ACTIVITY-BASED COST MODELING AND DYNAMIC SIMULATION STUDY OF AN
INTERNATIONAL REUSABLE PACKAGING SYSTEMS
By
Jongkyoung Kim

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Packaging – Doctor of Philosophy
2014

ABSTRACT
ACTIVITY-BASED COST MODELING AND DYNAMIC SIMULATION STUDY OF AN
INTERNATIONAL REUSABLE PACKAGING SYSTEMS
By
Jongkyoung Kim
This study compares reusable to expendable packaging systems in a total cost
analysis.

It explores whether reusable packaging is an economically viable option to

replace the expendable packaging in an international supply chain. It assesses the financial
performance of reusable packaging, using a combination of Activity-Based Costing (ABC)
with static and dynamic simulation.
Firstly, ABC method is used to visualize packaging activities and costs in an
automotive part supply chain. Three packaging system costs (expendable, reusable, and
rental packaging systems) are established, and for each the packaging activity drivers,
activity costs and the total packaging costs were calculated.
Secondly, a static simulation was used to reveal interrelationships between the
packaging and supply chain costs. Eleven scenarios were tested to learn if relative cost
changes in one or more of the variables can influence the packaging decision. Overall,
container cost ratio and cycle time were the most decisive factors in determining the
packaging system cost. It was found that cycle time is a more important factor than
shipping distance, meaning that time is more important than physical distance since it
directly affects the number of required containers. Customs charges were also an important
factor for implementing reusable plastic container system (RPCS) and rental plastic
container system (RENS) internationally because this will reduce profitability of running
reusable container system.

Proper documentation for import tax exemption, free trade

agreement and other contracts should be necessary.

Thirdly, a dynamic simulation method was used to compare and verify the
company-provided data and ABC model.

ARENA software was used to calculate the

number of reusable plastics containers (totes) and costs for three international supply chain
routes of a company.

Seven scenarios were tested which evaluated system time and cost,

resource utilization for the process and number of entities processed in the process.

The

greatest benefit of using the dynamic simulation was taking account of time during the
logistical process such as identifying a bottleneck at ports due to loading and unloading
process that is not revealed in the company-provided data or static simulation.
In summary, the cost ratio between expendable and reusable containers is the first
thing to consider because of relatively longer cycle time and distance, a greater number of
containers are required. However, shorter cycle time and efficient material handling can
reduce costs by avoiding unnecessary logistical activities and lag time during transshipment and custom process. Standardized reusable containers, since they are
interchangeable, reduce the number required for safety stock.
This study shows the amount of economic impact by adopting different packaging
systems in the international supply chain, implying changing any elements of packaging
process may change the cost driver for each activity, which eventually affects total logistics
costs.
This research is limited because the simulation model is only for an automotive part
supply chain from a single supplier to a single customer, and the analysis is limited to cost.
The environmental performance such as packaging waste generated and greenhouse gas
emissions, and long term performance of reusable container and operation systems are
recommended for future research

Special thanks to Dr. Diana Twede for all the advice
given during my Ph. D program

iv

ACKNOWLEDGEMENTS

The work presented in this thesis would not have been possible without my close
association with many people. I take this opportunity to extend my sincere gratitude
and appreciation to all those who made this Ph.D. thesis possible.
First and foremost, I would like to extend my sincere gratitude to my research
guide Dr. Diana Twede for her dedicated help, advice, inspiration, encouragement
and continuous support, throughout my Ph.D. I owe her lots of gratitude for having
me shown the way of research, and I am really glad to be associated with a person
like Dr. Diana Twede in my life.
My special words of thanks should also go to my graduate committee
members, Drs. David Closs, Bruce Harte, and Gary Burgess, for their continuous
support, guidance, cooperation, encouragement and for facilitating all the
requirements, going out of their way. Their constant guidance, cooperation,
motivation and support have always kept me going ahead. I feel privileged to be
associated with great professors in Michigan State University like them during my life.
I express my heart-felt gratitude to Dr. Myeong-Hoon Lee for his financial
support and aspirations. Dr. Lee has been for being with me as a life advisor
throughout my career. I gratefully acknowledge Dr. Jong-Hyon Lee of Korea
Container Pool, Mr. Jud Bradford of Bradford Company, and Yong-Jang Kwon of
Korea Railroad Institute for providing financial support.

I would not complete my

study without their generous support and understanding of my pursuit.
A special mention of thanks to my friends at School of Packaging, Yang-jae
Shin, Eun-jae Lee, Chan-young Chun, Chun-ki Min, Hyun-mo Jeong, Do-Chan Seo,
Seung-wook Whang, Jong-hoon Park, Tae-gyong Yeon, Won-tae Seo, Kun-woong
v

Park, Ji-yon Lee, Ki-kyung Kim, Ji-young Park, Dong-ho Kang, Dong-sik Kim, Suhyung Lee, Doug, Ricky and many others for their help and cooperation during my
life in East Lansing. Their timely help and friendship shall always be remembered.
I feel a deep sense of gratitude for my wife, Jin-suk, and son, Kyung-su.
Their patience and sacrifice will remain my inspiration throughout my life. I am also
very much grateful to my parents for their constant inspiration and encouragement.
Finally, I am thankful to the faculty and staff of School of Packaging for
cooperation.

Jongkyoung Kim

vi

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................ x
LIST OF FIGURES ....................................................................................................... xvi
CHAPTER 1 INTRODUCTION ........................................................................................ 1
CHAPTER 2 LITERATURE REVIEW .............................................................................. 6
2.1. The relationships between packaging and supply chain .............................. 7
2.1.1. Function of packaging in supply chain ............................................ 7
2.1.2. Packaging cost in a supply chain .................................................. 11
2.2. Reusable versus expendable packaging .................................................... 14
2.2.1. Cost aspects ................................................................................. 14
2.2.2. Impact of ownership ...................................................................... 21
2.2.3. Impact of standardization .............................................................. 26
2.3. Activity-Based Costing (ABC) in logistics ................................................... 30
2.3.1. The general concept of ABC ….. .................................................. 32
2.3.2. ABC applied to reusable packaging .............................................. 35
2.3.3. ABC process in supply chain management .................................. 37
2.3.3.1. Analyzing supply chain functions .................................. 38
2.3.3.2. Breaking processes down into activities ....................... 38
2.3.3.3. Identifying the resources consumed for activities ......... 38
2.3.3.4. Determining the cost for each activity ........................... 39
2.3.3.5. Tracing the costs to the cost objects ............................ 40
2.3.3.6. Collecting activity cost data and calculating the final total cost . 41
2.4. Dynamic simulation studies ........................................................................ 41
2.4.1. Complexity of global supply chain ….. .......................................... 42
2.4.2. Computer simulation techniques................................................... 43
2.4.3. Simulation studies for supply chain............................................... 45
2.4.4. Simulation studies in logistics packaging ...................................... 47

CHAPTER 3 OBJECTIVES OF THE STUDY .............................................................. 51
CHAPTER 4 RESEARCH METHOD ........................................................................... 53
4.1. Research model design ............................................................................. 55
4.2. The international logistics routes investigated ........................................... 58
4.3. Research variables .................................................................................. 58
4.4. Data collection ......................................................................................... 61
CHAPTER 5 PACKAGING COST ANALYSIS USING ABC METHOD ........................ 63
vii

5.1. Process and activity descriptions ............................................................... 63
5.1.1. Packaging purchasing activities................................................... 73
5.1.2. Logistics administration activities and cost drivers ...................... 73
5.1.3. Warehousing activities and cost drivers ...................................... 75
5.1.4. Transportation activities and cost drivers .................................... 80
5.1.5. After-use activities and cost drivers ............................................. 81
5.2. Packaging system cost calculations ........................................................... 82
5.2.1. Expendable packaging system cost (EPSC) ............................... 82
5.2.1.1. Expendable container unit cost .................................... 83
5.2.1.2. Logistics administration cost for expendable
packaging system...................................................................... 83
5.2.1.3. Outbound transportation cost for expendable
container system ....................................................................... 85
5.2.1.4. Warehousing cost for expendable container system .... 86
5.2.1.5. After use activity cost and revenue for
expendable container system .................................................... 89
5.2.2. Reusable packaging system cost (RPSC) ................................... 90
5.2.2.1. Reusable container cost ............................................. 91
5.2.2.2. Logistics administration cost for reusable packaging
system ..................................................................................... 92
5.2.2.3. Transportation cost for reusable container system ..... 93
5.2.2.4. Warehousing cost for reusable container system ......... 95
5.2.2.5. After use activity cost and revenue of reusable
container system ....................................................................... 96
5.2.3. Reusable Container Rental System Cost (CRSC) ..................... 97
5.2.3.1. Container rental cost .................................................. 98
5.2.3.2. Other costs ................................................................... 99
5.2.4. Summary ................................................................................... 100
CHAPTER 6 COMPARATIVE REGRESSION ANALYSIS USING THE ABC MODEL
.................................................................................................................................... 102
6.1. Fixed and assumed data.......................................................................... 105
6.2. Base regression analysis results ............................................................. 106
6.3. Results of regression analysis scenarios ................................................. 110
6.3.1. Scenario 1: Distance doubled ........................................ 110
6.3.2. Scenario 2: Cycle time doubled ..................................... 112
6.3.3. Scenario 3: Custom clearance charge removed ............ 114
6.3.4. Scenario 4: Quantity of containers on a pallet halved.... 116
6.3.5. Scenario 5: Average daily volume doubled ................... 117
6.3.6. Scenario 6: Backhaul volume factor halved ................... 119
6.3.7. Scenario 7: Designed container life doubled ................. 120
6.3.8. Scenario 8 Reusable and rental costs doubled ............. 122
6.3.9. Scenario 9 Return rate 20 percent dropped .................. 124
6.3.10. Scenario 10: Cycle time halved ................................... 125
6.3.11. Scenario 11: Combination of Distance Doubled,
Return rate 20 percent dropped and Cycle time doubled ........ 127
6.4. Summary of regression analysis results .................................................. 128

viii

CHAPTER 7 DYNAMIC SIMULATION MODELING .................................................. 131
7.1. Simulation model ..................................................................................... 131
7.2. Results of simulations .............................................................................. 139
7.2.1. Simulation 1: Impact of expected life of reusable containers ..... 140
7.2.2. Simulation 2: Impact of distance (port to port comparison) – for
2 years of operations ........................................................................... 143
7.2.3. Simulation 3: Impact of loss rate (return rate) of reusable
containers............................................................................................ 145
7.2.4. Simulation 4: Impact of container cost for 2 years of operations 147
7.2.5. Simulation 5: Average daily volume .......................................... 148
7.2 6. Simulation 6: Number of containers on a pallet ......................... 149
7.2.7. Simulation 7: Comparison of transportation cost between
straight wall and collapsible container types ....................................... 150
7.3. Summary of scenarios ............................................................................. 151
CHAPTER 8 CONCLUSIONS AND DISCUSSION ..................................................... 154
8.1. Importance of the study ........................................................................... 154
8.2. ABC analysis ........................................................................................... 155
8.3. Regression analysis ................................................................................. 156
8.4. Dynamic simulation .................................................................................. 157
8.5. Validation ................................................................................................. 158
8.6. Recommendations for reusable packaging optimization .......................... 159
8.6.1. Container cost ........................................................................... 160
8.6.2. Cycle time ................................................................................. 161
8.6 3. Custom charges ........................................................................ 161
8.6.4. Management options ................................................................. 161
8.6.5. Geographical location ................................................................ 162
8.6.6. Freight balances between inbound and outbound freights ........ 162
8.6.7. Standardization ......................................................................... 163
8.7. Key trade-offs ........................................................................................... 166
8.7.1. Cost ratio between expendable and reusable container............ 166
8.7.2. Cycle time ................................................................................. 166
8.7 3. Standardization ......................................................................... 167
8.7.4. Risk assessment ....................................................................... 167
8.8. Limitations and recommendations for future research.............................. 168
BIBLIOGRAPHY ......................................................................................................... 170

ix

LIST OF TABLES

Table 1: Key elements of performance measurement of logistics ................................. 10
Table 2: The six key global supply chain performance measurements ......................... 11
Table 3: Examples of packaging cost criteria from packaging suppliers ....................... 13
Table 4: Comparison of reusable and expendable packaging costs ............................. 17
Table 5:The breakdown of reusable packaging costs ................................................... 19
Table 6: Cost types and structure of reusable systems ................................................. 20
Table 7: Types of ownerships of reusable packaging ................................................... 22
Table 8:The advantages and disadvantages of the returnable container ownership
options ................................................................................................................... 23
Table 9: Return logistics systems in the Netherlands .................................................... 25
Table 10: Typical pallet dimensions .............................................................................. 27
Table 11: Advantages and Disadvantages of Implementation of ABC .......................... 34
Table 12: An example of breaking down of activity and resources: Truck Drive ........... 38
Table 13: Main warehouse and transport activities and activity drivers ......................... 40
Table 14: Examples of activities, resource (or cost) drivers, resources, activity drivers 41
Table 15: Comparison of four simulation types for supply chain management ............. 46
Table 16: Research parameters and variables for the study ......................................... 59
Table 17: Independent variables and associated ranges .............................................. 61
Table 18: Required data for the study ........................................................................... 62
Table 19: Processes and activities for the typical packaging system for the
international automotive part supply chain ............................................................. 64
Table 20: Proposed activities and activity cost drivers considered for calculation ......... 66
Table 21: ABC cost analysis for five logistical activity areas ......................................... 73
Table 22: Logistics administration activities and activity cost drivers considered for
calculation .............................................................................................................. 74
Table 23: Warehouse activities and activity cost drivers ............................................... 79
x

Table 24: Transportation activities and activity cost drivers considered for calculation . 81
Table 25: After-use activities and activity cost drivers considered for calculation ......... 82
Table 26: Definitions of acronyms used in calculations ................................................. 82
Table 27: Standard administration time and cost for each container ............................. 84
Table 28: Loaded containers per 5 ton truck and container .......................................... 86
Table 29: Main warehousing activities and activity cost drivers .................................... 88
Table 30: Warehousing activity cost calculation formulae ............................................. 88
Table 31: Warehousing cost calculation ........................................................................ 89
Table 32: Example of storing cost calculation ............................................................... 89
Table 33: After-use activities and cost drivers for expendable container system .......... 90
Table 34: Summary of the reusable container rental cost calculation ......................... 100
Table 35: Comparison of different cost elements of three packaging systems............ 100
Table 36: Summary of packaging system activity cost formulae ................................. 101
Table 37: Fixed and assumed data used for the cost modeling .................................. 105
Table 38: Base regression analysis results comparing between reusable packaging
system and expendable packaging system costs ................................................ 107
Table 39: Base regression analysis results comparing between reusable packaging
system and rental packaging system costs .......................................................... 109
Table 40: Base regression analysis results comparing between expendable
packaging system and rental packaging system costs......................................... 110
Table 41: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 1:
Distance doubled) ................................................................................................ 111
Table 42: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 1: Distance
doubled) ............................................................................................................... 111
Table 43: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 1: Distance
doubled) ............................................................................................................... 112

xi

Table 44: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 2: Cycle
time doubled) ....................................................................................................... 113
Table 45: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 2: Cycle time
doubled) ............................................................................................................... 113
Table 46: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 2: Cycle time
doubled) ............................................................................................................... 114
Table 47: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 3: Custom
charges removed) ................................................................................................ 115
Table 48: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 3: Custom
charges removed) ................................................................................................ 115
Table 49: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 3: Custom
charges removed) ................................................................................................ 115
Table 50: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 4:
Container Q on a pallet halved) ........................................................................... 116
Table 51: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 4: Container Q
on a pallet halved) ................................................................................................ 117
Table 52: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 4: Container
Q on a pallet halved) ............................................................................................ 117
Table 53: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 5:
Average daily Volume doubled) ........................................................................... 118
Table 54: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 5: Average daily
Volume doubled) .................................................................................................. 118
Table 55: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 5: Average
daily Volume doubled).......................................................................................... 118
xii

Table 56: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 6:
Backhaul volume halved) ..................................................................................... 119
Table 57: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 6: Backhaul
volume halved) ..................................................................................................... 119
Table 58: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 6: Backhaul
volume halved) ..................................................................................................... 120
Table 59: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 7:
Container life doubled) ......................................................................................... 121
Table 60: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 7: Container life
doubled) ............................................................................................................... 121
Table 61: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 7: Container
life doubled) ......................................................................................................... 121
Table 62: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 8:
Reusable cost doubled) ....................................................................................... 122
Table 63: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 8: Reusable
cost doubled)........................................................................................................ 123
Table 64: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 8: Rental cost
doubled) ............................................................................................................... 123
Table 65: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 8: Rental
cost doubled)........................................................................................................ 123
Table 66: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 9: Return
rate 20 percent dropped)...................................................................................... 124
Table 67: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 9: Return rate
20 percent dropped) ............................................................................................. 124
xiii

Table 68: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 9: Return
rate 20 percent dropped)...................................................................................... 125
Table 69: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 10: Cycle
time halved) ......................................................................................................... 126
Table 70: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 10: Cycle time
halved) ................................................................................................................. 126
Table 71: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 10: Cycle
time halved) ......................................................................................................... 126
Table 72: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 13:
Combination of Distance Doubled, Return rate 20 percent dropped and Cycle
time doubled) ....................................................................................................... 127
Table 73: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 13:
Combination of Distance Doubled, Return rate 20 percent dropped and Cycle
time doubled) ....................................................................................................... 127
Table 74: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 13:
Combination of Distance Doubled, Return rate 20 percent dropped and Cycle
time doubled) ....................................................................................................... 128
Table 75: Data comparison based on the number of replications ............................... 139
Table 76: Impact of expected useful life of reusable containers .................................. 140
Table 77: Unit and total cost comparison of three types after simulating 1st to 10th
years of operations............................................................................................... 142
Table 78: Distance vs. route time (port to port) ........................................................... 143
Table 79: Impact of shipping distance changes on route time and the number of
packages required................................................................................................ 144
Table 80: Unit cost comparison of three when shipping distance changed ................. 144
Table 81: Cost comparison of RPC and REN when loss rate changed ....................... 146
Table 82: Cost comparison when container costs changed ........................................ 147
xiv

Table 83: Cost comparison when average daily volume of containers changed ......... 148
Table 84: Comparison of total packaging system cost between straight wall and
collapsible container types for 1 year ................................................................... 151
Table 85: Cost comparison based on the company-provided data, ABC model, and
dynamic simulation ............................................................................................... 159
Table 86: Standards and guidelines developed on returnable packaging ................... 164

xv

LIST OF FIGURES

Figure 1: Organizational levels of reusable systems ..................................................... 24
Figure 2: General principles of an ABC model .............................................................. 37
Figure 3: The conceptual research model ..................................................................... 55
Figure 4: The conceptual model for packaging activity cost analysis ............................ 56
Figure 5: Simulation process flow ................................................................................. 57
Figure 6: Proposed International packaging and automotive part supply chain flow for
the study ................................................................................................................ 63
Figure 7: Warehouse (KD Center) activities .................................................................. 75
Figure 8: 3PL Consolidation Center activities ................................................................ 76
Figure 9: An example of loaded containers in a 40 feet sea container .......................... 86
Figure 10: Simulation model for an international automotive part packaging system .. 132
Figure 11: The first sub model: processes at a 3PL distribution center ....................... 133
Figure 12: The second sub model: processes at a Knock Down center ...................... 134
Figure 13: The third sub model: processes at an international port ............................. 134
Figure 14: The forth sub model: processes at a port in the US ................................... 135
Figure 15: The fifth sub model: processes at 3PL consolidation center in the US ....... 135
Figure 16: The sixth sub model: line feeding and disassembling at the manufacturing
plant ..................................................................................................................... 135
Figure 17: The seventh sub model: returning process at 3PL consolidation center .... 136
Figure 18: The eighth sub model: Shipping process of reusable containers (at the US)
............................................................................................................................. 137
Figure 19: The ninth sub model: Shipping process of reusable containers (at the
oversea port) ........................................................................................................ 137
Figure 20: The tenth sub model: reconditioning and repairing of reusable containers 137
Figure 21: Total cost comparison of different packaging systems depending on the
different expected useful life of the container ....................................................... 142

xvi

Figure 22: Total cost comparison of different packaging systems depending on the
different route (shipping) time of the container ..................................................... 145
Figure 23: Total cost comparison of different packaging systems depending on the
different loss rate of the container ........................................................................ 146
Figure 24: Total cost comparison of different packaging systems depending on the
different average daily volume of containers used ............................................... 149
Figure 25: Unit cost comparison of different packaging systems depending on the
different number of containers on a pallet ............................................................ 150
Figure 26: Cost identification loops and interaction between logistics and packaging
decision ................................................................................................................ 168

xvii

CHAPTER 1
INTRODUCTION
Packaging plays a critical role in the economics and sustainability of supply
chains. Packaging not only protects the product from physical damage, but affects
the cost of every logistical activity, such as transportation, freight handling, packing,
warehousing, waste disposal, and information management.
Although the importance of packaging in supply chains has been widely
addressed, the total cost and value of packaging has not been successfully
estimated (Twede, 2009). Many companies often fail to include important logistical
activity costs in their total packaging cost estimations (NEFAB USA, 2010).

