231: The number of times logistics has been reorganized and
differentiation benchmarking performance measurement are not
related.

H232: The number of times logistics has been reorganized and
total benchmarking performance measurement are not related.

H233: Participation in strategic jplanning' by' the senior
logistics executive and cost benchmarking performance
measurement are not related.

H234: Participation in strategic planning by the senior
logistics executive and differentiation benchmarking
performance measurement are not related.

H235: Participation in strategic planning by the senior
logistics executive and total benchmarking performance
measurement are not related.

H236: The number of activities controlled by the logistics
function and cost benchmarking performance measurement are not
related.

H237: The number of activities controlled by the logistics
function and differentiation benchmarking performance
measurement are not related.

H238: The number of activities controlled by the logistics
function and total benchmarking performance measurement are
not related.

H239: The title level of the senior logistics executive and
cost benchmarking performance measurement are not related.

214

H240: The title level of the senior logistics executive and
differentiation benchmarking performance measurement are not
related.

H241: The title level of the senior logistics executive and
total benchmarking performance measurement are not related.

Table 18

H242: No difference exists in mean internal cost performance
measurement between organizations with versus without a
logistics mission statement.

H243: No difference exists in mean internal differentiation
performance measurement between organizations with versus
without a logistics mission statement.

H244: No difference exists in mean total internal performance
measurement between organizations with versus without a
logistics mission statement.

H245: No difference exists in mean cost benchmarking
performance measurement between organizations with versus
without a logistics mission statement.

H24 6: No difference exists in mean differentiation
benchmarking performance measurement between organizations
with versus without a logistics mission statement.

H247: No difference exists in mean total benchmarking
performance measurement between organizations with versus
without a logistics mission statement.

H248: No difference exists in mean internal cost performance
measurement between organizations with versus without a
logistics strategic plan.

H249: No difference exists in mean internal differentiation
performance measurement between organizations with versus
without a logistics strategic plan.

H250: No difference exists in mean total internal performance
measurement between organizations with versus without a
logistics strategic plan.

H251: No difference exists in mean cost benchmarking
performance measurement between organizations with versus
without a logistics strategic plan.

H252: No difference exists in mean differentiation
benchmarking performance measurement between organizations
with versus without a logistics strategic plan.

215

H253: No difference exists in mean total benchmarking
performance measurement between organi zations with versus
without a logistics strategic plan.

H254: No difference exists in mean internal cost performance
measurement between organizations with logistics controlling
versus not controlling customer service.

H255: No difference exists in mean internal differentiation
performance measurement between organizations with logistics
controlling versus not controlling customer service.

H256: No difference exists in mean total internal performance
measurement between organizations with logistics controlling
versus not controlling customer service.

H257: No difference exists in mean cost benchmarking
performance measurement between organizations with logistics
controlling versus not controlling customer service.

H258: No idifference exists in :mean. differentiation
benchmarking performance measurement between organizations
with logistics controlling versus not controlling customer
service.

H259: No difference exists in mean total benchmarking
performance measurement between organizations with logistics
controlling versus not controlling customer service.

H260: No difference exists in mean internal cost performance
measurement between organizations with logistics controlling
versus not controlling logistics systems planning.

H261: No difference exists in mean internal differentiation
performance measurement between organizations with logistics
controlling versus not controlling logistics systems planning.

H262: No difference exists in mean total internal performance
measurement between organizations with logistics controlling
versus not controlling logistics systems planning.

H263: No difference exists in mean cost benchmarking
performance measurement between organizations with logistics
controlling versus not controlling logistics systems planning.

H264: No difference exists in mean differentiation
benchmarking performance measurement between organizations
with logistics controlling versus not controlling logistics
systems planning.

H265: No difference exists in mean total benchmarking
performance measurement between organizations with logistics
controlling versus not controlling logistics systems planning.

216

Table 20

H266: No difference exists in mean internal cost performance
measurement between manufacturers with versus without a
logistics mission statement.