Most

companies are not aware of the importance or nature of packaging-related costs.
Limited resources and the lack of reliable packaging cost information make it difficult
for them to make packaging management decisions (Dubiel, 1996). Beyond
economics, an increasing emphasis on the environmental responsibility of packaging
and logistics, combined with increasing complexity of supply chains, have forced
packaging managers to find more sustainable and profitable strategies.

However,

without comprehensive financial analyses, business decision makers have difficulties
identifying the opportunities for improvement throughout their entire logistics system
(Holmes, 1999).
A choice between reusable and expendable shipping container systems is
one such strategic opportunity. In the vehicle manufacturing business, where car
manufacturers are always looking for a more cost effective and greener supply chain,
reusable shipping containers have been a popular choice for leading companies
such as GM, Toyota and Volkswagen (Nunes & Bennett, 2010). The global
manufacturer John Deere & Co. reportedly invested an initial $20 million in
1

containers to develop a reusable shipping container system (Kroon & Vrijens, 1995).
Manufacturers have adopted these reusable systems because, by applying standard
and ergonomic design principles, reusable shipping containers can reduce the cost
of handling, materials and packaging waste (Modern Material Handling, 2006).
However, there has been no published study documenting the total profitability,
including logistics costs, of reusable shipping containers.
Previous studies of reusable packaging use cost inputs from the prospective
users are limited to simplified logistical networks and are not an absolute indication
of total costs and benefits for an end user. Mollenkopf et al. (2005) used a relative
cost approach to compare reusable and expandable shipping containers in a case
study using a static simulation methodology.

The Reusable Packaging Association

(2010) has developed a “Quick Economic Calculator” and “Environmental Calculator”
to compare basic cost differences of one-way corrugated packaging verses reusable
plastic packaging. Such methods can help guide packaging and supply chain
decision-makers, but their static cost models do not reflect the dynamic nature of the
supply chain and to systemically address economic trade-offs.
From an environmental performance perspective, WRAP (Waste &
Resources Action Programme), a non-profit, government-funded company in the UK,
tried to identify key factors that influence the environmental impact and performance
of reusable packaging systems in a Life Cycle Analysis (LCA) literature review. This
report found that although LCA studies can be useful for packaging decisions, LCA
results cannot identify whether one packaging option is environmentally preferable to
another because of various factors such as product and packaging type, supply
chain management situations, etc. They find that some LCA studies have credibility
issues because the results tend to favor sponsors (Wood & Sturges, 2010). Twede

2

and Clarke (2004) argued that although LCA studies are popular and useful, a
thorough financial analysis of shipping containers will likely come to the same
decision since almost all of those costs are directly related to the company who paid,
which internalizes most costs (like waste disposal and fuel) that would otherwise be
considered “externalities.”
It should be noted that if a reusable packaging system does not perform
properly, it can become a very expensive expendable packaging with more
packaging waste.

The system should be managed and monitored by a pool

operator with authority and responsibility, especially during the collection process
(Mckerrow, 1996).

As the automotive industry has learned the value of packaging

system management by trial-and-error, it has learned that reusable packaging
systems have different financial and environmental effects depending on
management of the system (Twede & Clake, 2004). Ownership, whether by the
suppliers, customers or third-party logistics providers (3PLs), influence a range of
factors including the respective bargaining power of the involved parties,
compatibility with production systems, and the respective logistical capabilities of
suppliers/receivers (Holmes, 1999).
The accounting method needs to be considered. Holmes (1999) found that it
is not easy to estimate the total cost in traditional costing systems based on volumebased allocation of overhead (or indirect cost). Traditional accounting methods
distort information, so that manager cannot identify cause and effect of logistical
activities and supply chain processes.

Traditional approaches to accounting based

upon full-cost allocation can be misleading and dangerous – and are one reason why
it is so difficult to calculate true packaging logistics costs.

3

Activity-Based Costing (ABC) methods have drawn interest because they can
identify the cost associated with each level of various activities (e.g. cost per line
item picked, cost per delivery, etc.) which enables to show a clearer picture of the
true packaging and logistics costs.

ABC traces the consumption of resources by

identifying activity cost drivers which trace particular cost objects such as specific
products and services (Brac, 2000).
Although it has not yet been applied to reusable packaging, ABC applied to
packaging activity cost drivers should be able to yield better comparative measures
of total packaging costs.
There has likewise been no research to compare returnable and expendable
packaging with dynamic simulation methods. Simulation techniques are popular for
supply chain studies because they provide answers to “what-if” questions, such as
redesigning supply chains for an industry or a company (Chu, 2003). Hellstöm and
Johansson (2007) introduced the methodological approach of combining case
studies and simulation studies while introducing the concept of asset visibility for
Returnable Transport Item (RTI). However, this study did not investigate
transportation costs or effects of system management, like differences between
various ownership options or logistical networks.
There has been some research focused on evaluating financial and
environmental aspects between reusable and expendable packaging systems.
However, no studies have developed integrated and theoretically comprehensive
results that reflect the current trend of global supply chains and visualize the
dynamics of packaging activity costs in the systems.
The key research objectives are to seek the best packaging management
option for a global automotive company considering the financial performance.

4

Independent variables include packaging types (expendable and reusable shipping
containers), ownership options (buying and rental), and other constraints (shipping
distance , average daily volume, container weight, container quantity on a pallet,
return rate, backhaul logistics volume factor, designed container life, buffers and
safety stocks, cycle time, customs charges).
Two simulation methods are applied to compare costs of reusable packaging
systems to single use expendable packaging systems for global supply chains. The
first is a static, multivariate regression approach similar to Mollenkopf et al. (2005).
The second is a dynamic, discrete-event simulation method which can better
visualize constant changes of container flows.
This multi-disciplinary approach, combining packaging and supply chain
management, is based on scientific, technical, economic and environmental aspects.
The results reveal differences in total costs for packaging, supply chain operations,
waste and externalities. The resulting dynamic simulation is intended to be scalable
for use in other industries which consider implementing reusable packaging systems
for their global supply chain.

5

CHAPTER 2
LITERATURE REVIEW
The literature review focuses on the purposed research objectives:
comparing the financial and environmental performance between reusable and
expendable shipping container systems for global supply chains. It is organized by
four sections.
The first section is about the relationships between packaging functions and
supply chain costs. It highlights importance of extended packaging functions such
as sustainability and standardization, and shows how these functions affect
packaging decisions.
The second section summarizes previous research on reusable versus
expendable packaging shipping container systems. Findings regarding the
advantages and disadvantages of these two systems are compared, and limitations
of the research methods are discussed.
The third and fourth sections introduce theories and methodologies for
activity-based costing and dynamic simulation.

General concepts, approaches,

limitations and rationales for the proposed study are explained based on the
previous studies.
The results of literature review support the need for the proposed study and
provide several pieces of information as a way to summarize the literature.

In order

to identify the true costs and benefits of a returnable packaging system in the global
supply chain, relationships between packaging and supply chain should be viewed
from holistic approach. Important criteria for the packaging decision making
process such as initial costs, environmental effects, ownership and standardization
are discussed, and these will be key variables for the study.
6

The literature review justifies the methodological framework to develop the
proposed cost simulation model. Few researchers in the packaging field have used
this combined approach, and so the literature review on these two parts is allocated
to explain the concepts and general processes.

Results of literature review leave

important questions unanswered, which will be the objectives of this study.

The

limitation of the available literature proves the way for the creative work to be done
for this research.
2.1. The relationships between packaging and supply chain
2.1.1. Function of packaging in supply chain
Packaging is the basic unit of logistical activities and its influence on supply
chain efficiency is significant (Twede, 1992). Packaging plays major role in the
operation of efficient and effective supply chains.

As a key element of a supply

chain, packaging not only protects the product from physical damage, but affects
every logistical activity, such as transportation, freight handling, packing,
warehousing, waste disposal and information management. Changing a packaging
type, size, and operation methods can significantly affect overall supply chain
efficiency in terms of economics and sustainability.

Packaging managers and

engineers need to make sure that a packaging system must meet required functions
in the supply chain without sacrificing its primary goal, protecting a product.
The efficiency of material handling during supply chain processes is greatly
influenced by package design, unitization and communication characteristics. The
weight, volume, and fragility of industrial packaging determine transportation and
material handling requirements and efficiency of overall logistical system
performance (Bowersox, Closs, & Cooper, 2012).

7

Positive impact of proper packaging on supply chain can be found from
numerous case studies.

For example, a smart label that utilizes active radio

frequency identification (RFID) technology can improve product traceability
(McCartney, 2006). IKEA’s “flat pack” furniture system significantly reduces transport
cost and helps to take significant strategic advantage over other competitors (Rundh,
2009).
Kumar, DeGroot and Choe (2008) surveyed various US hospitals and
concluded effective packaging design and packaging management significantly
impact the supply chain cost for the health care industry. They emphasized that a
more efficient and cost effective supply chain can be achieved with improved
logistical packaging designs and collaboration with packaging suppliers, but there
are no main drivers to the change. According to their estimation, improved
packaging management can reduce the 1 million US dollars spent on waste disposal
each year.
It is no wonder that some researchers insist that, for more effective
packaging design for supply chains, packaging should be developed during the
product development stage, so it does not restrain possible logistics improvements
and cost saving opportunities (Klevas, 2005).
Packaging, as a function of supply chain, affects multiple supply chain
metrics and is influenced by various supply chain activities such as freight handling,
packing, warehousing and waste management.

In order to explore the impact of

packaging on a supply chain, the first step to understand interactions with a supply
chain and identify the measurable performance metrics.
Hellstöm and Saghir (2006) focused on the interactions between the
packaging and retail supply chain process and observed how each packaging

8

function affects each logistics process.

They found out that increasing

standardization reduces handling costs, transport equipment costs and vehicle
waiting time for loading and unloading, while increasing modal choices for shippers.
They identified packaging activities in a retail supply chain and explained how retail
supply chain processes are related to the each packaging activities, but they did not
provide specific metrics for measurement.
Blanck (2008) discussed how the packaging dimension and weight ultimately
affect distribution environmental impacts and energy use. For packaging
optimization, he identified five key packaging performance drivers: packaging size,
shipping weight, shipping densities, damage and reusable packaging.
Kye, Lee, & Lee (2013) discussed the perceived impact of packaging
logistics on the efficiency of freight transportation (EOT) by developing a conceptual
model with seven hypotheses based on previous literatures. For factors such as
box modularity, palletization, returnable system, and information system were tested
if these factors impact on EOT. This study only provide theoretical results and a
statistical model based on a survey, therefore further development is necessary.
From the supply chain perspective, performance and optimization have been
a long time issue and many measurable performance metrics and measuring tools
have been introduced and applied in the logistics industry. A supply chain is a
network of relationships among trading partners (Lockamy III & Smith, 2000) and
consists of integrated activities between functions and companies (Hoek, 1998). A
number of researchers have explored these interactions and performance
measurements.
Andersson et al. (1989) reviewed previous research and proposed a model
that separates the measures into internal and external logistics performance.

9

Key

elements for internal logistics performance measurement consist of logistical
performance results compared to budget including inventory value, capital cost,
turnover rates, productivity, internal lead times, etc. For external performance, they
investigate the key performance indicators between units in the company, between
the company and the customers, and between the company and the suppliers. As
shown in Table 1, they suggested key elements of performance measurement that
can be compared for the overall logistics performance versus the performance of the
entire company.

Table 1: Key elements of performance measurement of logistics
Areas of Interest

Key Elements of Performance Measurement

•
-

Internal performance within the units

External performance between the
different units in the company
External performance for the entire
company towards the customers

Supplier performance towards the
company:

The relation between the logistics
performance and the performance of
the entire company

Result vs. budget (logistics costs)

•
•
•

inventory value, capital cost
turnover rates
productivity
internal lead times
Availability (lead time and/or service level)
Reliability (quality and timing)

Customer service elements (availability, reliability,
lead times, etc.)

•
•
•
•
•
•

Turnover
Quality
Reliability
Lead time
Price
Result vs. budget

-

Return on assets
Total turnover rate
Total value in stock
Total capital cost

-

Ronen and Boaz (2005) introduced the six key global supply chain
performance measurements defined in Table 2. These six are typical

10

measurements for supply chain metrics, but definitions vary depending on the
purpose of the study.
Each supply chain performance metric is an element for performance
measurement and a packaging and packaging system can impact each element. In
this research, key supply chain performance metrics affected by packaging will be
identified and used for estimating overall efficiency and cost calculations.
Table 2: The six key global supply chain performance measurements
Performance
measurements
Throughput
Operating Expenses
Inventory
Lead Time
Quality

Due-date performance

Definitions
The cash flow generated by actual sales (total sales minus refunds and
cancelled transactions, etc.)
The total fixed expenses of the organization at the measured period.
(Direct labor, indirect labor, rent, machine maintenance, etc.)
Inventory costs of raw materials, work-in-process and finished goods (Note:
The three inventory types are measured only by the costs of raw materials
with no further allocation of costs.)
Response time from the customer’s perspective
Each organization’s own measurement of quality such as percentage of
defects, percentage of products returned by customers, non-conformance
quality costs etc.
The organization’s ability to adhere to its quoted delivery schedule.

2.1.2. Packaging cost in a supply chain
The goal of a business is to maximize profits, and profits are revenue after
subtracting costs. In current competitive business logistics, reducing costs is often
easier than increasing revenue to maximize profit. Without comprehensive financial
analyses, business decision makers find it difficult to identify opportunities for
improvement throughout an entire logistics system (Holmes, 1999). However, there
has been little research to model and measure the performance and cost of
packaging in a supply chain.

Most studies have not successfully reflected the

actual industry situation.

11

Twede (2009) argued that, although the importance of packaging in supply
chains has been widely addressed and financial performance measurement of
packaging have been discussed for decades, the total cost and value of packaging
have not been successfully estimated.

Most models and metrics (where there has

been measurement) are focused on specific supply chains and a particular point of
view.
The lack of the ability to quantify packaging value and measure its costs
usually results in the total cost of packaging being neglected disappearing in the
supply chain cost. Azzi et al. (2012) argued although its impact on supply chain
costs and performances can be overwhelming, packaging activities are often
perceived as a cost rather than a value added activities.
Most companies are not aware of the importance or nature of packagingrelated costs, so they often fail to include important logistical activity costs in their
total packaging cost estimations (Mollenkopf, et al. 2005). Limited resources and
the lack of reliable packaging cost information make it difficult to make packaging
management decisions (Dubiel, 1996). Dubiel (1996) concluded that many
companies are not aware of the importance of packaging costs and do not attempt
enough to discover potential cost saving options by separating packaging costs from
prime cost activities such as logistical process. He pointed out that companies do
not have enough knowledge of type of packaging cost, how to calculate the true
packaging cost, and how to separate prime cost (such as manufacturing cost) from
packaging cost.
The perception of total packaging cost in industry largely depends on a
company’s own self-interest. For example, Table 3 shows packaging cost criteria

12

from several packaging suppliers’ point of view: NEFAB USA (2010), John Henry
Packaging Group (2010) and Security Packaging (2010)

Table 3: Examples of packaging cost criteria from packaging suppliers
Packaging cost criteria

Company

•

Costs beyond materials and machinery are not aggregated or
reported

•
•
•
•
•
•
•
•

Cost of filling and handling

•
•
•
•
•
•
•
•
•
•
•
•
•

Aesthetics (e.g. shelf appeal)

NEFAB USA (2010)

Repacking through supply chains
Waste disposal/recycling costs
Externalities like pollution and resource depletion
Product cost
Packaging Inventory
Obsolescence (e.g. packaging scrap)
Packaging distribution methods (e.g. shipping distance,
delivery service, etc.)

John Henry Packaging
Group (2010)

Quality
Service (e.g. meeting peak order cycle)
Purchasing
Cost of raw materials
Direct Labor
Indirect labor
Material movement

Security Packaging
(2010)

Warehousing
Waste management
Overtime
Quality control
Machinery operation

Although these companies are all suppliers, NEFAB USA and Security
Packaging tend to emphasize material costs and physical distribution costs while
John Henry Packaging Group considers more about service and marketing costs.
Many companies often fail to include some of important logistical activity costs in
their total packaging cost estimations.

13

While calculating total packaging costs is generally subjective and some cost
metrics are very difficult to quantify, identifying measureable packaging cost metrics
and maintaining consistency are very important when it comes to comparing the
impact of different packaging systems on a supply chain. Because of strong
interactions of packaging and supply chain activities and functions, the impact of
different packaging systems influence the performance metrics of the supply chain
and vice versa.

Not all performance metrics for supply chain are significantly

influenced by packaging performance, but key evaluation factors will be identified for
this study.
2.2. Reusable versus expendable packaging
This section of the literature review will focus on decision factors for choosing
a reusable or expendable packaging system in a supply chain. Based on published
studies, three factors including cost, ownership and standardization are examined
and discussed.
2.2.1. Cost aspects
Aside from the environmental benefits, several companies have found that
reusable packaging can be a profitable logistical solution. Reusable shipping
containers can improve a company’s supply chain management. Manufacturers have
looking for more cost effective and geometric options such as collapsible or nestable
features of containers.

Potential advantages of reusable packaging operations

include the following:

•

Reduces packaging waste, improve product protection and cut logistical operation
costs by improving cubic efficiency for transportation and storage (Mollenkopf et al.,
2005)

•

Reduces packaging costs and environmental impacts (Silva et al., 2013)
14

•

Reduces labor costs (Holmes, 1999)

•

Reduces costs, shorter lead times, and better product quality with implementation of
ISO 14000 standards (Hanson, 2004)

•

Applies standard and ergonomic design of reusable containers that can reduce or
eliminate multiple packing and repacking processes which create unnecessary
complexity of distribution network, additional handling and material costs and
increase lag time (Modern Material Handling, 2006)

The vehicle assembly industry has been the leader in reusable packaging use
during the past two decades. In 1995, John Deere & Co. invested $20 million in a
reusable packaging system. Global automotive companies such as Ford, GM and
Toyota have applied returnable container systems successfully (Kroon & Vrijens,
1995).
Besides the initial financial investment of purchasing reusable packages,
operating a reusable packaging system increases transportation costs for returns,
management costs for tracking, cleaning, sorting and storage space (NEFAB USA
201; Mollenkopf et al., 2005; Twede 2004).
Although switching from expendables to reusable packaging containers has
been a trend in some industries, there is no standard method of total cost estimation.
Cost categories and the amount of details for packaging costs differ by researcher
and the purpose of the study. Researchers exploring the supply chain effect of
reusable packaging compared to expendable packaging have modeled different
costs and activities although most cost research regarding reusable packaging
management and cost evaluation is limited to material (packaging) and handling
associated costs within simple logistical networks or domestic distribution. Since it
is so difficult to estimate true packaging costs, and most companies do not have

15

sufficient and reliable packaging cost information, it is difficult to make with decision
whether they should switch from expendable packaging to reusable packaging.
Holmes (1999) summarized key criteria to consider reusable packaging
operation for a company.

Comprehensive financial analysis is the most important

step to consider reusable packaging system. The decision makers need to identify
opportunities for improvement throughout entire logistics system. The capital
investment is significant, so this would not be possible without sound communication
with key players in logistical chain including senior management staff and
stakeholders.
Rosenau et al. (1996) outlined several cost factors that differentiate
returnable packaging from expendable packaging. The Net present value (NPV)
financial evaluation method is recommended because returnable packaging should
improve logistics profitability comparing expendable packaging.
Mollenkopf et al. (2005) used relative cost approach to compare reusable
and expandable packaging case study based on GM powertrain. Key metrics
applied were container unit cost, cycle time, pack quantity, delivery distance, daily
volume, average daily volume and peak volume. Although this research was limited
to simple physical distribution of a set of automotive parts, it used a practical
calculation method by simplifying cost factors of packaging and distribution activities
(Mollenkopf et al., 2005). As shown in Table 4, they analyzed transportation, labor,
recycling and disposal costs to compare packaging costs for automobile suppliers.
Honaker (2000) identified that the most important cost drivers to manage for
reusable packaging system are cost per use, returnable packaging asset utilization,
and average days in cycle. He considered “cost per use” to be the most important
metric because this represents the total cost accumulated from the all activities

16

associated with a supply chain. “Returnable packaging asset utilization” is related
to the productivity of returnable packaging containers in the system and a
measurement of utilization of containers. The “average days in cycle” relates to the
total amount of time required for the complete rotation of the container.

Table 4: Comparison of reusable and expendable packaging costs
Reusable packaging costs
1.

2.

Transportation cost
Base transportation rate
Delivery distance
Frequency of supply
Average daily volume
Discount rate for return
transportation (R)
Number of stops (R)
Stop-off rate (R)

4.

Transportation cost
Base transportation rate
Delivery distance
Frequency of supply
Average daily volume
Discount rate for return

5.

Labor cost

•
•
•

Labor cost

•
•
•
3.

Expendable packaging costs

Time to handle container
Labor rate

6.

Pack quantity

•
•
•
•
•
•

Recycling rate per pound
Container weight

7.

Pack quantity

Cycle time (R)

Pack quantity

Disposal rate (E)
Container weight (E)
Pack quantity (E)

Recycling revenue

•
•
•

Working days per year (R)

Labor rate

Disposal cost

•
•
•

Recycling revenue

Time to handle container

Recycling rate per pound
Container weight
Pack quantity

Container life (R)

※ Note: R: Reusable container only, E: Expendable container only.

Some industrial calculation models have been developed to compare the
basic cost differences of one-way corrugated packaging verses reusable plastic
packaging. The “Quick Economic Calculator” is a cost comparison tool developed
by the Reusable Packaging Association (RPA) (2010). The model uses basic
assumptions and requires users to input various cost components into the model,
such as purchase price, dwell time, annual number of packages shipped per year,
returnable container cost per use, costs related to return containers, possible
17

savings from packaging waste costs and labor costs. It is noticeable that this
calculator does not include benefits of standardization by using returnable containers
such as easier load, unload and better cube utilization. While the model uses
factual inputs from prospective users, it is intended to be directional and not an
absolute indication of exact cost benefits for an end-user.
Palsson et al. (2013) developed a theoretical evaluation model for the
comparison of one-way and returnable packaging system used for the automotive
part packaging and compared the environmental and economic impacts of two
different packaging systems. They selected five environmental and six economic
criteria and evaluated two packaging systems by calculating CO2 emissions and
costs. They provided very practical way to evaluate sustainability of a packaging
system in a particular supply chain, but this paper was limited itself by taking only
one case study and could not represent complexity of the international supply chain.
Dubiel (1996) insisted that the first step to compare the cost and performance
comparison between reusable and one-way packaging systems is to separate the
packaging process. He broke down packaging costs into ten categories as shown
in Table 5. The primary categories are materials, machines, transport, storage,
reusable systems, building, handling, resulting, waste disposal and other
miscellanies, but the list and categories can be extended depending on
organization’s specific circumstances.
Dubiel (1996) compared cost types and structures of reusable, expendable
packaging in cyclic system and one-way packaging.

Expendable packaging in

cyclic system includes costs of recycling, disposal, packaging management and
redistribution, and one-way packaging is not.

18

Table 5: The breakdown of reusable packaging costs
Cost category

Detail costs

Cost category

Detail costs
※ Allocated write-offs for buildings

※ Cost for packaging material
Material cost

※ Cost for packages
※ Cost for packaging accessories

Cost for machines,
appliances and
tools (for
manufacturing of
packages and
packing)

※ Allocated write-offs for
machines
※ Allocated interest charges

Cost for
buildings

※ Allocated interest charges
※ Rent
※ Energy cost for light, heating and air
conditioning
※ Cleaning cost

Transport cost
(distinction
between internal
and external
transports)

Handling cost

※ Energy cost
※ Maintenance cost

※ Labor cost for packing

※ Allocated write-offs
※ Allocated interest charges
※ Labor cost

Resulting cost

※ Energy cost
※ Freight

Cost (additional)
for reusable
systems

※ Cost for redelivery , repair and
delayed deliveries
※ Cost for settlement of damages
※ Cost for losses
※ Collection cost
※ Sorting cost

※ Allocated write-offs for
warehousing
Storage cost

※ Labor cost for manufacturing
packages

※ Allocated interest for
warehousing
※ Allocated interest for stored
goods
※ Labor cost

Waste
disposal cost

※ Return cost
※ Recycling cost
※ Waste disposal cost (deposition,
incineration)
※ Management cost

※ Allocated interest charges for
circulating packages

※ Labor cost for controls

※ Cleaning cost
※ Repair cost

Other cost

※ Cost for breakage and rejects
※ Insurance cost/premiums
※ Allocated risks

※ Deposit fees

As shown in Table 6, reusable packaging requires additional costs for
management such as capital lockup, repair, and cleaning compared to one-way and
expendable packaging in cyclic system. However, allocation of costs and cost level
for management varies depending on the particular system and management.
example, Dubiel did not include the cost for recycling and disposal for one-way
packaging, but this cost cannot be ignored nowadays.

19

For

Table 6: Cost types and structure of reusable systems (modified from Dubiel
1996)
Reusable
packaging

Types of costs

Expendable
packaging
(in cyclic
system)

One-way
packaging

Cost of capital lockup
Cost of repair
Cost of cleaning
Cost of recycling/disposal
Cost of administration (packaging
management)
Cost of redistribution
Cost of carry (cost of damage, loss etc.)
Cost of transportation and distribution
Cost of handling
Cost of Store for packaging material
Cost of production (machines, facilities etc.)
Purchase cost for packaging materials

※ Note: The graphs in this table are only for representing type of costs and do not indicate amount of
costs.

Kim, Glock and Kwon (2014) developed a stochastic returnable transport
items (RTI) inventory model for a closed loop supply chain of a perishable product
consisting of a single supplier and a single buyer. They examined three different
cases depending on the stochastic lead time of RTIs: a) RTIs are returned early, b)
RTIs are returned late, c) RTIs are returned late and shortages occur.

They

considered the following types of costs: inventory cost of keeping finished products
and RTIs at the supplier and the buyer, cost of deterioration, shortage cost, and
setup and ordering costs.

The results of mathematical simulation showed that the

longer lead time cause higher probability of large back orders by buyer and may lose
competiveness of the supply chain.
Although outsourcing from overseas is now common practice in the
manufacturing business, none of the cost models have been applied to international
logistics operations. This is because reusable packaging containers are more likely
to be used for domestic with well-organized distribution networks. However,

20

international applications of reusable containers may be very effective depending on
the managing system and container design.
With development of international pooling networks and product/package
tracking technologies such as RFID, international reusable packaging operations
may be viable option in an increasing number of cases.

Maleki (2011) proved that

implementing automatic identification technologies can improve the management of
the returnable containers effectively.
Because of continuous increases in awareness of environmentally-friendly
supply chain practices and improvement in the efficiency of global logistics systems,
reusable shipping containers are increasingly being considered by original
equipment manufacturers and their global third-party logistics providers (3PLs). A
study on environmental sustainability performance of the major global 3PLs support
the idea, as many 3PLs generated substantial cost savings from their sustainability
initiatives.

Global 3PLs are aggressively seeking growing opportunities with their

sustainability initiatives as a “market differentiation factor” (Lieb & Lieb, 2010).
2.2.2. Impact of ownership
It should be noted if a reusable packaging system does not work properly; it
can become a very expensive expendable packaging that increases packaging
waste. As the automotive industry has realized the value of packaging system
management with by trial-and-error from the past, reusable packaging systems have
different financial and environmental effects, depending on the maturity of the
program (Twede, 2004).

The system should be managed and monitored by a pool

operator with authority and responsibility especially during the collection process
(McKerrow, 1996).

21

Designing an optimized return logistics system for returnable packaging
containers starts with several important questions such as following (Kroon and
Vrijens 1995, p. 63):
※ How and who should operate containers?
※ How many containers should be needed in the system?
※ How many and where the container depots should be?
※ What are appropriate operation (service, distribution and collection) fees?
Answers for these questions can vary depending on the ownership of the
reusable containers.
Ownership options are an important factor to be considered because
effectiveness of the reusable container application is largely dependent on efficiency
of the managing system. Ownership (whether supplier, receiver (customer) or 3PL),
influences a range of factors including the respective bargaining power of the parties
involved, compatibility with production systems and the respective logistical
capabilities of suppliers/receivers (Holmes, 1999).
McKerrow (1996) used the term “equipment pool” for any interchangeable
and reusable packaging, and compared the five types of ownership: manufacturer,
customer, joint, common and third party. Examples are shown in Table 7.

Table 7: Types of ownerships of reusable packaging
Types

Ownership

Manufacturer owned

A manufacturer

Customer owned

A receiver
An industry association
or independent body.
A group of companies
or cooperation
An independent third
party

Jointly owned
Commonly owned
Third-party owned

22

Examples
Tightly closed loop system such as between
a glass manufacturer and a bottling plant
Some automotive assemblers
EURO pallet pool system
The Dutch Auction pool which is owned by
co-ops of growers or fishermen
CHEP pallet pool, IFCO fresh produce crate
pool

Holmes (1999) examined three ownership arrangements (supplier, receiver
and third-party) which are affected by various factors such as bargaining power of
the parties involved. Although he identified some advantages and disadvantages
for each of the three ownership options in Table 8, he explained that ownership
decisions depend on negotiations and different circumstances.

Table 8: The advantages and disadvantages of the returnable container
ownership options
Ownership
types

Advantages

Disadvantages

Supplierowned

- Reduced costs to supplier and customer
- Supplier may enhance customer loyalty by
reducing waste and management problems
- Supplier can optimize logistics efficiency by own
way

Receiverowned

- Greatest potential to achieve financial benefits
- Receiver can optimize logistics efficiency by own
way

Third-partyowned

- No initial capital costs needed
- Tracking and maintenance assured by contractor

- Supplier bears Initial capital
costs, tracking and
maintaining costs
- Receiver bears initial capital
costs, tracking and
maintenance costs – higher
risk
- Reduced potential for
savings

Dubiel (1996) viewed that finding a right decision is depending on
technological suitability, meeting of ecological and legal requirements, and ultimately,
the costs. He explained advantages and disadvantages for each of the three
reusable systems: individual, bi/multilateral and pool as shown in Figure 1.
The individual system does not use standardized reusable containers and
works only between senders and customers, while the bi/multilateral and pool
system can exchange standard reusable containers more freely.

For pool system,

because an outsourced pooling company controls containers, forward and return
logistics are simpler than other systems.

23

Load carrier

Indi
vid
ual

Logistical Chain

Sender

Loaded
boxes
Empty boxes

Customer

Sender

Loaded
boxes
Empty boxes

Customer

Individual
- Size
- Form
- Color
- weight

Load carrier

Bi/m
ultil
ater
al

Standard
- Size
- Form
- Color
- weight

Individual Logistical Chain
Loaded
boxes
Empty boxes

Sender

Customer

Empty boxes

Empty boxes

Empty boxes

Loaded boxes
Sender

Customer
Empty boxes

Load carrier

Poo
l
Syst
em

Standard Logistical Chain

Standard
- Size
- Form
- Color
- weight

Sender
Loaded

Empty boxes

Empty boxes
Pool company

Empty boxes

Customer

Figure 1: Organizational levels of reusable systems
Kroon and Vrijens (1995) summarized return logistics systems in the
Netherlands into three types depending on responsibility of returnable container
owner shown in Table 9.

They find that most logistics operations including

distribution, collection, cleaning and maintenance are handled by the 3PLs (Kroon
and Vrijens 1995, p. 61).

24

Table 9: Return logistics systems in the Netherlands
System

Essence

Switch pool

Every partner has
an allotment

Partners
Sender, recipient
Sender, carrier
and recipient

Responsibility

Every partner is
responsible for his
own allotment

Possibilities
Direct switch
Exchange-perexchange switch
Transfer system

With return
logistics

Return logistics
by agency

Agency, sender
Agency

Depot system
with booking

Carrier, recipient
Depot system
with deposit

Without return
logistics

Rental of the
containers

Agency, sender

Sender, also for
the return logistics

Rental of the
containers

From the perspective of 3PLs, Hofmann (2009) pointed out lack of studies in
supply chain. The role of 3PL is particularly important in the international operation
of returnable packaging systems, but this has not been considered because there
have been lack of systems and participating partners. This has been a major
disadvantage for reusable packaging systems in growing international trade.
In some cases, RPCs owned and controlled by 3PLs may lead to a significant
reduction in international logistical activities such as extra handling, packaging waste
and purchasing costs. For example, Eroski (Euro Pool System, 2010), a Spanish
supermarket chain which has a partnership with Euro Pool System, reported the
significant growth of the number of circulations of reusable crates for their fresh
products from 250,000 to 55,000,000 per year between 1998 and 2009. However,
it is still remained unknown how a 3PL-operated RPC system can contribute to a
company in terms of profitability, sustainability and efficiency of international supply
chain.

25

2.2.3. Impact of standardization
A primary requirement for the successful use of reusable containers is
standardization of containers.
enable supply chain integration.

Standardized packaging sizes, materials and weights
Standardized packages facilitate the automation of

conveyor flow, increase efficiency of inventory control, and reduce purchase costs
(Bowersox, Closs, & Cooper, 2012).
The success of Eroski Co. confirms that the standardization of the packaging
ensures efficient order picking and low purchasing costs.

Eroski Co. claimed that

an established standardized and returnable packaging system can also contribute to
their future supply chain plan, automatic pick system (Euro Pool System, 2010).
Standardization of packaging can significantly reduce supply chain
management cost by establishing efficient unit load systems. Unit load system
affects every distribution element such as transportation, storage, packaging,
shipping and handling, and is a key cost driver of 12 to 15 percent of retail sales
price (A.T. Kearney, 1999).
Standardization of pallet and packaging is the first step for efficient and
seamless unit load systems, but no universally accepted pallet dimensions exist.
Pallet dimensions vary depending on logistical environment and history of countries
and industries, but a few different dimensions are widely used.

Table 10 shows

typical pallet dimensions and region most used in (Clarke, 2003).

26

Table 10: Typical pallet dimensions
Region most
used in

Industry most used in

North
America
(by typical
industry)

Grocery, many others

Europe, Asia
Europe

Dimensions
mm (W x L)
1219 x 1016

in (W x L)
48 x 40

Telecommunications, paint
Drums

1067 x1067
1219 x 1219

42 x 42
48 x 48

Military, cement

1016 x 1219

40 x 48

Chemical, beverage

1219 x 1067

48 x 42

Dairy
Automotive
Drums, chemical

1016 x 1016
1219 x 1143
1118 x 1118

40 x 40
48 x 45
44 x 44

Beverage
Beverage, shingles, packaged
paper
Military 1/2 ISO container, fits
36" standard doors
Retail

914 x 914
1219 x 914

36 x 36
48 x 36

889 x 1156

35 x 45.5

1219 x 508

48 x 20

Similar to 48x40", ISO2
Fits many doorways, ISO1
ISO0, half the size of EUR

1000 x 1200
800 x 1200
800 x 600

39.37 x 47.24
31.50 x 47.24
31.50 x 23.62

600 x 400

23.62 x 15.75

Quarter the size of EUR
One-eighth the size of EUR
Asia
Australia

Japan, Korea
Fits for Australian Railway

400 x 300

15.75 x 11.81

1100 x 1100
1165 x 1165

43.30 x 43.30
45.87 x 45.87

Although several pallet standard dimensions are recommended and actively
discussed in the International Standard Organization (ISO), packaging standards
have not drawn much attention. The 600x400mm master module based on a
1200x1000mm pallet is the only dimension that the ISO has accepted (International
Organization of Standardization, 2012)
Although this module is widely accepted by European and the US, some
Asian countries have a different packaging module dimension as result of different
national pallet standards, 1100x1100mm. For example, based on the fact that the
area dimensions of standard pallets for the unit load system in Korea are
1100x1100mm and 1200x1000mm, the footprint size of 600x500mm has been
advocated as the standard packaging module. This module is beneficial when
27

several different sizes of packages need to be stacked together on a pallet as well as
improving the exchange process of different sizes of pallets during the international
shipping and handling. A new standard packaging module could improve
dimensional integrity in the various international distribution environments (Kim, Lee,
& Lee, 2009).
Pereira (2008) emphasized the important of packaging dimension standards.
He studied the modular packaging system for fruit and vegetables and found out that
two major factors, packaging standard sizes and the ability to interlock, were most
important to improve stability and security of loads. He recommended a pallet
standard size of 1,000mm x 1,200mm and divided it into modules 600mmx400mm,
400mm x 300mm and 300mm x 200mm (Pereira, 2008).
Peres (2008) recommended using two basic foot prints for packaging
dimension standardization: 600mm x 400mm and 400mm x 300mm. He pointed
out that the vital element of the total cost of the packaging is not simply the cost of
the containers, but the cost involved in the supply chain systems. For example, in
the US, a reduction of 14 percent in the cost of transporting grapes, and of 9 percent
in the cost of oranges, is expected if distribution systems improve cube utilization
throughout the supply chain.
Global automotive companies are considering standardization of pallet and
packaging dimensions because this can eliminate unnecessary packing and
repacking processes. Although U.S. automotive companies have well established
returnable packaging systems, which use the basic footprint size of 48 x 45 inches,
these have been a major obstacle for efficient global logistics. Due to different pallet
and packaging footprints, many costly activities such as transferring from one pallet
to another only increase overall logistics costs and decrease efficiency of logistics.

28

Recently, AIAG (American Automotive Action Group), a globally recognized and
opinion leading organization of automotive OEMs and suppliers formed a working
group to establish a global pallet footprint and recommended footprint of 1140 x
980mm pallet as the global standard pallet for automotive industry (Automotive
Industry Action Group, 2010).

The importance of packaging standardization in

supply chain is currently regarded as one of the biggest issues in automotive
logistics among industry experts (Automotive Logistics, 2010).
Furthermore, global standardization of pallets and packages can increase
business opportunities for 3PLs like CHEP, Goodpack and iGPS. For example,
CHEP, a multi-national pallet rental company, recently launched a global container
and IBC pool business. Global pallet and packaging rental companies which already
established their own pallet and packaging standards will need less investment while
having more efficiency to run pool systems if packaging, pallets and other logistical
means are globally standardized.
However, the trade-offs of standardization of packaging in logistics should
not be ignored. Although standardized reusable packaging could fulfill logistical
requirements and work well in marketing and environmental perspectives, replacing
current transport packages requires major investments in packages and in the
distribution network.

For many cases, “all-embracing integration” concept can be a

problematic and difficult to implement in real world. Adaptability and constraints of
packaging standardization differ depending on companies and industries, for this
reason, possible trade-offs of standardization must be carefully considered (Jahre &
Hatteland, 2004).
Packaging standardization is a crucial element to improve efficiency of
returnable packaging system in a supply chain and can help to integrate a supply

29

chain. For international supply chains in the automotive industry, standardized
reusable packaging can facilitate smooth and integrated packaging and logistics
interfaces from suppliers to assembly plants. Hence, impact of packaging
standardization on international automotive supply chains should be identified and
financially studied.
2.3. Activity-Based Costing (ABC) in logistics
In order to determine packaging costs in a supply chain, it is important to
understand that packaging not only consists of materials, but is also associated with
many activities such as packing, unpacking, filling, palletizing, etc. Many activities
are directly or indirectly mixed with supply chain activities and these are very hard to
convert to financial terms, so Activity-Based Costing (ABC) concepts are used for
this research.
Traditionally, supply chain management was viewed as a cost-generator
rather than a possible source of competitive advantage. There were no distinctive
concepts or discipline for supply chain management.

In the 1980s, the discipline of

supply chain management evolved and many researchers and companies started to
find benefits in supply chain management. However, it is still very difficult to find
the true cost of supply chain management because of different nature of the
business compared to manufacturing. Logistics activities do not just generate cost,
they also generate revenue through the provision of availability – thus it is important
to understand the profit impact of logistics and supply chain decisions.
Especially in today’s global logistics environment, it is difficult to make
decisions based on using traditional cost accounting methods alone because it limits
or distorts the true financial performance in an unstable and unpredictable market
with a larger portion of indirect variable costs.

30

In modern logistics process, overhead and indirect costs are a larger
component of the overall cost structure than direct costs due to an increased
regulatory and environmental rule compliance; wider customer base and subsequent
delivery channels; new and more complex technologies; and proliferation of product
lines. (Kosior & Stron, 2006)
Traditional cost accounting systems such as a volume based cost system,
overhead and indirect costs would be allocated to a job or function based on
direct labor hours, machine hours, or direct labor costs, that leaves business
decision make harder. Traditional approaches to accounting based upon full-cost
allocation can be misleading and dangerous – and it would be impossible to
calculate true packaging and logistics costs. It is no surprise that ABC methods
have drawn interest from various industries because ABC aims to identify the cost
attached to each level of activity (e.g. cost per line item picked, cost per delivery,
etc.).

ABC can present a clearer picture of the true packaging and logistics costs.
Lack of knowledge of logistics costs leads to businesses making uninformed

decisions. From the logistics cost survey by Supply Chain Digest (2006), “40
percent of respondents said their primary measure of logistics costs is as a percent
of sales. This compares with 25 percent who said the primary measure was in
absolute cost, 16 percent who said it was cost by some unit of weight (hundred
weight, kilograms, etc.), 11 percent who said it was cost per some unit measure
(case, unit), and only 8 percent who said they used “activity-based costing” as the
primary measure.” Based on the report, logistics costs could be distorted
depending on each reporting method, and there is no way to compare costs of
logistics among companies directly.

31

Today’s competitiveness of the global market increases the need for ABC
costing systems.