H267: No difference exists in mean internal differentiation
performance measurement between manufacturers with versus
without a logistics mission statement.

H268: No difference exists in mean total internal performance
measurement between manufacturers with versus without a
logistics mission statement.

H269: No difference exists in mean cost benchmarking
performance measurement between manufacturers with versus
without a logistics mission statement.

H270: No difference exists in :mean differentiation
benchmarking performance measurement between manufacturers
with versus without a logistics mission statement.

H271: No difference exists in mean total benchmarking
performance measurement between manufacturers with versus
without a logistics mission statement.

H272: No difference exists in mean internal cost performance
measurement between wholesalers with versus without a
logistics mission statement.

H273: No difference exists in mean internal differentiation
performance measurement between wholesalers with versus
without a logistics mission statement.

H274: No difference exists in mean total internal performance
measurement between wholesalers with versus without a
logistics mission statement.

H275: No difference exists in mean cost benchmarking
performance measurement between wholesalers with versus
without a logistics mission statement.

H276: No difference exists in mean differentiation
benchmarking'performance:measurementfibetweennwholesalers'with
versus without a logistics mission statement.

H277: No difference exists in mean total benchmarking
performance measurement between wholesalers with versus
without a logistics mission statement.

H278: No difference exists in mean internal cost performance
measurement between retailers with versus without a logistics
mission statement.

217

H279: No difference exists in mean internal differentiation
performance measurement between retailers with versus without
a logistics mission statement.

H280: No difference exists in mean total internal performance
measurement between retailers with versus without a logistics
mission statement.

H281: No difference exists in mean cost benchmarking
performance measurement between retailers with versus without
a logistics mission statement.

H282: No difference exists in mean differentiation
benchmarking performance measurement between retailers with
versus without a logistics mission statement.

H283: No difference exists in mean total benchmarking
performance measurement between retailers with versus without
a logistics mission statement.

Table 21

H284: No difference exists in mean internal cost performance
measurement between manufacturers with versus without a
logistics strategic plan.

H285: No difference exists in mean internal differentiation
performance measurement between manufacturers with versus
without a logistics strategic plan.

H286: No difference exists in mean total internal performance
measurement between manufacturers with versus without a
logistics strategic plan.

H287: No difference exists in mean cost benchmarking
performance measurement between manufacturers with versus
without a logistics strategic plan.

H288: No difference exists in. mean differentiation
benchmarking performance measurement between manufacturers
with versus without a logistics strategic plan.

H289: No difference exists in mean total benchmarking
performance measurement between manufacturers with versus
without a logistics strategic plan.

H290: No difference exists in mean internal cost performance
measurement between wholesalers with versus without a
logistics strategic plan.

218

H291: No difference exists in mean internal differentiation
performance measurement between wholesalers with versus
without a logistics strategic plan.

H292: No difference exists in mean total internal performance
measurement between wholesalers with versus without a
logistics strategic plan.

H293: No difference exists in mean cost benchmarking
performance measurement between wholesalers with versus
without a logistics strategic plan.

H294: No difference exists in mean differentiation
benchmarking performance measurement.between wholesalers with
versus without a logistics strategic plan.

H295: No difference exists in mean total benchmarking
performance measurement between wholesalers with versus
without a logistics strategic plan.

H296: No difference exists in mean internal cost performance
measurement between retailers with versus without a logistics
strategic plan.

H297: No difference exists in mean internal differentiation
performance measurement between retailers with versus without
a logistics strategic plan.

H298: No difference exists in mean total internal performance
measurement between retailers with versus without a logistics
strategic plan.

H299: No difference exists in mean cost benchmarking
performance measurement between retailers with versus without
a logistics strategic plan.

H300: No idifference exists in :mean. differentiation
benchmarking performance measurement between retailers with
versus without a logistics strategic plan.

H301: No difference exists in mean total benchmarking
performance measurement between retailers with versus without
a logistics strategic plan.