In the past, although traditional costing systems might generate

incorrect costs and profit data, companies could make up or hide their mistakes
because products or services with bigger margin of profits could compensate for the
less competitive products or services. Now, in the world where margin of error is
slimmer and the market is more competitive, knowledge of real costs of the products
and services is becoming the key to company survival (Themidol et al., 2000)
2.3.1. The general concept of ABC
Activity-based costing (ABC) has been introduced as more reasonable cost
accounting method in order to find and measure more accurate and realistic cost
allocations in a company. While traditional accounting methods allocate indirect
costs to direct costs or direct labor costs, ABC attempts to turn overhead (indirect
costs) into direct costs based on the number of activities related to the products
(Varila et al., 2007).
The American Institute of Management Accountants defined ABC as follows;
“A methodology that measures the cost and performance of activities, resources and
cost objects, assigns resources to activities and activities to cost objects based on
their use, and recognizes the causal relationships of cost drivers to activities”
(Themidol et al., 2000, p1149). Themidol et al. (2000) summarized the terminology of
ABC method as follows:
a. Activities: tasks or sets of tasks that require the consumption or
utilization of resources and result in the completion of a specific service,
or in the physical transformation of a product from one state to another;
b. Cost object: the final good or service created as a result of the
performance of an activity or of a chain of activities

32

c. Resources: the ingredients required for the production of a good or of a
service. They are referred to, in their most basic form, as labor, material,
and capital
d. A cost driver: a variable that demonstrates a logical and quantifiable
cause and effect relationship between the utilization of resources, the
performance of activities, and the final cost object(s). ABC utilizes a
multi- step cost assignment approach, in the first step; the resources
consumed in the performance of activities are assigned to activities
using “resource cost drivers.” In the following steps, resource costs
accumulated within the activity centers are assigned to the final cost
object(s) using “activity cost drivers. ”For simplicity, many use, instead of
resource cost drivers and activity cost drivers, the terms resource driver
and activity driver, respectively;
e. Operational cost drivers or cause of cost: those variables that determine
the workload and hence explain why activities are performed. Inasmuch
the cost object is considered the end of the trail, the operational cost
driver can be viewed as the start of the trail
f.

Cost object: the target of cost activity performance such as including
products, service and customers.

ABC can help a company to identifying resource allocations, labor-cost
based costing and value added activities although it has some limitations. The
advantages and disadvantages of ABC versus its traditional counterpart are
summarized in Table 11

33

Table 11: Advantages and Disadvantages of Implementation of ABC
Advantages
(1) ABC has helped firms across the world to
become more efficient and more effective;
(2) ABC provides a clear picture of where
resources are being spent, customer value is
being created, and money is being made or lost;
(3) ABC offers a better alternative to labor-cost
based product costing;
(4) ABC identifies value-added activities;
(5) ABC identifies many activity costs that are
not related to production at all but are
traditionally allocated to products as production
cost. On the other hand, it identifies many
marketing, selling and administrative costs that
should be included to determine better product
pricing estimates;
(6) ABC indicates the areas where the change
in firm operation to reduce costs will allow the
firm to satisfy customer demands better;
(7) ABC helps retailers with dual channel firms
such as a combination of online and counter
selling operation to identify how much they
spend on marketing and other functions and
where the costs should be allocated;
(8) ABC eliminates or reduces non-value added
activities;
(9) ABC allows for the pursuit of competitive
advantages to many firms through the
identification of relevant cost drivers and
activities.

Disadvantages
(1) It is a resource-consuming activity. It
is costly for the firm to adopt ABC
because of the cumbersome accounting
changes involved.
(2) It is time-consuming due to the
lengthy procedures it entails. It takes
time for adjustment.
(3) It is not appropriate for every firm;
typically, firms with low overhead costs
will not benefit from adopting this system.
(4) It is a labor-intensive operation.
(5) The benefits from the implementation
of ABC are not always easy to define.
(6) It may cause poor labor relations in
the firm if people are not willing to buy
into this concept or not willing to break
from the status quo.

Although an ABC method appears to be very sound in theoretically, it has
limitations to implementation in actual practice due to complexity of its operations
and difficulty to maintain. Based on the survey conducted by Management
accounting quarterly (Stratton & Lawson, 2009), the usage ranking of ABC as a
management tool has been dropped from 11th (in 1995) to 22nd (in 2002). Esculier
(1997) suggested that ABC should be only considered as “a complementary tool of
direct costing management.”
However, Stratton and Lawson (2009) concluded that the ABC method is
superior to other accounting methods for accurate overhead allocation, activity cost

34

information, and cost- and profitability measurement. It can help managers to make
more informed business decisions.
ABC cannot and does not replace traditional accounting systems and records,
but it is useful tool for managers to make more informed business decision.
Although ABC used majorly for manufacturing practices, there a number of studies
can be found in cost modeling studies in supply chain management.
2.3.2. ABC applied to reusable packaging
Traditional approaches to accounting based upon full-cost allocation can be
misleading and make it impossible to calculate true packaging and logistics costs.
This is why an ABC accounting method should be considered.
Holmes (1999) found that comprehensive financial analyses are foremost
important step to consider reusable packaging system, but it is not easy with
traditional costing systems. Traditional costing systems based on volume-based
allocation of overhead (or indirect cost) cannot give accurate and actual costing
information to managers.

This method could distort information, so managers

cannot identify the cause and effect of logistical activities and supply chain
processes.
Dubiel (1996) expressed that allocation of packaging costs within operational
accounting is problematic or impossible.

It is very difficult to achieve without

structural changes in a company’s accounting systems.
ABC methods have drawn interest in supply chain management because it
can identify the cost attached to each level of activity (e.g. cost per line item picked,
cost per delivery, etc.) by applying ABC to reusable packaging logistics, then a
clearer picture of the true packaging and logistics costs will emerge. ABC traces
the consumption of resources by identifying “activity cost drivers” which traces

35

particular cost objects such as specific products and services (Brac, 2000). These
activity cost drivers can be measured quantitatively and used to calculate total
packaging costs.
Application of ABC to reusable packaging logistics could show the
advantages of outsourcing activities to a 3PL. Stapleton et al. (2004) found that
“ABC in a supply chain setting could identify opportunities for eliminating redundant
activities existing within the supply chain, chain members with excessive resource
consumption patterns, or analyzing alternative channel structures (p. 589).” They
suggested that application of ABC in a supply chain might show that it would be
more beneficial to outsource the logistics of a firm such as third party logistics
providers.
The main elements of the ABC are resources, activities, and cost objects
(Damme & Zon, 1999). ABC traces the consumption of resources by identifying
activity cost drivers which traces particular cost objects such as specific products
and services (Brac, 2000). These activity cost drivers can be measured
quantitatively and used to calculate total product costs.
ABC model are shown in Figure 2.

36

General principles of an

Resources

Resource

Resource

drivers

cost

Activities
Process

Activity cost
assignment

Activity

Cost

drivers

objects

Figure 2: General principles of an ABC model
2.3.3. ABC process in supply chain management
Generally the ABC process for supply chains can be broken down into 7
steps as follows (Lin, Collins, & Su, 2001).
a. Analyzing supply chain functions
b. Breaking processes down into “activities”
c. Identifying the “resources” consumed for activities
d. Determining the cost for each activity
e. Determining “activity cost drivers”
f.

Collecting activity data and

g. Calculating the final cost.

37

2.3.3.1.

Analyzing supply chain functions

The first step is identifying and classifying the major processes in supply
chain functions. Major processes include packaging, material handling, order
processing, transportation, inventory, sorting, and return transportation management.
Identified processes can be further analyzed and classified into activities.
2.3.3.2.

Breaking processes down into activities

This step identifies the main activities that consume resources.

Lin et al.

(2001) suggested breaking down each logistics process into many possible activities
for better cost analysis. For example, a warehousing processing in a logistical
process may be broken down into the activities like Pick, packaging, labeling,
weighing, sorting by region (Lin, Collins, & Su, 2001).
2.3.3.3.

Identifying the resources consumed for activities

The amount of resource consumption depends on performance of the
activities. Different types of activities can result in different amount or type of
resource consumption. For example, if a company changes the Load activity by
switching from manual labor to an automated forklift truck, labor resource should be
changed to equipment resource. Most resources in a company can be categorized
into six major categories: labor, materials, equipment, facilities, property, and capital
(Damme and Zon 1999, p. 708). Table 12 is an example of activity and resource
break downs for the delivery process.

Table 12: An example of breaking down of activity and resources: Truck Drive
Process

Activity

Delivery

Truck Drive

Resources
Labor (truck driver’s salary)
Equipment (truck)
Capital (gas and oil)

38

2.3.3.4.

Determining the cost for each activity

The cost of each activity can be determined by aggregating cost of the
resources consumed for the activity, and resource consumption is measured by
resource drivers. For example, a truck Drive consumes one man hour of work, then
the resource is labor (truck driver’s salary) and a resource driver is time. It is
important to note that for some activities, particularly activities with indirect natures,
use only a fraction of resources, extensive interviews and on-site observations are
important processes to allocate more accurate resource consumption (Lin, Collins, &
Su, 2001).
Every activity cost is influenced by the activity driver.

An activity driver

determines how much of an activity is used to produce a cost object. Examples of
activity drivers are number of miles or number of packages.

For example, an

activity cost of order Pick is influenced by the activity driver such as number of cases
per order since number of cases per order requires key resources such as labor
(time) and equipment to perform the activity (Lin, Collins, & Su, 2001).

Only the

most relevant activity driver(s) should be identified and used for calculation to reduce
confusion and distortion of costs.
Some examples of warehousing and transport activities and activity drivers
are as shown in Table 13.

39

Table 13: Main warehouse and transport activities and activity drivers
Activities
Order receipt

Warehousing

Unload incoming goods
Quantity and packaging
Palletize
Check incoming goods
Put away incoming goods
Pick

Transport

Packaging and labeling
Replenishment
Load outgoing goods
Delivery to consignee sites
Empty pallets and container
returns
Unload in consignees' sites
Collection at consignor
locations
Sorting
Trucking
Booking in
Proof of delivery
Invoicing

2.3.3.5.

Activity drivers
Order volume and order source (electronic data
interchange (EDI), fax, phone, or post)
Pallets or cartons
Pallets or cartons
Quantity of cartons
Quantity and quality of supplier(including returns)
Quantity and number of returns
Number of visits to pick location and percentage of
back orders
Number of orders picked
Quantity
Quantity
Distance and square meters of pallets throughput
Space occupied and time required
Number of consignments and kind of consignee
Distance and number of collections
Number of consignments and number of cartons
per consignment
Distance and square meters of pallets throughput
Number of consignments to specific consignees
Number of consignments
Number of consignments

Tracing the costs to the cost objects

Activity drivers are usually expressed on a cost per unit basis such as a
dollar amount per activity, derived by dividing the total cost of resources used on the
activity. An example is the labor hours spent by the number of cases handled (Lin,
Collins, & Su, 2001). This cost of each activity is for an individual cost object, which
is the “object” of each activity such as products, customers and services.

The cost

object should be selected depending on the company’s decision making needs.
Table 14 shows some examples of logistics activities, resource (or cost)

drivers, resources, activity drivers and cost objects (Stapleton, Pati, et al. 2004, p.
592).

40

Table 14: Examples of activities, resource (or cost) drivers, resources, activity
drivers
Activities

Resource or
cost drivers

Resources

Activity drivers

Cost objects

Order filling

Number, size or
weight of units
shipped

Sales liaisons
employed,
computer interface
used

Sales liaison
employed to
expedite highly
preferential
customers

Product and/or
service and/or
customers

Warehousing

Number, size or
weight of units
shipped

Shelve space used,
material handlers
employed

Space needed to
store type A products

Products and/or
customers

Shipping

Number, size or
weight of units
shipped

Number of carriers
used, trucks used

Freight $ used for
transportation A
products to B
markets

Products and/or
customers

2.3.3.6.

Collecting activity cost data and calculating the final total cost.

In order to determine the total cost of an activity for an individual cost object,
the usage amount of a cost driver in performing an activity is multiplied by the unit
cost of the driver. If a cost driver of shipping cost is the number of units shipped, for
example, the shipping cost can be calculated by multiplying with the unit cost of the
driver, $/unit. The unit cost of the driver can be expressed differently depending on
cost drivers as $/order, $/hour, etc.
It is important to have a total cost approach in supply chain management
because the goal of any organization should be reducing total costs rather than
individual activity costs (Lin, Collins, & Su, 2001). Therefore, the manager should
see every possible solution and consider trade-offs that might affect total costs of the
supply chain.
2.4.

Dynamic simulation studies
Managing efficient supply chain is a challenging task for any company

considering current business trends of expanding supply chain network globally.
Dynamic simulation can be one of the effective analysis techniques for establishing a
company’s supply chain strategy.

In this section, complex nature of global supply
41

chain, characteristics and applications of different simulation techniques and
simulation studies in supply chain and packaging are introduced.
2.4.1. Complexity of Global Supply Chain
Extending supply chain globally is not a goal for a major company, but it is a
result of a company’s efforts to reduce production cost. Theoretically, the company
should achieve its main goal: increasing profits by reducing manufacturing, inventory
and material costs (Braithwaite, 1992).
complexity in a supply chain.

However, in reality, global sourcing causes

Braithwaite (1992) identified major differences

between global and local supply chains which increase the complexity are:

•

Extended lead times of supply

•

Unreliable lead times and transit times

•

Multiple mode and consolidation options

•

Intermediate local added value options

•

Distrust between cultures.

Braithwaite (1992) pointed out that the most significant of these differences
are extended and unreliable lead times of supply. He addressed the importance of
the global logistics strategy, and identified packaging as one of the key
componentsfor an efficient supply chain network along withmaterials management,
facilities, source policy, local value added and transport.
For an automotive supply chain where Just-In-time (JIT) and Just-InSequence (JIS) are common practices and supplying automotive parts should be
precise like a machine processing, speed and reliability are key indicators for
delivery performance. Speed is related to supplier’s responsiveness to perform the
requested activity or fill an order, while reliability is related to capacity of suppliers to
perform the promise (Milgate, 2001).
42

An extended global supply chain creates uncertainties in the form of demand
variability, which can cause various supply chain problems such as planning,
scheduling and control of delivery performance.
Complexity of supply chain is caused by great differences between countries
in available information technology, logistical infrastructure, labor quality and
supplier’s performance standards.Among the three critical factors (uncertainty,
technological intricacy and organizational systems) which cause complexity of global
supply chain, he pointed out that the uncertainty causes most significant impact on
delivery speed and reliability (Milgate, 2001).
Uncertainties in supply chain are usually due to demand, supply and
technology variations. It is assumed that in a mature, established supply chain
network, uncertainties are relatively low.

However, if a supply chain network and

process are in the early development stage and changing rapidly, high uncertainty in
the supply chain occurs (Sun, Hsu, & Hwang, 2009).
Given the complex and dynamic nature of global supply chain, various
changes such as new packaging types and new transportation routes could increase
uncertainty of supply chain. Because successful implementation of a global
reusable shipping container system largely depends on reliable and cost effective
management of shipping containers, simulation techniques can be used to identify
these uncertainties before the implementation of the system.
2.4.2. Computer simulation techniques
Computer simulation techniques can be a very useful tool in answering
“what-if” questions such as redesigning supply chains for an industry or a company
(Chu, 2003).

Computer simulations have gained popularity because of their ability

43

to solve complex questions by developing models to analyze interactions in the
system by changing the input values and observing the output values.
Developments in computer hardware and advanced simulation software have
enabled researchers to conduct more advanced and complex dynamic simulation
studies. Advanced simulation software provides more flexibility and easy to use
functions without the tedious and erratic programming procedures in the past (Kelton,
2010). In the manufacturing industry, dynamic simulation technology has been
widely used to improve and optimize manufacturing systems (Tahar & Adham, 2010).
Although simulation is one of the most efficient tools in system analysis, there
are some disadvantages of simulation studies which analysts should take account
when they design and analyze simulation experiments.

First of all, model building

needs special training and time consuming efforts (Banks, Carson, Nelson, & Nicol,
2010). Even once it is created; analysts need to understand that the outputs would
be randomly different depending on the time frame and probability distributions that
the analysts chose. To reduce the unpredictable outcomes, analysts might be able
to get rid of the uncertainty in the system, but they have to be careful that they
should not over-simplify the system.

It is safe to say that simulation study is for an

approximate answer to the right problem rather than an exact answer to the wrong
problem (Kelton, 2010).
A 12 step process of discrete event simulation is suggested by Banks et al.
(2010).
a. Problem formulation: a statement of the problem.
b. Setting of objectives and overall project plan
c. Model building.

As much as possibly, create simple, but resemble real

system.

44

d. Data collection. Data collection is the most time consuming and labor
intensive step of the simulation, so it usually starts with model building
concurrently.
e. Coding or software modeling. There are number of options to convert
the model into a computer program, but the most advanced tools are the
visually animated simulation programs such as ARENA, Simul8 and
ProModel.
f.

Verify.

Testing of modeling logic. In many cases, common sense is

enough to complete this step without complicated mathematical
verification process.
g. Validation by comparing the simulation output variables with actual data.
h. Experimental design.

Design and evaluate alternatives with controlling

of common parameters such as the initialization period, total simulation
length, and number of replications.
i.

Production runs and analysis. Run for analyzing the performance
measures for the system such as efficiency, utilization and service rate
for the model and alternatives.

j.

Document program and report results.

k. Implementation of the simulation to the actual situation.
2.4.3. Simulation studies for supply chain
Kleijnen (2005) emphasized importance of simulation for supporting supply
chain management decisions.

He pointed out that the simulation may give

researchers clear looks about the causes and effects of the supply chain
performance by testing (or experimenting) inputs and model structures.

45

The

simulation model can explain great details of supply chain process such as order
arrivals and machine breakdowns. He characterized a simulation model as follows.

•

A simulation model is quantitative, mathematical and computer based.

•

A simulation model is naturally dynamic because it has at least one
variable and at least two different point of time.

•

A simulation model is not solved by mathematical analysis, but time
paths of the dependent variables are computed by given input values
and initial model structures.

Kleijnen (2005) compared for different types of simulation used in SCM that
can be useful for quantifying benefits and costs of decisions: spreadsheet simulation,
system dynamics (SD), discrete-event dynamic systems (DEDS) simulation and
business games. Typical usages and characteristics are compared in Table 15.

Table 15: Comparison of four simulation types for supply chain management
Simulation types

Typical usages

Spreadsheet
simulation

Manufacturing resource planning
(MRP), vendor managed
inventory (VMI)

System dynamics
(SD)

Characteristics

Supply chain, Bullwhip effects,
feedbacks which compares target
and real values

Often too simple and unrealistic
Views companies as systems with six
types of flows: materials, goods,
personnel, money, orders and
information.
Most SD model doesn’t have
randomness

Discrete-event
dynamic systems
(DEDS)

Most popular simulation method
for SCM, use for Enterprise
Resource Planning (ERP),
alternative supply chain design

More detailed than spreadsheet and SD.

Business games

Often used as a education and
research tool for bullwhip effects,
production scheduling, etc.

Easy to simulate technological and
economic process, but difficult for human
behavior modeling

It represents individual events and
incorporates uncertainties (e.g. irregular
consumer order)

There are a number of studies that have used computer simulation
techniques to identify inefficiencies in supply chains and help mangers optimize

46

supply chain costs. For example, bullwhip effect (BWE), a phenomenon that leads
excessive safety stocks and inventory and inefficient production due to unstable and
fluctuating demands by the customer, has been a frequent research theme because
it can cause serious order fulfillment problems in global supply chain. Li and Duan
(2009) designed and developed simulation software to simulate the impact of BWE
and to compare between manufacturer and distributor.

Centeno and Perez (2009)

used ARENA simulation program to quantify BWE and evaluate management
strategies.
2.4.4. Simulation studies in logistics packaging
Most dynamic simulations applied to reusable packaging are limited for certain
part of supply chain process.

Kroon and Vrijens (1995) sought a quantitative model

that could be used in the planning of return logistics system for reusable containers.
Key research questions were,
a. How many containers should be available in the system?
b. How many container depots should there be and where should they be
located?
c. How should the distribution, collection, and relocation of the containers
be organized?
d. What are appropriate service, distribution and collection fees?
They conducted a case study for a large logistics company in Netherlands,
and carried out a simple simulation-optimization model which compared three return
logistics systems such as a switch pool, a system with return logistics, and a system
with return logistics. Because this study only provided a methodological framework
without specific cost data, and was based on the strict environmental legislation such

47

as Duales System Deutschland in Germany, a more sophisticated and refined study
in different environment are needed (Kroon & Vrijens, 1995).
Castillo and Cochra (1996) presented a framework and a mathematical
formulation for the optimal reusable bottle production and distribution system of a
large soft drink manufacturer located in Mexico City, Mexico, and the results
benefited the company improving product and container control and inventory
behavior. Mathematical computations make it easy to compare packaging costs
directly, but it cannot model the unpredictable and complex nature of global supply
chain.
Gupta, Jarupan and Kamarthi (2003) used ARENA software to investigate
the effect of vehicle management for a reusable packaging system in order to
improve customer satisfaction, and concluded that different combination of
dispatching and vehicle assignment schemes affect customer satisfaction differently.
Mollenkopf et al. (2005) used a combination of static simulation-regression
analysis to compare the relative costs of reusable and expendable shipping
containers, but it only focused on a single supply chain. They suggested using a
dynamic simulation approach for a more realistic analysis of a complex packaging
system.
Johansson and Hellstöm (2007) introduced the methodological approach of
combining case study and simulation study while introducing the concept of asset
visibility study for a returnable transport item. However, this study did not attempt to
investigate transportation costs nor effects of managing a pool system.
Marchet, Melacini and Perotti (2011) developed a simulation model for
evaluation of an order-Pick systems (OPSs) and used three design variables of
duration, workload and number of daily Pick waves (customer orders). They found

48

out the trade-off between the Pick efficiency and the sorting cost such as relationship
of minimum number of daily packing orders versus pick costs.
From industry, simple cost equations or mathematical computation using
computer software such as Excel have been used in industries for simple, direct
comparisons (Walter, 1982; Reusable Packaging Association, 2010). Recently,
Ijumba (2012) developed a simulation model to estimate and to optimize container
fleet size using the stochastic model optimization method varying supply chain
conditions of an OEM radiator closed loops.
Considering all simulation options and previous studies above, a discreteevent dynamic systems (DEDS) simulation technique is used for this study. The
dynamic simulation technique enables researchers to model a sequence of events
that occur over time, so it can be utilized for identifying and analyzing the dynamic
behavior of reusable packaging systems caused by continuous demand and supply
fluctuations. Key constraints that prohibit time- and cost-effective returnable
shipping container flows are identified.
A DEDS can visualize constant changes of container flows and costs of
reusable packaging systems, compared to single use expendable packaging
systems, for global supply chains. A developed simulation model can help
packaging and logistics managers identify where the possible constraints and
bottlenecks are.

Commercial simulation software, ARENA ®, is utilized to develop a

model.
Statistical analysis is performed for validation and verification of the
developed simulation model. The developed model is compared with actual data
provided by a global logistics service provider and an automotive company. Based
on the simulation outputs, recommendation for the optimum operation solution for

49

packaging types (reusable or expendable), costs and benefits, structure of reusable
shipping container management is provided.

50

CHAPTER 3
OBJECTIVES OF THE STUDY
This study is to answer the following questions: Can the reusable packaging
be a financially viable option over the expendable packaging for a global supply
chain? Can a combination of Activity-Based Costing (ABC) and simulation
techniques be an effective way to measure the financial performance of the reusable
packaging system?

What are the key opportunities and constraints for

implementing a successful reusable packaging system for the global supply chain?
To answer the questions, the following research objectives are determined.
a. To develop a framework for visualizing packaging costs in a supply chain
using the ABC method.
b. To evaluate different types of reusable and expendable shipping
containers in terms of relative costs, functionalities, structures,
technologies and purposes.
c. To develop a dynamic simulation to reveal interrelationships between the
packaging and supply chain. The developed simulation model can be
used for evaluating relative influence of the various factors and
performing comparative analysis on reusable and expendable packaging
operation systems.
d. To identify and evaluate key trade-offs for implementing a reusable
packaging system for the global supply chain.
e. To demonstrate the importance of using a more scientific approach in
assessing the financial performance of reusable packaging, using a
combination of case studies, ABC method, and dynamic simulation, so

51

the industry can significantly reduce its risk when making such
packaging decisions.

52

CHAPTER 4
RESEARCH METHOD
Reusable packaging can be a sustainable and profitable investment -- or a
costly and unsustainable mistake. This research explores impact of two different
packaging options (reusables and expendables) on an international supply chain and
use a new approach to find a practical solution.
In order to evaluate financial effectiveness of the perceived operating
reusable, rental and expendable packaging systems in potential logistical routes for
a global manufacturing company, three steps for an evaluation framework is derived
from Zeng and Rossetti (2003) and Creazza, Dallari and Melacini (2010)

•

Step 1: Identify packaging and logistics activities and develop the activity
cost drivers (input parameters) associated with the total cost (See Table 16
for input and output variables)

•

Step 2: Collect actual data and generalize for developing simulation models

•

Step 3: Develop a static simulation model and perform a sensitivity analysis
to assess the impact of key parameters

•

Step 4: Develop a dynamic simulation model and simulate for a significant
period of time (e.g. 5 years).

•

Step 5: Validate and compare the simulation results with actual and ABC
data derived from the sample company.
The proposed cost models compare the relative costs of each packaging

system. By relative cost model approach, this research can generalized the results
of the study while simplifying comparative analysis of three packaging options.

53

Although the analysis is performed based on actual situation and real data
derived from leading global 3PLs, suppliers and automotive companies, their identity
is not reported in this research for confidentiality reasons.
This study engaged partners with automobile companies, third party logistics
providers (3PL) and packaging industries with particular emphasis on “transplant”
manufacturers who have assembly operations overseas. Data were collected by
Industry on-site interviews (in both suppliers and manufacturers), observations, and
from company’s database.

Data acquired from the case study and activity based

costing (ABC) analysis were utilized to develop the simulation model. The results
are compared with actual data and ABC analysis.
A static simulation with comparative regression analysis based on ABC
analysis is undertaken to test relative sensitivity of factors that affecting total
packaging system cost for each system. Based on the sample data provided by “A”
company, a triangular generating function from Excel was used to generate
individual observations for each factors.

Five hundred observations were generated

and each observation represents a unique combination of independent variables.
The simulation model developed is used for testing differing combinations of
variables provided in Table 16.
A dynamic, discrete-event simulation (DEDS) method is applied to the study
because it can visualize constant changes of container flows and costs of reusable
packaging systems, compared to single use expendable packaging systems, for the
global supply chain. The dynamic simulation technique enables researchers to
model a sequence of events that occur over time, so it can be utilized for identifying
and analyzing the dynamic behavior of reusable packaging systems caused by
continuous demand and supply fluctuations. ARENA ® simulation software is used

54

for the simulation model.

The following sections describe details of the research

model design.
4.1.

Research model design
Figure 3 shows the conceptual research model for this study. As shown in

the top portion of Figure 3, packaging types (reusable or expendable shipping
containers) and associated costs, and supply chain routes and associated costs are
featured for the strategic decisions of a global manufacturing company, while
environmental uncertainties are classified as demand and supply variables.
Uncertainties include supply and demand fluctuations, unexpected delays; oil price
hikes, etc.

Strategic parameters
- Packaging type and costs
- Supply chain routes and costs

Environmental variables
- Demand variables
- Supply variables

ABC analysis

Static and dynamic
simulation

Performance evaluation
- Total packaging system cost

Figure 3: The conceptual research model
The packaging and supply chain cost model is developed utilizing ABC
method based on the previous study (Lin, Collins, & Su, 2001).

55

Packaging and

supply chain performance will be determined in terms of total packaging cost, total
supply chain cost. A concept model of packaging activity cost analysis is shown in
Figure 4.

Figure 4: An example of th
the conceptual
ceptual model for packaging activity cost
analysis
Each activity is analyzed and calculated costs are used for input data for the
simulation model.

Figure 5 show tthe simulation process flow based on Banks et al.

(2010) for this study.

56

Problem formulation

Setting research goals and
overall project plan

Data collection

Model building

On-site survey and
interviews

Developing scenarios

Cost analysis

Conceptual model
design

Coding

No

Verified?

Yes
No

No

Verified?

Yes
Runs and analysis
No
Done?

Yes

Documentations

Implementation

Figure 5: Simulation process flow

57

4.2.

The international logistics routes investigated
After an in depth analysis of the literature and interviews with international

freight forwarders and 3PLs, several international logistical routes are investigated
for this study. The logistics process is performed by a 3PL from suppliers located in
a specific geographic area (e.g. Far East), delivering goods to the manufacturing
plants in a specific destination (e.g. Eastern Europe, USA and China). This study
considers ocean container shipping with 40-feet containers as the primary
international transportation mode considering its low freight rates despite long transit
time.
For ocean container shipping, two types of shipment methods are considered,
namely full container load (FCL) and less than container load (LCL). For LCL,
shipments from several different suppliers are collected and consolidated at a crossdocking facility and they are shipped to the final destinations (Creazza, Dallari, &
Melacini, 2010).

Only FCL is considered for this study because costs of shipping

methods are not directly related to the type of shipping containers.
After unloading and custom processes, all shipments are shipped by means
of road transportation and unconsolidated at the 3PL’s warehouses adjacent to the
manufacturing plants. All goods packed with expendable shipping containers must
be repacked in order to feed to assembly lines.

Used reusable shipping containers

are collected by manufacturers and stored temporarily before a 3PL consolidates
containers for shipping back to suppliers.
4.3.

Research variables
The ABC model and simulation needs a significant amount of data to fit the

proposed variables. A portion of data is collected from industry on-site interviews
and observations.

Other data is based on historical data and a system database

58

from the target company. Table 16 summarizes and explains the main research
parameters and variables for the study.

Table 16: Research parameters and variables for the study
Main parameters

Packaging
parameters

Independent
variables
Supply chain
parameters

Influenced by
※

Container part costs ($/container)

※
※
※
※

Container weight (lbs/container)
Product weight (lbs/part)
Pack quantity (parts/container)
Container return rate (percent/year, reusables only)

※
※

Backhaul packaging volume factor (percent/container,
reusables only)
Designed container life (years, reusables only)

※

Recycling / Waste disposal cost ($/container)

※
※

Delivery distance (miles)
Buffers and safety stocks (percent of daily demand)

※
※

Cycle time (days in shipping loop)
Customs charges for customs clearance, brokerage,
allocation fee ($/container)
Inventory costs ($/container)

※
※

Handling costs for terminal, material, disposal,
cleaning, sorting, repairing, etc. ($/container)
Facilities and equipment costs including labor, energy,
etc ($/container)
Risk costs for damage, loss, delay, insurance, etc.
($/year)

※
※
Environmental variables
Demand
Supply

Dependent
variables

Total packaging
system cost

※

Average daily demand (containers/day)

※

Changes in demand quantity (containers/day)

※

Average daily supply (containers/day)

※

Changes in supply quantity (containers/day)

※ Sum of total container costs incurred by packaging
activities such as container purchasing costs, cost
for transportation and inventory, and packing costs
($/container)

In order to reduce the complexity of input variables, the following
assumptions have been made for this study.

•

The types of packaging materials are not considered for this study. For
example, if a shipping container is made of steel and designed for a
single use, this container is regarded as an expendable packaging.

59

•

The dimensions and packing quantity of containers are uniform across
the expendable and reusable shipping containers.

Hence, annual

demand including buffers and safety stocks for containers will be same
regardless of container types. Differences between expendable and
reusable shipping containers in terms of packaging types are cost,
weight and designed container life.

•

Some supply chain costs including customs charges (e.g. customs
clearance, brokerage, allocation fee), inventory costs, handling costs,
facilities and equipment costs, risk costs (e.g. damage, loss, delay,
insurance, etc.) are uniform across the two packaging alternatives, but
rates are different depending on the location of the manufacturing plant.

•

The reusable shipping container can have collapsible or nestable
functions in order to reduce the total volume during backhaul logistics.

•

Average production year for an automobile assembly plant covers 52
weeks, 5 days per week, for total of 260 days. This study will examine
for at least 1 and up to 5 years of operation.

•

Reusable shipping containers can be purchased or leased by a 3PL, and
handled by the 3PL throughout the supply chain. All manufacturing
plants are responsible to return all used containers to the 3PL and
cannot share or use alternatively. Any loss or damage occurred at the
plant should be responsible for the plant, and should be charged at full
cost of the container.

For meaningful statistical analysis and simplifying to analyze the impacts and
relationships among purposed variables (Mollenkopf, et al. 2005), ranges of each
independent variable value are determined based on actual data obtained from the
60

global 3PLs and automobile manufacturing companies. Table 17 shows the ranges
(low, mode, and high) for the key independent variables.
Table 17: Independent variables and associated ranges
Ranges
Independent variables
Low

Medium

High

Expendables($/container)

0.5

1.5

3.0

Reusables Purchasing($/container)

2.0

5.0

10.0

Reusables Leasing($/container/day)

0.02

0.05

0.10

International shipping distance (miles)

500

5,000

10,000

In-land Drive distance (miles)

100

300

500

1,000

5,000

10,000

Container weight (lbs/container)

1

5

20

Container quantity on a pallet (containers/pallet)

15

60

120

Return rate (percent/year, reusables only)

80

90

95

Backhaul logistics volume factor(percent/container, reusables only)

0.5

0.7

1.0

Designed container life (year, reusables only)

2

3

10

Buffers and safety stocks (percent of daily demand)

2

5

10

Cycle time (days)

30

90

180

Customs charges ($/FCL 40ft)

500

800

1,500

Average daily Volume (containers/day)

4.4.

Data collection
The research is based on an international supply chain case study.

An

extensive literature review and industry on-site interviews (in both suppliers and
manufacturers) are performed to develop a conceptual simulation model.

Data

acquired from the case study and activity-based costing (ABC) analysis is utilized to
develop the simulation variables.
The study engages partners with automobile companies, third party logistics

61

providers (3PL) and packaging industries with particular emphasis on “transplant”
manufacturers who have assembly operations overseas. Required data to fit the
research variables are presented in Table 18.

Table 18: Required data for the study
Data Sources
On-site interviews

•
•
•
•

Observations

•
•
•
•
•
•
•
•
•

Historic Data /
Company’s
Database

•
•
•
•
•
•

Data
General description about the company, business and distribution
networks
Description of the product, packaging and logistical activities
Packaging activities such as labor, time, cost associated with packing
and repacking
Logistical activities such as labor, time, and cost associated with
shipping, loading, storing, etc.
Logistical means such as pallets, trucks, ships, etc.
Packaging ownership (supplier owned, buyer owned, co-op owned, 3PL)
Location of supplier (domestic/international), physical distance
Average changes in daily demand and order quantity, order lead time
Product types including materials, size, weight, etc.
Packaging types (expendables and reusables) including materials, size,
weight, etc.
Packaging activities such as labor, time, delays, cost associated with
packing and repacking
Logistical activities such as labor, time, delays, and cost associated with
shipping, Load, Store, returning etc.
Logistical means such as pallets, trucks, ships, etc.
Description of the product, packaging and logistical activities
Product and packaging types (expendables and reusables) types
including prices, materials, life, size, weight, etc.
Cost data of packaging related costs such as buying cost, labor costs,
waste disposal costs, etc.
Cost data of logistical activity related costs such as buying cost, labor
costs, vehicle payload, waste disposal costs, etc.
Average changes in daily demand and order quantity, order lead time
Logistical means such as pallets, trucks, ships, etc.

62

CHAPTER 5
PACKAGING COST ANALYSIS USING ABC ME
METHOD
THOD
5.1.

Process and activity descriptions
Based on the acquired data from observation, company information and

industry on-site
site interviews (in both suppliers and manufacturers), packaging activities
which affect packaging and supply chain costs were analyzed.

Figure 6 shows an

international packaging and automotive part supply chain network used for this study.

Figure 6: Proposed International packaging and automotive part supply chain
flow for the study
The
he packaging cost models for each packaging type were developed utilizing
ABC method based on a previous study (Lin, Collins, & Su, 2001). Packaging
performance was determined in terms of total packaging system cost. The six
processes and eighty activities for this study are shown in Table 19.

63

Table 19: Processes and activities for the typical packaging system for the
international automotive part supply chain
Processes

Detail Processes
Receive from supplier(s)

Purchasing
(Inbound)

Preparing shipments
Drive to KD center
Arrived at KD center

Packing at KD center or
vender(s)
Packing

Preparing outbound
shipments

Preparing shipments

Outbound
logistics
(Shipping to
plants)

Shipping from Busan, KR
to port of Savannah, GA
Arrived at port
Shipping to domestic
Consolidation center (CC)
Arrived at CC

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.

Activities
Paperwork
Receive
Inspection
Store (for inbound)
Paperwork
Pick
In-house delivery
Load the truck
Drive
Unload
Paperwork
In-house delivery
Unpalletize empty boxes( or containers)
Move empty boxes to packing area
Pack (RPC)
Pack (EXP)
Move packaged products to shipping
preparation area
Paperwork
Pick
Palletize product & packages
Load
Drive
Unload of container from truck in CFS
Wait for Customs
Customs clearance departure (domestic port)
Load of container to move container alongside
ship

27. Ship
28.
29.
30.
31.
32.
33.
34.
35.
36.

64

Unload of container from ship
Hold for Customs
Customs clearance arrival (oversea port)
Paperwork
Load of container to truck
Drive
Paperwork
Unload
Unpalletize product & packages

Table 19 (cont’d)
Processes
Outbound
logistics
(Shipping to
plants)

Detail Processes
Transshipping (Expendables
only)

Activities
37. Repacking(Expendables)
38.
39.
40.
41.
42.
43.
44.
45.
46.

Preparing line feeds
Line feeding

In house return
(Expendables only)
In-house
return process

47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.

In house return
(RPCs only)

Preparing backhaul
shipments

Backhaul
logistics
(RPCs only)

Shipping from CC to
reconditioning center

60.
61.
62.
63.
64.
65.

Arrived at reconditioning
center

Move containers to feeding area
Load
Unload
Line feeding
Unload empty boxes from workstation
Move containers to dunnage storage area
Sort expendables
Palletize empty boxes
Load empty boxes to CC for recycling or
disposal
Unload empty RPCs from workstation
Move empty RPCs to dunnage sort area
Sorting empty RPCs
Store empty RPCs
Palletize empty RPCs
Load empty RPCs to CC for reuse
Paperwork
Count and check containers
Drive
Unload containers from truck in CFS
Wait for Customs
Customs clearance departure (oversea)
Load of container to move container
alongside ship
Ship
Unload from ship
Hold for Customs
Customs clearance arrival (domestic)
Load of containers
Drive (send to reconditioning center)

66. Paperwork
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.

Reconditioning process
(RPCs)
After use
process
After use Process at CC
(Expendables)
Disposal/recycle

65

Unload
Unpalletize empty RPCs
Inspection
Recondition (cleaning, repairing, etc)
Palletize empty RPCs
Sort
Store empty containers
Paperwork
Pick
Sort
Load
Drive
Disposal
Recycle

Resource drivers, activity cost drivers and cost units of the drivers are
identified for each activity as shown in Table 20.