Table 22

H302: No difference exists in mean internal cost performance
measurement between manufacturers with versus without
logistics controlling customer service.

219

H303: No difference exists in mean internal differentiation
performance measurement between manufacturers with versus
without logistics controlling customer service.

H304: No difference exists in mean total internal performance
measurement between manufacturers with versus without
logistics controlling customer service.

H305: No difference exists in mean cost benchmarking
performance measurement between manufacturers with versus
without logistics controlling customer service.

H306: No idifference exists in :mean differentiation
benchmarking performance measurement between manufacturers
with versus without logistics controlling customer service.

H307: No difference exists in mean total benchmarking
performance measurement between manufacturers with versus
without logistics controlling customer service.

H308: No difference exists in mean internal cost performance
measurement between wholesalers with versus without logistics
controlling customer service.

H309: No difference exists in mean internal differentiation
performance measurement between wholesalers with versus
without logistics controlling customer service.

H310: No difference exists in mean total internal performance
measurement between wholesalers with versus without logistics
controlling customer service.

H311: No difference exists in mean cost benchmarking
performance measurement between wholesalers with versus
without logistics controlling customer service.

H312: Not difference exists in :mean. differentiation
benchmarking'performance'measurement.betweenuwholesalers‘with
versus without logistics controlling customer service.

H313: No difference exists in mean total benchmarking
performance measurement between wholesalers with versus
without logistics controlling customer service.

H314: No difference exists in mean internal cost performance
measurement between retailers with versus without logistics
controlling customer service.

H315: No difference exists in mean internal differentiation
performance measurement between retailers with versus without
logistics controlling customer service.

220

H316: No difference exists in mean total internal performance
measurement between retailers with versus without logistics
controlling customer service.

H317: No difference exists in mean cost benchmarking
performance measurement between retailers with versus without
logistics controlling customer service.

H318: No difference exists in 'mean differentiation
benchmarking performance measurement between retailers with
versus without logistics controlling customer service.

H319: No difference exists in mean total benchmarking
performance measurement between retailers with versus without
logistics controlling customer service.

Table 23

H320: No difference exists in mean internal cost performance
measurement between manufacturers with versus without
logistics controlling logistics systems planning.

H321: No difference exists in mean internal differentiation
performance measurement between manufacturers with versus
without logistics controlling logistics systems planning.

H322: No difference exists in mean total internal performance
measurement between manufacturers with versus without
logistics controlling logistics systems planning.

H323: No difference exists in mean cost benchmarking
performance measurement between manufacturers with versus
without logistics controlling logistics systems planning.

H324: Noi difference exists in. :mean differentiation
benchmarking performance measurement between manufacturers
with versus without logistics controlling logistics systems
planning.

H325: No difference exists in mean total benchmarking
performance measurement between manufacturers with versus
without logistics controlling logistics systems planning.

H326: No difference exists in mean internal cost performance
measurement between wholesalers with versus without logistics
controlling logistics systems planning.

H327: No difference exists in mean internal differentiation
performance measurement between wholesalers with versus
without logistics controlling logistics systems planning.

221

H328: No difference exists in mean total internal performance
measurement between wholesalers with versus without logistics
controlling logistics systems planning.

H329: No difference exists in mean cost benchmarking
performance measurement between wholesalers with versus
without logistics controlling logistics systems planning.

H330: No difference exists in mean differentiation
benchmarking performance measurement between wholesalers with
versus without logistics controlling logistics systems
planning.

H331: No difference exists in mean total benchmarking
performance measurement between wholesalers with versus
without logistics controlling logistics systems planning.

H332: No difference exists in mean internal cost performance
measurement between retailers with versus without logistics
controlling logistics systems planning.

H333: No difference exists in mean internal differentiation
performance measurement between retailers with versus without
logistics controlling logistics systems planning.

H334: No difference exists in mean total internal performance
measurement between retailers with versus without logistics
controlling logistics systems planning.