Table 20: Proposed activities and activity cost drivers considered for
calculation
Processes

Detail
Processes

Resource
drivers

Activities
Receive (paper
work)
Unload

Receive from
supplier(s)
Inspection

Inspectors
employed

Store (for
inbound)

Space used

Paperwork

Preparing
shipments

Arrived at KD
center

Total time
worked

$/hour

Time required
to check a
container

$/hour

Time required
for inspection
per container
Space occupied
and time
required
Total time
worked

$/hour
$/sq.
ft/day
$/hour

$/hour

Time required
to pick a
container

$/mile

Time required
to load a
container

$/hour

Drive

Drivers
employed

Distance and
square meters
of pallets
throughput

$/mile

Unload

Material
handlers
employed &
equipment used

Time required
to Unload a
container

$/hour

In-house delivery
Load (Load the
truck; and
dispatch the
truck)

Drive to KD
center

Administrators
employed
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used

cost
unit of
the
driver

Time required
to pick a
container

Pick

Purchasing
(Inbound)

Administrators
employed
Material
handlers
employed &
equipment used

Activity cost
drivers

66

Table 20 (cont’d)

Processes

Detail
Processes

Activities

Paperwork

In-house delivery

Packing at KD
center or
vender(s)

Unpalletize
empty boxes( or
containers)
Move empty
boxes to packing
area

Pack
Packing
Move empty
boxes to packing
area
Paperwork

Pick
Preparing
outbound
shipments

Palletize product
&pkgs

Load

67

Resource
drivers

Activity cost
drivers

cost
unit of
the
driver

Administrators
employed
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Administrators
employed
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used

Total time
worked

$/hour

Time required
to pick a
container

$/mile

Time required
to pick a
container

$/hour

Time required
to move a
container

$/mile

Time required
to pack a
container

$/hour

Time required
to move a
container

$/mile

Total time
worked

$/hour

Time required
to pick a
container

$/hour

Time required
to pick a
container

$/hour

Time required
to load a
container

$/hour

Table 20 (cont’d)

Processes

Detail
Processes

Preparing
shipments

Activities

Resource
drivers

Activity cost
drivers

cost
unit of
the
driver

Drive

Drivers
employed

Distance and
square meters
of pallets
throughput

$/mile

Unload
containers from
truck in CFS

Material
handlers
employed &
equipment used

Time required
to Unload a
container

$/hour

Wait for Customs

Space used

Space occupied
and time
required

$/sq.
ft/day

Administrators
employed

Total time
worked

$/hour

Material
handlers
employed &
equipment used

Time required
to load a
container

$/hour

Shipping

Material
handlers
employed

Distance and
square meters
of pallets
throughput

$/mile

Unload
containers from
ship

Material
handlers
employed &
equipment used

Time required
to Unload a
container

$/hour

hold for Customs

Space used

Customs
clearance arrival
(oversea port)

Administrators
employed

Customs
clearance
departure
(domestic port)
Load containers
to move
container
alongside ship
Outbound
logistics
(Shipping to
plants)

Shipping from
export port to
import port
(Expendables
& RPCs)

Arrived at port

Paperwork
Shipping to
domestic
Consolidation
center (CC)

Load containers
to truck
Drive (dispatch
the truck)

68

Administrators
employed
Material
handlers
employed &
equipment used
Driver
employed

Space occupied
and time
required
Customs
clearance cost
per container
Total time
worked

$/sq.
ft/day
$/cont
ainer
$/hour

Time required
to load a
container

$/hour

Number of
orders picked

$/mile

Table 20 (cont’d)

Processes

Detail
Processes

Activities

Paperwork

Unload
Arrived at CC
Unpalletize
product & pkgs

Outbound
logistics
(Shipping to
plants)

Transshipping
(Expendables
only)

Repack(Expenda
bles)

Move containers
to feeding area
Preparing line
feeds

Load

Unload

Line feeding

Line feeding

Unload empty
boxes from
workstation

In-house
return process

In house return
(Expendables)

Move containers
to dunnage
storage area
Sorting
expendables
Palletize empty
boxes

69

Resource
drivers

Activity cost
drivers

cost
unit of
the
driver

Administrators
employed
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Material
handlers
employed
Material
handlers
employed &
equipment used

Total time
worked

$/hour

Time required
to Unload a
container

$/hour

Time required
to pick a
container

$/hour

Time required
to pack a
container

$/hour

Time required
to move a
container

$/mile

Time required
to load a
container

$/hour

Time required
to Unload a
container

$/hour

Time required
to move a
container

$/mile

Time required
to Unload a
container

$/hour

Time required
to move a
container

$/mile

Time required
to sort a
container

$/hour

Time required
to pick a
container

$/hour

Table 20 (cont’d)

Processes

Detail
Processes

Resource
drivers

Activities
Load empty
boxes to CC for
recycling or
disposal
Unload empty
RPCs from
workstation
Move empty
RPCs to
dunnage sort
area

In-house
return process

Sort empty RPCs
In house return
(RPCs)

Store empty
RPCs

Space used

Palletize empty
RPCs
Load empty
RPCs to CC for
reuse
Preparing
backhaul
shipments for
RPCs

Backhaul
logistics
(RPCs only)

Shipping from
CC to
reconditioning
center (RPCs)

Material
handlers
employed &
equipment used
Material
handlers
employed
Material
handlers
employed &
equipment used
Material
handlers
employed

Paperwork
Count and check
containers

Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used
Administrators
employed
Material
handlers
employed

Activity cost
drivers

cost
unit of
the
driver

Time required
to load a
container

$/hour

Time required
to Unload a
container

$/hour

Time required
to move a
container

$/mile

Time required
to sort a
container
Space occupied
and time
required

$/hour
$/sq.
ft/day

Time required
to pick a
container

$/hour

Time required
to Unload a
container

$/hour

Total time
worked
Time required
to check a
container
Distance and
square meters
of pallets
throughput

$/hour
$/hour

Drive

Drivers
employed

Unload
containers from
truck in CFS

Material
handlers
employed &
equipment used

Time required
to Unload a
container

$/hour

Waiting for
Customs

Space used

Space occupied
and time
required

$/sq.
ft/day

Customs
clearance
departure
(oversea)

Administrators
employed

Number of
orders picked

$/hour

70

$/mile

Table 20 (cont’d)

Processes

Backhaul
logistics
(RPCs only)

Detail
Processes

Shipping from
CC to
reconditioning
center (RPCs)

Arrived at
reconditioning
center

Activities

Resource
drivers

Activity cost
drivers

cost
unit of
the
driver

Load containers
to move
container
alongside ship

Material
handlers
employed &
equipment used

Time required
to load a
container

$/hour

ship

Material
handlers
employed

Distance and
square meters
of pallets
throughput

$/mile

Unload from ship

Material
handlers
employed &
equipment used

Time required
to Unload a
container

$/hour

hold for Customs

Space used

Customs
clearance arrival
(domestic)

Administrators
employed

Load containers

Material
handlers
employed &
equipment used

Time required
to Unload a
container

$/hour

Drive (send to
reconditioning
center)

Drivers
employed

Distance and
square meters
of pallets
throughput

$/mile

Paperwork

Administrators
employed

Total time
worked

$/hour

Time required
to Unload a
container

$/hour

Time required
to pick a
container

$/hour

Material
handlers
employed &
equipment used
Material
handlers
employed &
equipment used

Unload

After use
process

Reconditioning
process
(RPCs)

Unpalletize
empty RPCs

Inspection

Inspectors
employed

Reconditioning
(cleaning,
repairing, etc.)

Quantity of
containers

71

Space occupied
and time
required
Customs
clearance cost
per container

Time required
for inspection
per container
quantity of
containers to be
cleaned

$/sq.
ft/day
$/cont
ainer

$/hour
$/cont
ainer

Table 20 (cont’d)

Processes

Detail
Processes

Resource
drivers

Activities

Palletize empty
RPCs
Reconditioning
process
(RPCs)

Sort
Store empty
containers

Space used
Administrators
employed
Material
handlers
employed &
equipment used
Material
handlers
employed
Material
handlers
employed &
equipment used

Paperwork

Pick

After use
process

After use
Process at CC
(Expendables)

Sort

Load

Disposal/
recycle

Material
handlers
employed &
equipment used
Material
handlers
employed

Drive

Drivers
employed

Dispose

Weight of
material
Weight of
recyclable
material
Administrators
employed

Recycle
Paperwork

Activity cost
drivers

cost
unit of
the
driver

Time required
to pick a
container

$/hour

Time required
to sort a
container
Space occupied
and time
required
Total time
worked

$/hour
$/sq.
ft/day
$/hour

Time required
to pick a
container

$/hour

Time required
to sort a
container

$/hour

Time required
to load a
container

$/hour

Distance and
square meters
of pallets
throughput
Weight of
containers
disposed
Weight of
containers
recycled
Total time
worked

$/mile
$*lb/co
ntainer
s
$*lb/co
ntainer
s
$/hour

The following main activities, typical for third party logistics operators, were
identified and classified as shown in Table 21: purchasing, logistics administration,
transportation (outbound and backhaul), warehousing operations, and after-use
activity. The following sections (5.1.1 to 5.1.5) describe each activity and cost
driver.

72

Table 21: ABC cost analysis for five logistical activity areas
Cost Types
Logistics administration
cost(AC)
($/container)
transportation(outbound)
cost(TCF) ($/container)
transportation(backhaul)
cost(TCB) ($/container)
Warehousing cost(WC)
($/container)
After use cost/revenue
After-use cost(AC)
($/container)
Recycling revenue(RE)

Details
Sum of costs incurred by administration activities including customer
service, order processing, production planning, procurement,
purchasing, planning, scheduling and dispatch, custom charges,
inventory control, general administration and management costs
Sum of costs incurred by outbound transportation activities including
Load, Unload, Drive, equipment and energy uses
Sum of costs incurred by backhaul transportation activities including
Load, Unload, Drive, equipment and energy uses
Sum of costs incurred by warehousing activities including Receive, inhouse handling (Pick, repacking, consolidation, put-away, etc.),
equipment, storage and energy uses
Sum of costs incurred by cleaning, reconditioning, repairing, disposal
activities
Sum of revenue incurred by recycling activities

5.1.1. Packaging purchasing activities
All (either expendable or reusable) packaging containers and automotive parts
are either purchased or produced by oversea suppliers. Packaging purchasing
costs include the annual purchasing costs for expendable and reusable, and annual
rental cost for the reusable containers. The numbers of containers required and
material costs were calculated for each packaging system.
Although purchasing activity is a part of the activity-based cost equation for
expendable and reusable containers, the rental system does not require any
purchasing activity in this case because the rental company provides the containers
directly to KD center. Each purchasing activity is assigned into the logistics
administration and warehousing categories.
5.1.2. Logistics administration activities and cost drivers
Logistics administration activities includes paper work for customer service,
order processing, production planning, procurement, purchasing, forecasting,
planning, custom charges, scheduling and dispatch, and inventory control, general
administration and management. Table 22 shows the logistics administration
activities and activity cost drivers considered in this research.
73

Table 22: Logistics administration activities and activity cost drivers
considered for calculation
Processes

Purchasing
(Reusables &
Expendable)

Detail Processes

Activities

Activity cost
drivers

cost unit of the
driver

Purchase and Receive
from supplier(s)

Receive paper
work

Quantity of
order

Total time worked
($/hour)

Prepare shipments

Paperwork

Pack at KD center or
vender(s)

Paperwork

Quantity of
order
Quantity of
order
Quantity of
order

Total time worked
($/hour)
Total time worked
($/hour)
Total time worked
($/hour)

Customs related
paperwork

Quantity of
order

Total time worked
($/hour)

Dispatch to Consolidation
center (CC)

Paperwork

Quantity of
order

Total time worked
($/hour)

Arrive at CC

Paperwork

Quantity of
order
Quantity of
order
Quantity of
order
Quantity of
order

Total time worked
($/hour)
Total time worked
($/hour)
Total time worked
($/hour)
Total time worked
($/hour)

Quantity of
order

Total time worked
($/hour)

Paperwork
Packing
(for all
three
systems)

Backhaul
logistics
(Reusables&
rentals only)
After use
Process
(Reusables &
Expendable)

Prepare outbound
shipments

Prepare backhaul
shipments for reusables

Paperwork
Customs related
paperwork

Arrive at reconditioning
center

Paperwork

Recycle or disposal

Paperwork

Most logistics administration activities are clerical activities related to paperwork
and data entry that requires labor cost and time. The main activity cost driver for
administration has been the quantity of orders because it was manual data entry
activity, but quantity is not an important cost driver nowadays since most orders and
paper works are linked with EDI or other management software (Griful-Miquela,
2001). In this case, quantity is not a cost driver anymore, but the frequency of
incoming and outgoing goods still consumes resources of time and labor.

74

5.1.3. Warehousing activities and cost drivers
Two warehousing/distribution center processes are assumed in this study: KD
(knock down) center and consolidation ccenter.
The KD center, as shown in Figure 7,, products (automotive parts) and containers
are received separately from respective suppliers and assembled into packaged goods.
In KD center, the automotive part is arriving as a knock down kit consisting of the
smaller parts needed to assemble a produc
product to export.
In this process, the associated cost and time are not significantly different
between expendable and reusable containers assuming packing activity for both
containers requires same amount of labor and time.

Figure 7
7: Warehouse (KD Center) activities
Figure 8 shows typical activities observed in the consolidation center near the
car manufacturer. The main purpose of the consolidation center is to help the efficient
supply flow of automotive parts from oversea and loca
locall supply chains to the actual car
manufacturing plants.
75

The consolidation center is differ from warehouse since the center does not
store goods for long time, but distributes just
just-in-time
time to meet the daily needs of plants.
Only expendable packaging requires a repacking activity in order to meet the feeding
system of the manufacturer. The repacking activity, in addition to the labor and time,
requires repacking materials. Repacking material costs are included in material
purchasing cost. There are ccertain
ertain types of expendable packages that do not need
repacking, but most expendable packages need to be repacked since those are not
designed for direct feeding and containers are partly damaged during international
shipping process.

Figure 8:: 3PL Consolidation Center activities
In this research, warehouse ownership is not considered because the cost
(whether it is rented or owned) is not directly related to each activity and most
packaging related activity is related to the quantity of product. However
However,, it may need to

76

allocate them to consignors in a different way if additional warehousing facility is
necessary to perform activities.
It should be noted that this activity cost calculation for warehousing does not
take into account unused space or capacity in the warehouse, so empty space or
unused warehousing resources are excluded.

In this way, it is possible to show clearly

only packaging and logistics activity costs.
The following is a brief explanation of these typical activities:
a. Inspection/Checking incoming or returning containers involves
inspecting/checking if the actual product received is the same as the figure
on the invoice. The main cost driver is the quantity of product (or
container) received, and the cost can be calculated with the time required to
sort a certain quantity of containers multiplied by labor costs.
b. Storing involves storing the pallets at the storage location. The main cost
driver is the quantity to be stored, which determines the time spent in this
activity.

The number of containers on a pallet is important because it affects

unit cost of Store per container in the warehouse. The cost can be
calculated with the time to store a certain quantity of containers multiplying by
space usage rate.
c. Picking takes a place when an order is made; main cost driver should be the
number of items picked during warehousing process. The picking cost
should be calculated by the number of picked multiplied by amount of time
and associated labor to pick a pallet load of products from a storage location.

77

d. Moving involves moving the products in the warehouse such as moving
products from arriving dock to the storage location. The main cost driver is
the quantity to be moved, which determines the time, labor and equipment
spent in this activity.
e. Unloading is carried out when the goods from the packager or
manufacturing plant arrive at the logistics company's warehouse. A very
common practice is unloading pallet loads of products with forklift, so main
cost driver is quantity of products unloaded. By observation and
interviewing, the time spent for unloading a pallet should be calculated.

The

unloading cost should be calculated by observed time to unload a pallet
multiplied by relevant labor and equipment to perform this activity.

Loading

is the opposite of unloading and the main cost driver is also quantity.
f. Packing (including labeling and repacking) involves
packaging/repackaging each order and putting on the label with the
information about consignee, delivery route, and so on. The main cost driver
is the number of containers.

Packing costs involved are the time of the

workers responsible for packaging and the material used in this activity. In
this case, only labor cost is considered since material cost is already taken
into account separately.
g. Palletizing involves loading shipping containers to the pallet and securing
the load with stretch wrapping, banding, or any other methods.

Supposing

the entire product loaded is palletized, the main cost driver of palletizing
activity is quantity.

Palletizing cost should be calculated with the time

78

required to palletize a certain quantity of shipping containers multiplied by
labor and equipment costs.

Unpalletizing is the opposite of palletizing,

dismantling incoming loads from a pallet. The main cost driver is also
quantity.
h. Sorting includes arranging and organizing used containers by their
specifications in order to reuse, recycle or dispose. The main cost driver is
quantity, and cost should be calculated the time required to sort a certain
quantity of containers multiplied by labor costs.
Table 23 shows warehousing activities and activity cost drivers considered in this
research.
Table 23: Warehouse activities and activity cost drivers
Processes

Detail Processes

Receive from supplier

Activities
Inspecting
incoming
containers

Space occupied and time required

Picking

Number of visits to pick location

In-house delivery

Distance and square meters of pallets
throughput

Loading (Loading
the truck; and
dispatching the
truck)

Quantity of containers received

Unloading

Number of orders picked

In-house delivery

Distance and square meters of pallets
throughput

Packing

Number of orders picked

Picking

quantity of containers

Loading

quantity of containers

Prepare shipments

Pack at KD center or
vender(s)
Packing
Prepare outbound
shipments

Quantity of containers received

Storing (for
outbound)

Receiving

Arrive at KD center or
vender(s)

Activity cost drivers

79

Table 23 (cont’d)
Processes

Detail Processes

Activities

Activity cost drivers

Ship to domestic
Consolidation center
(CC)

Loading

Quantity of containers loaded

Arrive at CC

Unloading

Quantity of containers Unloaded

Moving containers to
feeding area

Quantity of containers loaded

Loading

Quantity of containers loaded

Line feeding

Quantity of containers loaded

Unloading empty
RPCs from
workstation

Quantity of containers Unloaded

Moving empty RPCs
to dunnage sort area

quantity of containers

Storing empty RPCs

Space occupied and time required

Prepare backhaul
shipments for RPCs

Counting and
checking the order,
etc)

Quantity of containers

Ship from CC to
reconditioning center
(RPCs)

Unloading

Quantity of containers Unloaded

Unloading

Quantity of containers Unloaded

Inspection

Quantity of containers inspected

Reconditioning
(cleaning, repairing

Quantity of containers cleaned

Storing empty
containers

Space occupied and time required

Prepare line feeds
Outbound
(Ship to the
assembly
plant)

Line-feed

In house return
(RPCs)

Backhaul
logistics
(RPCs only)

After use
Process

Arrive at reconditioning
center Reconditioning
process (RPCs)

5.1.4. Transportation activities and cost drivers
As shown in Table 24, transportation activities include driving, shipping, custom
related fees, equipment and energy uses.

Expendable container system is only

considering forward transportation and after-use driving activities, but reusable and
80

rental systems include backhaul (reverse) transportation and custom related fees during
returning process.
Table 24: Transportation activities and activity cost drivers considered for
calculation
Processes

Outbound
Transportation
(Ship to
plants)

Backhaul
Transportation
(reusables
only)

After use

Detail Processes

Activities

Activity cost drivers

Drive to KD center or
vender(s)

Drive (dispatch the
truck)

Distance and volume efficiency of
truck (quantity of cartons per truck)

Drive to port

Drive (dispatch the
truck)

Distance and volume efficiency

Ship from export port to
import port

Ship

Distance and volume efficiency of
sea container (quantity of cartons
per pallet & sea container)

Drive from port to
consolidation center
(CC)

Drive (dispatch the
truck)

Distance and volume efficiency

Drive to port

Drive (dispatch the
truck)

Distance and container quantity

Ship from CC to
reconditioning center

Ship

Customs clearance at
arrival port

Customs clearance
and brokerage
Drive (send to
Drive to reconditioning
reconditioning
center
center)
Drive to
Drive (dispatch the
Disposal/recycling
truck)

Distance and container quantity
(quantity of cartons per pallet &
sea container)
Customs clearance cost per
container
Distance and container quantity
Distance

5.1.5. After-use activities and cost drivers
As shown in Table 25, after use activities include all the processes related to the
disposal and recycling.

The main cost drivers are weight of containers disposed or

recycled and will consume labor and equipment for the process. Rental systems do
include this activity because all costs associated with after-use activity are included in
the purchase cost.

81

Table 25: After-use activities and activity cost drivers considered for calculation
Processes
After use

5.2.

Activities

Resource drivers

Activity cost drivers

Disposal

Weight of material

Weight of containers disposed

Recycling

Weight of recyclable material

Weight of containers recycled

Packaging system cost calculations
Total packaging system costs for three systems were calculated based on

identified activity cost drivers and resources consumed. The activity cost calculation
models for three systems (expendable, reusable and rental packaging system) are
explained. To clarify the acronyms used for the calculation, Table 26 provides the
definitions of acronyms.
Table 26: Definitions of acronyms used in calculations
CC: Container cost ($/container): purchasing cost of an expendable container (ECC) or a
reusable container (RCC)
DRC: Daily rental cost ($/container/day)
DD: Delivery distance (mile)
CR = Constant rate per mile, $/mile
FS = frequency of supply, days
ADV: Average daily volume (containers/day)
CW: Container weight (lbs/container): weight of container
PW: Product weight (lbs/part): weight of product
PQ: Pack quantity (parts/container): quantity of products in a container
CRR: Container return rate (percent/year, reusables only): return rate of reusables per year
BCV: Backhaul container volume factor (percent/container, reusables only): percent volume of
reusable nesting or collapsible containers during backhauling process
DCL: Designed container life (years, reusables only): container life
PVF: Peak volume factor due to Buffers and safety stocks (percent of daily demand)
CT: Container cycle time (days)
CCF: Custom Charges ($/FCL 40ft)
CCC: Custom Charges ($/container)
RC: Reconditioning cost ($/container)
WDC: Waste disposal cost ($/container)
CDQ: Changes in demand quantity (containers/day)

5.2.1. Expendable packaging system cost (EPSC)
Expendable packaging system cost is sum of five types of cost: container cost,
logistics administration, transportation, warehousing, and after-use cost and minus
recycling revenue for expendable packaging materials.
82

All cost unit is $/container.

‫ ܥܵܲܧ‬ൌ ‫ ܥܥܧ‬൅ ‫ ܥܣܧ‬൅ ‫ ܥܶܧ‬൅ ‫ ܥܹܧ‬൅ ‫ ܥܣܧ‬െ ‫ܧܴܧ‬
Where
EPSC = expendable packaging system cost, $/container
ECC= expendable container cost, $/container
EAC = logistics administration cost, $/container
ETCF= outbound transportation cost for expendable container system, $/container
EWC= warehousing cost for expendable container system, $/container
EAC = after-use cost (disposal) for expendable container system, $/container
ERE = recycling revenue for expendable container system, $/container
5.2.1.1. ECC= expendable container unit cost
Expendable container costs include container cost, repacking material cost (80
percent of original container cost), and reusable container costs (for feeding the parts at
the assembly plant). For example, container cost (CC) is $3.7 if a container unit cost is
$2.0, a container cost for repacking is $1.6, and a reusable container cost is $0.1
($10/100 times). Later, procurement costs (e.g. 5 percent of CC or $0.155) will be
added to logistics administration cost (EAC) as purchasing activity related costs such as
planning, supplier management, ordering, etc.
‫ܥܥܧ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ‫ ܥܥ‬൅ ‫ܥܥ‬௥ ൅ ‫ܥܥ‬௠
Where
ECC= expendable container cost, $/container
CC = container cost, $/container
CC୰ = repacking material cost, $/container

CC୫ = reusable container costs, $/container
83

5.2.1.2. EAC = logistics administration cost for expendable packaging system
Logistics administration cost is related to the time spend for paperwork (or data
input) and labor is the key resource to consume.

Note that labor rates vary depending

on the location. Table 27 shows standard administration time and cost for each
container type.