H335: No difference exists in mean cost benchmarking
performance measurement between retailers with versus without
logistics controlling logistics systems planning.

H336: No difference exists in mean differentiation
benchmarking performance measurement between retailers with
versus without logistics controlling logistics systems
planning.

H337: No difference exists in mean total benchmarking
performance measurement between retailers with versus without
logistics controlling logistics systems planning.

Table 24

H338: The frequency of logistics strategic plan update and
internal cost performance measurement are not related among
manufacturers.

222

H339: The frequency of logistics strategic plan update and
internal differentiation performance measurement are not
related among manufacturers.

H340: The frequency of logistics strategic plan update and
total internal performance measurement are not related among
manufacturers.

H341: The number'of years logistics has been a formal function
and internal cost performance measurement are not related
among manufacturers.

H342: The number of years logistics has been a formal function
and internal differentiation performance measurement are not
related among manufacturers.

H343: The number’of years logistics has been a formal function
and total internal performance measurement are not related
among manufacturers.

H344: The number of times logistics has been reorganized and
internal cost performance measurement are not related among
manufacturers.

H345: The number of times logistics has been reorganized and
internal differentiation performance measurement are not
related among manufacturers.

H346: The number of times logistics has been reorganized and
total internal performance measurement are not related among
manufacturers.

H347: Participation in strategic planning by the senior
logistics executive and internal cost performance measurement
are not related among manufacturers.

H348: Participation in strategic planning by the senior
logistics executive and internal differentiation performance
measurement are not related among manufacturers.

H349: Participation. in .strategic jplanning' by ‘the senior
logistics executive and total internal performance measurement
are not related among manufacturers.

H350: The number of activities controlled by the logistics
function and internal cost performance measurement are not
related among manufacturers.

H351: The number of activities controlled by the logistics
function and internal differentiation.performance measurement
are not related among manufacturers.

223

H352: The number of activities controlled by the logistics
function and total internal performance measurement are not
related among manufacturers.

H353: The title level of the senior logistics executive and
internal cost performance measurement are not related among
manufacturers.

H354: The title level of the senior logistics executive and
internal differentiation performance measurement are not
related among manufacturers.

H355: The title level of the senior logistics executive and
total internal performance measurement are not related among
manufacturers.

H356: The frequency of logistics strategic plan update and
cost benchmarking performance measurement are not related
among manufacturers.

H357: The frequency of logistics strategic plan update and
differentiation benchmarking performance measurement are not
related among manufacturers.

H358: The frequency of logistics strategic plan update and
total benchmarking performance measurement are not related
among manufacturers.

H359: The number of years logistics has been a formal function
and cost benchmarking performance measurement are not related
among manufacturers.

H360: The number of years logistics has been a formal function
and differentiation benchmarking performance measurement are
not related among manufacturers.

H361: The number of years logistics has been a formal function
and total benchmarking performance measurement are not related
among manufacturers.

H362: The number of times logistics has been reorganized and
cost benchmarking performance measurement are not related
among manufacturers.

H363: The number of times logistics has been reorganized and
differentiation benchmarking performance measurement are not
related among manufacturers.

H364: The number of times logistics has been reorganized and
total benchmarking performance measurement are not related
among manufacturers.

224

H365: Participation in strategic planning by the senior
logistics executive and cost benchmarking performance
measurement are not related among manufacturers.

H366: Participation in strategic planning by the senior
logistics executive and differentiation benchmarking
performance measurement are not related among manufacturers.

H367: Participation in strategic planning by the senior
logistics executive and total benchmarking performance
measurement are not related among manufacturers.

H368: The number of activities controlled by the logistics
function and cost benchmarking performance measurement are not
related among manufacturers.

H369: The number of activities controlled by the logistics
function and differentiation benchmarking performance
measurement are not related among manufacturers.

H370: The number of activities controlled by the logistics
function and total benchmarking performance measurement are
not related among manufacturers.

H371: The title level of the senior logistics executive and
cost benchmarking performance measurement are not related
among manufacturers.

H372: The title level of the senior logistics executive and
differentiation benchmarking performance measurement are not
related among manufacturers.

H373: The title level of the senior logistics executive and
total benchmarking performance measurement are not related
among manufacturers.

Table 25

H374: The frequency of logistics strategic plan update and
internal cost performance measurement are not related among
wholesalers.

H375: The frequency of logistics strategic plan update and
internal differentiation performance measurement are not
related among wholesalers.

H376: The frequency of logistics strategic plan update and
total internal performance measurement are not related among
wholesalers.

225

H377: The number of years logistics has been a formal function
and internal cost performance measurement are not related
among wholesalers.

H378: The number'of years logistics has been a formal function
and internal differentiation performance measurement are not
related among wholesalers.

H379: The number of years logistics has been a formal function
and total internal performance measurement are not related
among wholesalers.

H380: The number of times logistics has been reorganized and
internal cost performance measurement are not related among
wholesalers.

H381: The number of times logistics has been reorganized and
internal differentiation performance measurement are not
related among wholesalers.

H382: The number of times logistics has been reorganized and
total internal performance measurement are not related among
wholesalers.

H383: Participation in strategic planning by the senior
logistics executive and internal cost performance measurement
are not related among wholesalers.

H384: Participation in strategic planning by the senior
logistics executive and internal differentiation performance
measurement are not related among wholesalers.

H385: Participation in strategic planning by the senior
logistics executive and total internal performance measurement
are not related among wholesalers.

H386: The number of activities controlled by the logistics
function and internal cost performance measurement are not
related among wholesalers.

H387: The number of activities controlled by the logistics
function and internal differentiation.performance measurement
are not related among wholesalers.

H388: The number of activities controlled by the logistics
function and total internal performance measurement are not
related among wholesalers.

H389: The title level of the senior logistics executive and
internal cost performance measurement are not related among
wholesalers.

226

H390: The title level of the senior logistics executive and
internal differentiation performance measurement are not
related among wholesalers.

H391: The title level of the senior logistics executive and
total internal performance measurement are not related among
wholesalers.

H392: The frequency of logistics strategic plan update and
cost benchmarking performance measurement are not related
among wholesalers.

H393: The frequency of logistics strategic plan update and
differentiation benchmarking performance measurement are not
related among wholesalers.

H394: The frequency of logistics strategic plan update and
total benchmarking performance measurement are not related
among wholesalers.

H395: The number of years logistics has been a formal function
and cost benchmarking performance measurement are not related
among wholesalers.

H396: The number of years logistics has been a formal function
and differentiation benchmarking performance measurement are
not related among wholesalers.

H397: The number of years logistics has been a formal function
and total benchmarking performance measurement are not related
among wholesalers.

H398: The number of times logistics has been reorganized and
cost benchmarking performance measurement are not related
among wholesalers.

H399: The number of times logistics has been reorganized and
differentiation benchmarking performance measurement are not
related among wholesalers.

H400: The number of times logistics has been reorganized and
total benchmarking performance measurement are not related
among wholesalers.

H401: Participation in strategic planning' by' the senior
logistics executive and cost benchmarking performance
measurement are not related among wholesalers.

H402: Participation in strategic planning' by the senior
logistics executive and differentiation benchmarking
performance measurement are not related among wholesalers.

227

H403: Participation in strategic planning' by the senior
logistics executive and total benchmarking performance
measurement are not related among wholesalers.

H404: The number of activities controlled by the logistics
function and cost benchmarking performance measurement are not
related among wholesalers.

H405: The number of activities controlled by the logistics
function and differentiation benchmarking performance
measurement are not related among wholesalers.

H406: The number of activities controlled by the logistics
function and total benchmarking performance measurement are
not related among wholesalers.

H407: The title level of the senior logistics executive and
cost benchmarking performance measurement are not related
among wholesalers.