Table 27: Standard administration time and cost for each container
Container
types

Observed
total time per
container
(hours)

Cost
($/container)

EXPs

0.75

$ 0.17

RPCs

1.05

$ 0.28

RENs

0.88

$ 0.25

Activities involved
Purchasing (Order requests from customer,
scheduling, etc.), counting, checking, and shipping
paperwork (numbers, destinations, truck scheduling,
bill of freight preparation, etc.)
Purchasing (Order requests from customer,
scheduling, etc.), counting, checking, and (forward
and backhaul) shipping paperwork (numbers,
destinations, truck scheduling, bill of freight
preparation, etc.)
Purchasing (Order requests from customer,
scheduling, etc.), counting, checking, and (forward
and backhaul) shipping paperwork (numbers,
destinations, truck scheduling, bill of freight
preparation, etc.)

For example, if labor rate in Korea is $100 per day, the hourly rate should be
$100/day / 8 hours = $12.5/hour.

Labor rate in the US is applied much higher which is

$200 /8 hours = $25/hour. The cost of EAC per container is suggested as:
ሺܶ‫ܥ‬௢ ൈ ‫ܴܮ‬௢ ሻ ൅ ሺܶ‫ܥ‬௟ ൈ ‫ܴܮ‬௟ ሻ
‫ܥܣܧ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ
ൌ
‫ܥܸܣ‬

௛௢௨௥

ൈ

$

௖௬௖௟௘
௛௢௨௥
௖௢௡௧௔௜௡௘௥௦
௖௬௖௟௘

Where
EAC = logistics administration cost, $/container
TCo = sum of time required for logistics administration activity at overseas, hours/cycle
TCl = sum of time required for logistics administration activity at local, hours/cycle
84

LRo = labor rate for administration per hour at overseas, $/hour
LRl = labor rate for administration per hour at local, $/hour
AVC = average container volume during 1 cycle (containers/cycle)
5.2.1.3. ETC= outbound transportation cost for expendable container system,
$/container
Transportation cost is sum of total costs incurred by forward transportation
activities including drive, shipping and custom related fees ($/container).
for truck Drive also vary depending on the location.

Mileage rates

For example, mileage rate in

Korea is $3.00 per mile, but it is $1.5 per mile in the US due to gas price difference.
‫ ܥܶܧ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ

ሺ‫ܯ‬௅ଵ ൈ ‫ܦܦ‬௅ଵ ሻ ൅ ሺ‫ܯ‬ௌ ൈ ‫ܦܦ‬ௌ ሻ ൅ ሺ‫ܯ‬௅ଶ ൈ ‫ܦܦ‬௅ଶ ሻ
$/݈݉݅݁ ൈ ݈݉݅݁
ൌ
௖௢௡௧௔௜௡௘௥௦
‫ ܵܨ‬ൈ ‫ܸܦܣ‬
݀ܽ‫ݏݕ‬ⅹሺ
ሻ
ௗ௔௬

Where,
ETC= outbound transportation cost for expendable container system, $/container
Mx = constant rate per mile, $/mile (MLx: constant rate per mile for in-land Drive, Ms:
constant rate per mile for ocean shipping)
DDx: delivery distance, mile (DDLx: in-land delivery distance, mile, DDs : ocean delivery
distance, mile)
FS = frequency of supply, days
ADV: average daily volume of containers, containers/day
Load efficiency of containers on the pallet, pallets on a truck, and pallets on a
sea container are both important factors to calculate total transportation costs.

All

containers are shipped using the standard pallet (1140x980mm) and 5 ton truck which
can load up to 10 pallets.

Sea container holds 48 pallets at a time. See Table 28.

85

Table 28: Loaded containers per 5 ton truck and container
Container
Type

Containers
/ pallet

pallets
/ 5 ton
truck

Contain
ers /5
ton
truck

Containers/
5 ton truck if average
load efficiency is 80
percent

Pallets per
container
(WⅹDⅹL)

Containers /
sea
container

A

120

10

1,200

960

2ⅹ2ⅹ12

5,760

B

60

10

600

480

2ⅹ2ⅹ12

2,880

C

39

10

390

312

2ⅹ2ⅹ12

1,872

D

30

10

300

240

2ⅹ2ⅹ12

1,440

E

24

10

240

192

2ⅹ2ⅹ12

1,152

F

15

10

150

120

2ⅹ2ⅹ12

720

Figure 9 shows how to load containers in a sea container.

Load efficiency is

89.2 percent for this illustration.

Figure 9: An example of loaded containers in a 40 feet sea container
5.2.1.4. EWC= warehousing cost for expendable container system, $/container
Warehousing cost is the sum of total costs incurred by warehousing activities
including Receive, in-house handling (picking, loading, unloading, packing, repacking,
dispatching, moving, storing, consolidation, putting-away, etc.), equipment, storage and
energy uses ($/container)
Calculating warehousing costs is the most complicate due to various activities
86

and cost drivers. It should be noted that most warehousing activities during the
international shipping requires a pallet load as the basic unit of activity.
‫ܥܹܧ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ෍ ܹ‫ܥ‬௜
Where,
EWC = warehousing cost for expendable container system, $/container
ܹ‫ܥ‬௜ = cost required for each warehousing activity, $/container
And ܹ‫ܥ‬௜ can be described as follows.

ܹ‫ܥ‬௜ ൌ ܹ‫ܥ‬௣௜ ൅ ܹ‫ܥ‬௣௜ ൅ ܹ‫ܥ‬௠ ൅ ܹ‫ܥ‬௟ ൅ ܹ‫ܥ‬௣௟ ൅ܹ‫ܥ‬௣௔ ൅ ܹ‫ܥ‬௦௢ ൅ ܹ‫ܥ‬௦௧

Table 29 describes the common activities (ܹ‫ܥ‬௜ ሻ and activity cost drivers carried
out for warehousing in a 3 PL logistics company.
Note that only resources consumed for containers and packaged products are
considered in this equation because of this research purpose to calculate only activity
costs related to container movement, labor and machine costs differ depending on the
location of activity performed. Table 30 is warehousing activity cost calculation
formulae.

Where,
ܶ௫ : Time required to complete X activity for a container, hour
‫ܮ‬௫ : Labor costto complete X activity for a container, $/hour

‫ܧ‬௫ : Equipment costto complete X activity for a container, $/hour
ܴ௦௧ : Store rate per hour, $/hour-ft2

‫ܣ‬௖௢௡௧௔௜௡௘௥ : Area of a container, ft2/container

87

Table 29: Main warehousing activities and activity cost drivers
Activities
ܹ‫ܥ‬௜௖ (Inspection/checking)
ܹ‫ܥ‬௦௧ (Storing)
ܹ‫ܥ‬௣௜ (Picking)
ܹ‫ܥ‬௠௢ (Moving)

ܹ‫ܥ‬௟௢ (Loading/ Unloading)

ܹ‫ܥ‬௣௞ (Packing, repacking &
labeling)
ܹ‫ܥ‬௣௟ (Palletizing /
Unpalletizing)
ܹ‫ܥ‬௦௢ (Sorting)

Resources

Activity cost
drivers

Time & labor required for inspection/checking a
container
Space occupied and time required
Time, labor and equipment required to pick a
container
Time, labor and equipment required to move a
container
Time, labor and equipment required to load/unload
a container

Quantity of
containers

Time and labor required to pack/unpack a container
Time, labor and equipment required to
palletize/unpalletize a container
Time and labor required to sort a container

Table 30: Warehousing activity cost calculation formulae
Activities

formulae

ܹ‫ܥ‬௜௖ (Inspection/checking)
ܹ‫ܥ‬௟௢ (Loading/ Unloading)
ܹ‫ܥ‬௦௧ (Storing)
ܹ‫ܥ‬௣௜ (Picking)
ܹ‫ܥ‬௦௢ (Sorting)
ܹ‫ܥ‬௠௢ (Moving)

ܹ‫ܥ‬௜௖ ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ܶ௜௖ ൈ ‫ܮ‬௜௖
ܹ‫ܥ‬௟௢ ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ܶ௟௢ ൈ ሺ‫ܮ‬௟௢ ൅ ‫ܧ‬௟௢ ሻ
ܹ‫ܥ‬௦௧ ሺ$/hourሻ ൌ ܴ௦௧ ൈ ‫ܣ‬௖௢௡௧௔௜௡௘௥ ൈ ܶ௦௧
ܹ‫ܥ‬௣௜ ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ܶ௣௜ ൈ ሺ‫ܮ‬௣௜ ൅ ‫ܧ‬௣௜ ሻ
ܹ‫ܥ‬௦௢ ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ܶ௦௢ ൈ ‫ܮ‬௦௢
ܹ‫ܥ‬௠௢ ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ܶ௠௢ ൈ ሺ‫ܮ‬௠௢ ൅ ‫ܧ‬௠௢ ሻ

ܹ‫ܥ‬௣௟ (Palletizing / Unpalletizing)

ܹ‫ܥ‬௣௟ ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ܶ௣௟ ൈ ሺ‫ܮ‬௣௟ ൅ ‫ܧ‬௣௟ ሻ

ܹ‫ܥ‬௣௞ (Packing, repacking & labeling)

ܹ‫ܥ‬௣௞ ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ܶ௣௞ ൈ ሺ‫ܮ‬௣௞ ሻ

Note that storing cost (ܹ‫ܥ‬௦௧ (storing)) is depending on the warehouse rental (or
owning) cost per area and day.

For example, if rental cost per m2 is $4.9/day, pallet

area is 1.12 m2, number of containers on a pallet are 120, and stacking number is 2, the
daily rate for the a container will be $4.9/day x 1.12 m2 / (120 x 2) = $0.023/day.
31 is actual data used for the calculation.

88

Table

Table 31: Warehousing cost calculation
Unit cost ($/hour)
Korea
ܹ‫ܥ‬௜௖
(Inspection/checking)
ܹ‫ܥ‬௣௜ (Picking)

ܹ‫ܥ‬௠௢ (Moving)
ܹ‫ܥ‬௟௢ (Load/ Unload)
ܹ‫ܥ‬௣௞ (Packing,
repacking & labeling)
ܹ‫ܥ‬௣௟ (Palletizing /
Unpalletize)
ܹ‫ܥ‬௦௢ (Sorting)

Labor

Machine

11.4

US
Labor

Machine

No of activities

Observed
average activity
time per pallet
(sec)

EXP

RPC

REN

EXP

RPC

REN

110

1

2

1

0

1

1

22.7

Korea

US

11.4
11.4
11.4

28.4
28.4
28.4

22.7
22.7
22.7

34.1
34.1
34.1

125
150
90

2
4
6

2
4
9

1
3
6

1
3
8

0
3
9

0
3
9

11.4

28.4

22.7

34.1

1300

1

1

1

1

0

0

11.4

28.4

22.7

34.1

540

2

4

4

2

3

3

11.4

28.4

22.7

34.1

180
5.0~6.5/container

0

1

1

2

1

1

0

1

0

1

1

0

ܹ‫ܥ‬௖௟ (Cleaning) (fixed)

$3.6 per pallet load

For this calculation, all pallets for storage were stacked up to 4.

See Table 32

for the calculations.
Table 32: Example of storing cost calculation
Container
type

A
B
C
D
E

Rental
rate
(KO,
2
m,
day)
4.9
4.9
4.9
4.9
4.9

Rental
rate(US,
2
m , day)

Containers
/Pallet

Stacking
layer

Containers

Day

2.7
2.7
2.7
2.7
2.7

120
60
39
30
15

4
4
4
4
4

480
240
156
120
60

1
1
1
1
1

Unit hourly
Storing
cost per
container
(KR, $)
0.0004
0.0009
0.0013
0.0017
0.0034

Unit hourly
storing cost
per
container
(US, $)
0.0002
0.0005
0.0007
0.0010
0.0019

5.2.1.5. EAU= after use activity cost and revenue for expendable container
system, $/container
After use activity cost and revenue for expendable container system (EAU) is
related to amount of materials disposed or recycled.
‫ ܷܣܧ‬ൌ ‫ ܥܣܧ‬െ ‫ܧܴܧ‬

89

EAU can be calculated as below.

•

EAC: after use cost, $/container: Sum of total costs incurred by disposal activities,
$/container
‫ܥܣܧ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ‫ܴܦ‬ⅹ‫ܹܥ‬

Where,
EAC = after use activity cost for expendable container system, $/container
DR = disposal rate per pound, $/lb
CW = container weight, lbs/container

•

ERE: recycling revenue, $/container: Sum of total revenue incurred by recycling
activities, $/container
‫ ܧܴܧ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ܴܴⅹ‫ܹܥ‬
Where,
‫ = ܧܴܧ‬recycling revenue for expendable container system, $/container
ܴܴ = recycling rate per pound, $/lb

‫ = ܹܥ‬container weight, lbs/container
Table 33 is summary of the after-use activity cost calculation that includes
disposal cost and recycling revenue.
Table 33: After-use activities and cost drivers for expendable container system
Processes

Activities

Resource drivers

Activity cost drivers

Disposal

Weight of material

Weight of containers disposed

Recycling

Weight of recyclable
material

Weight of containers recycled

After-use

Cost unit

$ ൈ lb
containers
$ ൈ lb
containers

5.2.2. Reusable packaging system cost (RPSC)
Similar to expendable packaging system cost, reusable packaging system cost
(RPSC) is sum of five types of cost: container cost, logistics administration,
90

transportation, warehousing, after-use cost and minus recycling revenue for reusable
packaging materials, but RPSC includes backhaul transportation cost for returning used
containers.

All cost unit is $/container.
ܴܲܵ‫ ܥ‬ൌ ܴ‫ ܥܥ‬൅ ܴ‫ ܥܣ‬൅ ܴܶ‫ ܨܥ‬൅ ܴܶ‫ ܤܥ‬൅ ܴܹ‫ ܥ‬൅ ܴ‫ ܥܣ‬െ ܴܴ‫ܧ‬

Where,
RPSC = reusable packaging system cost, $/container
RCC= reusable container cost, $/container
RAC = logistics administration cost, $/container
RTCF= outbound transportation cost for reusable container system, $/container
RTCB= backhaul transportation cost for reusable container system, $/container
RWC= warehousing cost for reusable container system, $/container
RAC = after-use cost for reusable container system, $/container
RRE = recycling revenue for reusable container system, $/container
5.2.2.1.

Reusable container cost
In order to calculate reusable container cost, first of all, the number of containers

to run the system must be calculated (Mollenkopf, et al. 2005).
ܰ ൌ ‫ܶܥ‬ⅹ‫ܸܦܣ‬ⅹ‫ܴܴܥ‬ⅹܸܲ‫ܨ‬
Where,
N = number of containers required
CT = container cycle time, days
ADV = average daily volume, containers/day
CRR = reusable container return rate per year, percent/year

91

PVF = peak volume factor due to Buffers and safety stocks, peak daily volume/average
daily volume
CT is total time that reusable containers require to complete one full rotation
from the point of distribution center.

CRR is replenishing rate, as a percent of N,

because we cannot expect to bring every reusable container back due to various
reasons such as damage, stolen, etc., a certain percent of containers should be refilled.
PVF is a buffer factor to prepare uncertain needs and volume changes in daily basis.
Based on the number of containers required, reusable container cost (RCC) can
be calculated as below.
ܴ‫ ܥܥ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ

‫ܥܷܥ‬ⅹܰ
‫ܸܣ‬ⅹ‫ܮܥܦ‬

Where,
RCC= reusable container cost, $/container
CUC = container unit cost, $/container
N = number of containers required
AV = annual volume, containers/year
DCL = designed reusable container life, years
5.2.2.2.

Logistics administration cost for reusable packaging system
Like expendable packaging system, the logistics administration activity cost

(RAC) is sum of total costs incurred by administration activities for returnable packaging
system including paper works for customer service, order processing, production
planning, procurement, purchasing, forecasting, planning, custom charges, scheduling
and dispatch, and inventory control, general administration and management costs
($/container)
92

Similar to EAC equation, most logistics administration activities are clerical
activities related to labor cost and time.
ሺܶ‫ܥ‬௢ ൈ ‫ܴܮ‬௢ ሻ ൅ ሺܶ‫ܥ‬௟ ൈ ‫ܴܮ‬௟ ሻ
ܴ‫ܥܣ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ
ൌ
‫ܥܸܣ‬

௛௢௨௥

ൈ

$

௖௬௖௟௘
௛௢௨௥
௖௢௡௧௔௜௡௘௥௦
௖௬௖௟௘

Where,
RAC = logistics administration cost, $/container
ܶ‫ܥ‬௢ = Sum of time required for logistics administration activity at overseas, hours/cycle
ܶ‫ܥ‬௟ = Sum of time required for logistics administration activity at local, hours/cycle
‫ܴܮ‬௢ = labor rate for administration per hour at overseas, $/hour
‫ܴܮ‬௟ = labor rate for administration per hour at local, $/hour

‫ = ܥܸܣ‬average volume during 1 cycle (containers/cycle)
5.2.2.3.

Transportation cost for reusable container system
Transportation cost for reusable container system is sum of total costs incurred

by outbound and backhaul transportation activities including driving, shipping, custom
related fees, equipment and energy uses ($/container).

Unlikely expendable container

system that is only considering forward transportation, reusable container system needs
not only to include backhaul transportation, but also to add customs clearance and
brokerage fees, if it is an international trade.
RTCF (outbound transportation cost for reusable container system) is exactly
same as expendable container system.

•

RTCF= outbound transportation cost for reusable container system,
$/container
ܴܶ‫ ܨܥ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ

ሺ‫ܯ‬௅ଵ ൈ ‫ܦܦ‬௅ଵ ሻ ൅ ሺ‫ܯ‬ௌ ൈ ‫ܦܦ‬ௌ ሻ ൅ ሺ‫ܯ‬௅ଶ ൈ ‫ܦܦ‬௅ଶ ሻ
‫ ܵܨ‬ൈ ‫ܸܦܣ‬
93

Where,
RTCF= outbound transportation cost for reusable container system, $/container
ETC= outbound transportation cost for expendable container system, $/container
Mx = constant rate per mile, $/mile (MLx: constant rate per mile for in-land Drive, Ms:
constant rate per mile for ocean shipping)
DDx: delivery distance, mile (DDLx: in-land delivery distance, mile, DDs : ocean delivery
distance, mile)
FS = frequency of supply, days
ADV: average daily volume of containers, containers/day

•

RTCB= backhaul transportation cost for reusable container system,
$/container
Backhaul transportation cost for reusable container system (BTCB) calculation

requires several factors affecting total cost. ݀ is a discount rate for the backhaul
transportation applies at the flat rate of 70 percent of the forward transportation cost.
BVF (Backhaul container volume factor) is a volume factor for the reusable container
which has a function to reduce its volume by nesting or collapsing during backhauling
process.

Container return rate per cycle (CRR) is a percent of containers returned to

the departed point (in this case, 3PL’s distribution center). The RTCB is calculated as
follows:
ܴܶ‫ ܤܥ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ
ൌ
ൌ

ሺ‫ܯ‬௅ଵ ൈ ‫ܦܦ‬௅ଵ ሻ ൅ ሺ‫ܯ‬ௌ ൈ ‫ܦܦ‬ௌ ሻ ൅ ሺ‫ܯ‬௅ଶ ൈ ‫ܦܦ‬௅ଶ ሻ
ⅹ݀ⅹ‫ܨܸܤ‬ⅹ‫ ܴܴܥ‬൅ ‫ܥܥܥ‬
‫ ܵܨ‬ൈ ‫ܸܦ‬
$/݈݉݅݁ ൈ ݈݉݅݁

݀ܽ‫ݏݕ‬ⅹሺ

௖௢௡௧௔௜௡௘௥௦
ௗ௔௬

ሻ

൅

ሺ$/‫ܮܥܨ‬40݂‫ݐ‬ሻ
ܿ‫ݏݎ݁݊݅ܽݐ݊݋‬/‫ܮܥܨ‬40݂‫ݐ‬
94

Where,
RTCB= outbound transportation cost for reusable container system, $/container
Mx = constant rate per mile, $/mile (MLx: constant rate per mile for in-land Drive, Ms:
constant rate per mile for ocean shipping)
DDx: delivery distance, mile (DDLx: in-land delivery distance, mile, DDs: ocean delivery
distance, mile)
FS = frequency of supply, days
ADV = Average daily volume, containers/day
d = discount rate for reusable, percent
BVF = Backhaul container volume factor
CRR = Container return rate per cycle, percent/cycle
CCC: Custom clearance charge, $/container
Custom clearance charge (CCC) is considered only for backhaul logistics (e.g.
reusable container). This includes customs clearance fee which is imposed by local
customs to clear goods, brokerage fee which is charged by a specialized freight broker
agent, and other custom related direct costs. For example, if average custom charges
for reusable containers in 40ft sea container is $909.1 for shipping 1200 reusable
containers, it will be $0.76 per container ($909.1/1200ea = $0.76/container).
5.2.2.4. RWC= warehousing cost for reusable container system, $/container
The cost calculation formula for warehousing cost of reusable container system
(RWC) is almost same as expendable container system, but it requires different
activities as shown in Table 23 which describes the common activities carried out for
warehousing in the logistics company and its activity cost drivers.