H408: The title level of the senior logistics executive and
differentiation benchmarking performance measurement are not
related among wholesalers.

H409: The title level of the senior logistics executive and
total benchmarking performance measurement are not related
among wholesalers.

Table 26

H410: The frequency of logistics strategic plan update and
internal cost performance measurement are not related among
retailers.

H411: The frequency of logistics strategic plan update and
internal differentiation performance measurement are not
related among retailers.

H412: The frequency of logistics strategic plan update and
total internal performance measurement are not related among
retailers.

H413: The number of years logistics has been a formal function
and internal cost performance measurement are not related
among retailers.

H414: The number of years logistics has been a formal function
and internal differentiation performance measurement are not
related among retailers.

228

H415: The number of years logistics has been a formal function
and total internal performance measurement are not related
among retailers.

H416: The number of times logistics has been reorganized and
internal cost performance measurement are not related among
retailers.

H417: The number of times logistics has been reorganized and
internal differentiation. performance measurement are not
related among retailers.

H418: The number of times logistics has been reorganized and
total internal performance measurement are not related among
retailers.

H419: Participation in strategic planning“ by the senior
logistics executive and internal cost performance measurement
are not related among retailers.

H420: Participation in strategic planning by the senior
logistics executive and internal differentiation performance
measurement are not related among retailers.

H421: Participation. in strategic planning' by the senior
logistics executive and total internal performance measurement
are not related among retailers.

H422: The number of activities controlled by the logistics
function and internal cost performance measurement are not
related among retailers.

H423: The number of activities controlled by the logistics
function and internal differentiation performance measurement
are not related among retailers.

H424: The number of activities controlled by the logistics
function and total internal performance measurement are not
related among retailers.

H425: The title level of the senior logistics executive and
internal cost performance measurement are not related among
retailers.

H426: The title level of the senior logistics executive and
internal differentiation performance measurement are not
related among retailers.

H427: The title level of the senior logistics executive and
total internal performance measurement are not related among
retailers.

229

H428: The frequency of logistics strategic plan update and
cost benchmarking performance measurement are not related
among retailers.

H429: The frequency of logistics strategic plan update and
differentiation benchmarking performance measurement are not
related among retailers.

H430: The frequency of logistics strategic plan update and
total benchmarking performance measurement are not related
among retailers.

H431: The number of years logistics has been a formal function
and cost benchmarking performance measurement are not related
among retailers.

H432: The number of years logistics has been a formal function
and differentiation benchmarking performance measurement are
not related among retailers.

H433: The number of years logistics has been a formal function
and.total benchmarking performance measurement.are not.related
among retailers.

H434: The number of times logistics has been reorganized and
cost benchmarking performance measurement are not related
among retailers.

H435: The number of times logistics has been reorganized and
differentiation benchmarking performance measurement are not
related among retailers.

H436: The number of times logistics has been reorganized and
total benchmarking performance measurement are not related
among retailers.

H437: Participation in strategic planning by the senior
logistics executive and cost benchmarking performance
measurement are not related among retailers.

H438: Participation in strategic jplanning’ by the senior
logistics executive and differentiation benchmarking
performance measurement are not related among retailers.

H439: Participation in strategic planning by the senior
logistics executive and total benchmarking performance
measurement are not related among retailers.

H440: The number of activities controlled by the logistics
function and cost benchmarking performance measurement are not
related among retailers.

230

H441: The number of activities controlled by the logistics
function and differentiation benchmarking performance
measurement are not related among retailers.

H442: The number of activities controlled by the logistics
function and total benchmarking performance measurement are
not related among retailers.

H443: The title level of the senior logistics executive and
cost benchmarking performance measurement are not related
among retailers.

H444: The title level of the senior logistics executive and
differentiation benchmarking performance measurement are not
related among retailers.

H445: The title level of the senior logistics executive and
total benchmarking performance measurement are not related
among retailers.

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