95

RWC can be

calculated as follows:
ܴܹ‫ܥ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ෍ ܹ‫ܥ‬௜
Where,
RWC = warehousing cost for reusable container system, $/container
ܹ‫ܥ‬௜ = cost required for each warehousing activity, $/container
5.2.2.5. RAU= after use activity cost and revenue of reusable container system,
$/container
The cost calculation formula for after use activity cost for reusable container
system (RAU) is sum of total costs incurred by reconditioning(cleaning), repairing and
disposal activities as shown in formula.

Recycling revenue is sum of total revenue

incurred by recycling activities same as expendable container system.
ܴ‫ܷܣ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ܴ‫ ܥܣ‬െ ܴܴ‫ܧ‬

•

RAC: after-use activity cost, $/container
After-use activity cost for reusable container system (RAC) includes

reconditioning cost such as cleaning and repairing costs.

Cleaning cost is depending

on many factors such as locations, cleaning technology, equipment, time, shape of
containers, etc.

In this study, researcher uses a fixed observed cost which is $3.6 per

full pallet load based on the company data. For example, the cleaning cost for a pallet
load of containers containing 60 containers will be $3.6/60 = $0.06 per container.
ܴ‫ܥܣ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬ሻ ൌ ‫ܴܦ‬ⅹ‫ ܹܥ‬൅ ሺܴ‫ ݊݋ܿ݁ݎ‬ൈ ܶ‫ ݊݋ܿ݁ݎ‬ሻ
Where
RAC = after-use activity cost for reusable container system, $/container
DR = disposal rate per pound, $/lb.

96

CW = container weight, lbs./container
ܴ௥௘௖௢௡ : Basic reconditioning (cleaning and repairing) rate per container, $/container
ܶ௥௘௖௢௡ : Time required to complete X activity for a container, hour

•

RRE: recycling revenue, $/container
Only small amount of recycling revenue (RRE) is expected compared to

expendable container system if reusable container system works properly.

The

mathematical formula for RRE is same as expendable container system as follows:
ܴܴ‫ ܧ‬ቆ

$
ܴܴⅹ‫ܹܥ‬
ቇ ൌ ௐ஽
ܿ‫ݎ݁݊݅ܽݐ݊݋‬
ൈ ‫ ܮܥ‬ൈ ‫ܴܴܥ‬
஼்

Where
RRE = recycling revenue for reusable container system, $/container
RR = recycling rate per pound, $/lb.
CW = container weight, lbs./container
WD = Working days, 260 days/year
CT = Cycle time, day
CL = Expected container life, year
CRR = Container return rate per cycle (reusables only, return rate of reusables per year)
5.2.3. Reusable Container Rental System Cost (CRSC)
Notable difference between owned reusable and rental container system is
calculation of basic container costs, warehousing costs and after-use costs.
‫ ܥܴܵܥ‬ൌ ‫ ܥܴܥ‬൅ ‫ ܥܣܥ‬൅ ‫ ܨܥܶܥ‬൅ ‫ ܤܥܶܥ‬൅ ‫ ܥܹܥ‬൅ CAC
Where
CRSC = container rental system cost, $/container/cycle

97

CRC= container rental cost, $/container/cycle
CAC = logistics administration cost, $/container/cycle
CTCF= outbound transportation cost for reusable container rental system,
$/container/cycle
CTCB= backhaul transportation cost for reusable container rental system,
$/container/cycle
CWC= warehousing cost for reusable container rental system, $/container/cycle
CAU= after use activity cost for reusable container rental system, $/container
5.2.3.1.

Container rental cost
Reusable container rental cost (CRC) can be calculated as below.
‫ ܥܴܥ‬ሺ$/ܿ‫ݎ݁݊݅ܽݐ݊݋‬/ܿ‫݈݁ܿݕ‬ሻ ൌ ‫ܴܷܥ‬ⅹ‫ܶܥ‬ⅹሺ1 ൅ ሺ1 െ ‫ܴܴܥ‬ሻሻⅹܸܲ‫ܨ‬

Where
CRC= container rental cost per cycle, $/container-cycle
CUR = container unit rental rate, $/container-day
CT = container cycle time, days/cycle
CRR = reusable container return rate
PVF = peak volume factor due to buffers and safety stocks, peak daily volume/average
daily volume
CUR contains basic purchasing cost, depreciation, financial costs and profit for a
rental company.

For example, the basic purchasing cost for a reusable container is

$2/container, depreciation period is 3 years, interest rate is 7.5 percent and profit
margin is 5 percent, daily rental cost will be ($2/365 X 0.075) + ($2/365/3)) + (($2/365 X
0.075) + ($2/365/3)) X 0.1 = $0.0025/container/day.

98

‫ ܴܷܥ‬ሺ$/ܿ‫ݎ݁݊ ܽݐ݊݋‬/݀ܽ‫ݕ‬ሻ ൌ

ܴ‫ܥܥ‬ⅹ‫ܴܫ‬
ܴ‫ܥܥ‬
ܴ‫ܥܥ‬ⅹ‫ܴܫ‬
ܴ‫ܥܥ‬
൅൬
൰൅ሺ
൅
ሻⅹ‫݉݌‬
365
365
365/݀‫݌‬
365/݀‫݌‬

Where,
CUR = container unit rental rate, $/container/day
IR = interest rate
dp = depreciation period
pm = profit margin
N = number of containers required per day
5.2.3.2.

Other costs
Logistics administration cost (CAC) and warehousing cost (CWC) activities are

very similar, but slightly different depending on involved activities.

This is because

many rental companies do some administration and warehousing activities for their
customers, so these activity costs are already included in their daily rental cost (See
Table 34). The outbound transportation (CTCF), backhaul transportation (CTCB) and
the after use activity cost and revenue (CAU) are identical with reusable packaging
system. The formulae for rental packaging system are summarized in Table 34.

99

Table 34: Summary of the reusable container rental cost calculation
Cost, $/container
CAC (logistics administration cost)
CTCF (outbound transportation cost)
CTCB (backhaul transportation cost)

CWC (warehousing cost)
CAU (after use activity cost and
revenue)

Formulae
ሺܶ‫ܥ‬௢ ൈ ‫ܴܮ‬௢ ሻ ൅ ሺܶ‫ܥ‬௟ ൈ ‫ܴܮ‬௟ ሻ
ൌ
‫ܥܸܣ‬
ሺ‫ܯ‬௅ଵ ൈ ‫ܦܦ‬௅ଵ ሻ ൅ ሺ‫ܯ‬ௌ ൈ ‫ܦܦ‬ௌ ሻ ൅ ሺ‫ܯ‬௅ଶ ൈ ‫ܦܦ‬௅ଶ ሻ
൅ ‫ܧܥܥ‬
‫ ܵܨ‬ൈ ‫ܸܦܣ‬
ሺ‫ܯ‬௅ଵ ൈ ‫ܦܦ‬௅ଵ ሻ ൅ ሺ‫ܯ‬ௌ ൈ ‫ܦܦ‬ௌ ሻ ൅ ሺ‫ܯ‬௅ଶ ൈ ‫ܦܦ‬௅ଶ ሻ
ൈ ݀ ൈ ‫ ܨܸܤ‬ൈ ‫ܴܴܥ‬
‫ ܵܨ‬ൈ ‫ܸܦܣ‬
൅ ‫ܧܥܥ‬
ሺܹ‫ܥ‬௣௜ ൅ ܹ‫ܥ‬௣௜ ൅ ܹ‫ܥ‬௠ ൅ ܹ‫ܥ‬௟ ൅ ܹ‫ܥ‬௣௟ ൅ܹ‫ܥ‬௣௔ ൅ ܹ‫ܥ‬௦௢ ൅ ܹ‫ܥ‬௦௧ ሻ
‫ܥܨ‬
൅
‫ܥܸܣ‬
Reconditioning cost: ‫ ܴܦ‬ൈ ‫ ܹܥ‬൅ ሺܴ௥௘௖௢௡ ൈ ܶ௥௘௖௢௡ ሻ
Recycling revenue: ܴܴ ൈ ܰ ൈ ‫ܹܥ‬ⅹሺ1 െ ‫ܴܴܥ‬ሻ

5.2.4. Summary
Table 35 compared different elements of activities of each packaging system.
Generally speaking, reusable and rental packaging system tend to require more
complex and many activities compared to expendable packaging system.

All

cost formulae are summarized in Table 36.

Table 35: Comparison of different cost elements of three packaging systems
Area of activity
costs
Container
purchasing
Administration

Transportation
Warehousing

After use

Expendable packaging
system
(EXPS)
Depends on no. of
containers needs
Administration related
costs including
purchasing, receiving
and sending
Forward transportation
cost only
Only need forward
logistics process

Disposal and recycling
process

Packaging systems
Reusable packaging
Rental packaging system
system
(RENS)
(RPCS)
Depends on no. of
Depends on basic rental cost
containers needs
and no. of containers needs
Need additional activity for
Need additional
replenishment process, but
administration activity for
need less activity for
replenishment process
purchasing
Forward and backhaul
Forward and backhaul
transportation cost
transportation cost
Need additional activity
Need additional activity for
for backhaul logistics
backhaul logistics
Affect less than expendable
Affect less than
system.
expendable system.
Replenishment process
Replenishment process
including Reconditioning is
including
required, but parts of the
Reconditioning is
process is included in rental
required
cost

100

Table 36: Summary of packaging system activity cost formulae
Formulae
Area of
activity
Containe
r
purchasi
ng
Administ
ration
Transportation
Warehousing
After use

Expendable packaging
system
(EXPS)

Reusable packaging system
(RPCS)

‫ ܥܥܧ‬ൌ ‫ ܥܥ‬൅ ‫ܥܥ‬௥ ൅ ‫ܥܥ‬௠

ܴ‫ ܥܥ‬ൌ

‫ܥܣܧ‬
ሺܶ‫ܥ‬௢ ൈ ‫ܴܮ‬௢ ሻ ൅ ሺܶ‫ܥ‬௟ ൈ ‫ܴܮ‬௟ ሻ
ൌ
‫ܥܸܣ‬

‫ܰܥܷܥ‬
‫ܮܥܦܸܣ‬

ܴ‫ ܥܣ‬ൌ

ሺܶ‫ܥ‬௢ ൈ ‫ܴܮ‬௢ ሻ ൅ ሺܶ‫ܥ‬௟ ൈ ‫ܴܮ‬௟ ሻ
‫ܥܸܣ‬

‫ܥܶܧ‬
ሺ‫ܯ‬௅ଵ ൈ ‫ܦܦ‬௅ଵ ሻ ൅ ሺ‫ܯ‬ௌ ൈ ‫ܦܦ‬ௌ ሻ ൅ ሺ‫ܯ‬௅ଶ ൈ ‫ܦܦ‬௅ଶ ሻ
ൌ
‫ ܵܨ‬ൈ ‫ܸܦܣ‬
൅ ‫ܧܥܥ‬

‫ ܥܹܧ‬ൌൌ ෍ ܹ‫ܥ‬௜
‫ ܥܣܧ‬ൌ ‫ ܴܦ‬ൈ ‫ܹܥ‬
‫ ܧܴܧ‬ൌ ܴܴ ൈ ‫ܹܥ‬

ܴܶ‫ ܨܥ‬ൌ

ሺ‫ܯ‬௅ଵ ൈ ‫ܦܦ‬௅ଵ ሻ ൅ ሺ‫ܯ‬ௌ ൈ ‫ܦܦ‬ௌ ሻ ൅ ሺ‫ܯ‬௅ଶ ൈ ‫ܦܦ‬௅ଶ ሻ
‫ ܵܨ‬ൈ ‫ܸܦܣ‬
൅ ‫ܧܥܥ‬

ܴܶ‫ܤܥ‬
ሺ‫ܯ‬௅ଵ ൈ ‫ܦܦ‬௅ଵ ሻ ൅ ሺ‫ܯ‬ௌ ൈ ‫ܦܦ‬ௌ ሻ ൅ ሺ‫ܯ‬௅ଶ ൈ ‫ܦܦ‬௅ଶ ሻ
݀‫ܴܴܥܨܸܤ‬
ൌ
‫ ܵܨ‬ൈ ‫ܸܦܣ‬
൅ ‫ܧܥܥ‬

ܴܹ‫ ܥ‬ൌ ෍ ܹ‫ܥ‬௜

ܴ‫ ܥܣ‬ൌ ‫ܹܥܴܦ‬
ܴܴ‫ ܧ‬ൌ ܴܴ ൈ ܰ‫ܹܥ‬ሺ1 െ ‫ܴܴܥ‬ሻ

101

Rental packaging system
(RENS)
‫ ܥܴܥ‬ൌ ‫ܨܸܴܴܲܥܶܥܴܷܥ‬
CACൌ

ሺ்஼೚ ൈ௅ோ೚ ሻାሺ்஼೗ ൈ௅ோ೗ ሻ
஺௏஼

‫ܨܥܶܥ‬
ሺ‫ܯ‬௅ଵ ൈ ‫ܦܦ‬௅ଵ ሻ ൅ ሺ‫ܯ‬ௌ ൈ ‫ܦܦ‬ௌ ሻ ൅ ሺ‫ܯ‬௅ଶ ൈ ‫ܦܦ‬௅ଶ ሻ
ൌ
‫ ܵܨ‬ൈ ‫ܸܦܣ‬
൅ ‫ܧܥܥ‬
‫ܤܥܶܥ‬
ሺ‫ܯ‬௅ଵ ൈ ‫ܦܦ‬௅ଵ ሻ ൅ ሺ‫ܯ‬ௌ ൈ ‫ܦܦ‬ௌ ሻ ൅ ሺ‫ܯ‬௅ଶ ൈ ‫ܦܦ‬௅ଶ ሻ
ൌ
݀‫ܨܸܤ‬
‫ ܵܨ‬ൈ ‫ܸܦܣ‬
൅ ‫ܧܥܥ‬

‫ ܥܹܥ‬ൌ ෍ ܹ‫ܥ‬௜

‫ ܥܣܥ‬ൌ ‫ ܴܦ‬ൈ ‫ܹܥ‬
‫ ܧܴܥ‬ൌ ܴܴܰ ൈ ‫ܹܥ‬ሺ1
െ ‫ܴܴܥ‬ሻ

CHAPTER 6
COMPARATIVE REGRESSION ANALYSIS USING THE ABC MODEL
For the purpose of this study, regression analysis is used to identify and quantify
the impact factors that determine packaging system costs in an international supply
chain.

ABC costs for five logistical activity areas are calculated based on the formulae

explained throughout 5.2.

Sixteen independent variables are identified and tested.

The purpose of the regression analysis is to seek the relative differences among
different packaging systems rather than absolute differences.

It imploys a static

simulation approach used by Mollenkopf, et al. (2005) and is a regression analysis to
compare realtive differences between systems as the independent variables in the
model change.

Dependent variables are the cost differences among reusable (RPCS),

expendable (EXPS) and rental systems (RENS)1.

The cost differences is subtracting

the system cost from one to the other (e.g. reusable system cost – expendable system
cost), thus the results of regression equations mean the amount of the difference
among the systems. Three systems are compared one by one.
Given the elements of costs in Table 35 and equations in Table 36, the
independent variable of time, labor and cost of logistical activities are different among
the expendable, reusable and rental container systems. Multiple “what if” analyses can
be performed to determine the sensitivity of each system to changes in any of the model
variables or parameters.
In order to understand the relative influence of the various factors cooperated in
the model, a combination simulation-regression analysis was undertaken. A static
1

RPCS: Reusable packaging system using reusable container: EXPS: Expendable packaging system using one-way
container: RENS: Rental packaging system using reusable container.

102

simulation approach was employed to create multiple independent observations, based
on the sample data provided by “A” company, as shown in Table 17.
Since the factor values ranged from low to medium to high, a triangular
generating function from Excel was used to generate individual observations for five
factors. The unit cost was basically calculated based on the size of containers.
Five hundred observations were generated and each observation represents a
unique combination of the eleven factors with factors independently following a
triangular distribution. Each of the variables is explained below.
The container unit cost range is based on three package sizes.

The low cost

settings represent a small 380mm x 240mm x 105mm (outside dimension) totes that is
made from either single-wall corrugated fiberboard or injection molded plastic. The
mode settings are based on 480mm x 380mm x 200mm single wall corrugated
fiberboard containers with customized inserts or customized vacuum-formed plastic
containers. The high cost settings are based on a large 960mm x 380mm x 200mm
double wall corrugated fiberboard box with wood supports or a reusable steel rack.
The high cost packages hold the biggest and heaviest parts.
For purposes of the regression analysis, the container cost factor is combined
into a single variable representing the ratio of the container costs (reusable unit cost
divided by expendable unit cost).
As shown in Table 17, the low cost (small) container ratio is 2/.5 = 4; the
medium cost container ratio is 5.0/1.5=3.3; and the high cost (large) container ratio is
10/3 = 3.3. For rental system, reusable container is used at the daily rate of reusable

103

container divided by 100. The cost ratio approach enhances the generalizability of the
research by making the relative cost differential the important variable.
Shipping distance was varied from 500 miles (low) to 5,000 miles (mode) to
10,000 miles (high). The high distance, 10,000 miles, was chosen considering the
approximate distance between the U.S. West coasts and Korea East coasts.
In-land delivery distance was varied from 100 miles (low) to 300 miles (mode) to
500 miles (high) considering the distance from the port to the consolidation center. The
high distance, 500 miles, was chosen considering the approximate distance between
the U.S. South coasts and consolidation centers of clients in the middle of the US.
The range used for container quantity on a pallet varied from 15 parts per
container (low) to 60 (mode) and 120 (high) based on actual data.
Cycle time ranged from 30 days (low), 90 days (mode), and 180 days (high).
Note that cycle time includes transit time in both directions as well as dwell and queuing
time at both the supplier and customer sites. While many automobile manufacturers aim
for a short cycle, poorly managed systems require more time.
Average daily volume ranged from 1,000 (low), 5,000 (mode), and 10,000 (high)
parts per day, while buffers and safety stocks was estimated as a percentage of
average daily volume, ranging from 2 percent (low) to 5 percent (mode) to 10 percent
(high).
For both reusable and rental system, three variables are added to determine the
impact of return rate, backhaul volume factor and designed container life.
Return rate ranged from 80 (low), 90 (mode), and 95 (high) container per cycle.
Backhaul volume factor was used for foldable and/or nesting type of containers. The

104

factor was estimated as a percentage of decreased volume, ranging from 0.5 (low) to
0.7 (mode) to 1.0 (high). Designed container life is one of the most important cost
factors for reusable containers.

This ranged from 2 (low), 3 (mode), and 10 (high)

years.
6.1. Fixed and assumed data
Cost and other operating assumptions were also necessary to complete the
analysis.

The cost model is based on the assumption that transportation occurs once

every day for reusable, rental and expendable systems. Fixed and assumed data used
of the ABC modeling are provided by the company “A” and “B,” and shown in Table 37.

Table 37: Fixed and assumed data used for the cost modeling
Foreign country, $/hour
US, $/hour
Foreign country, $/hour
Standard equipment rate
US, $/hour
2
Foreign country, $/m -day
Storing rate
2
US, $/m -day
53`, Local (US)
mileage rate (truck)
5 ton, oversea (KO)
Transportation
Shipping mileage rate ($/mile)
cost
Discount mileage rate for returns, percent
Reusable volume factor, percent
disposal rate, $/lb
Expendable container
recycling rate, $/lb
After-use
costs
disposal rate, $/lb
Reusable container
recycling rate, $/lb.
Frequency of supply
number per day
Basic cleaning rate
$/pallet load
Working days, year
Days
Standard labor rate

11.4
22.7
28.4
34.1
4.9
2.7
5.0
3.0
0.3
30
10
0.010
0.015
0.015
0.020
1
3.6
260

Labor cost is related to the difference in time required to handle packages in
each system. The handling time is calculated for the ABC calculation (see section 5.1
for a review of the different processes and activities).

The difference in operations and

the time estimates are based on observations and measurements from company “A”

105

and company “B,” in 2010, described in section 5.1.

Standard labor rate in a foreign

country (Korea) is assumed to be $11.4 /hour, but the US is 22.7 $/hour. Equipment
rate, storing rate and transport rate are all different depending on where the container is
handled. See Table 37 for details.
The shipping mileage rate is assumed to be 0.3 $/mile and 30 percent of discount
transport rate is applied for a return trip. Ten percent of volume factor is added for the
reusable container since most reusable containers require thicker wall and rib structure,
which take up extra space.
For the reusable option, empty containers are cleaned before sending them back
to warehouse, so 3.6 $/pallet load of basic cleaning rate is applied. Regardless of
system types, frequency of supply is 1 per day and working days are 260 days per year.
Due to loss and damage, the return rate varies from 80 percent to 95 percent. It
is also assumed that the weight characteristics are the same for the packaging systems.
For simplification, the initial analyses assume a 2-year project life for the
reusable container system, due to the fact that some firms use a 2-year payback period
justification basis and others (especially in the automotive industry) base packaging
decisions on a product life of two model years.

This is a bias in favor of expendable

packages since most reusable shipping containers can last much longer.
6.2. Base regression analysis results
Tables 38 through 40 compare the cost of RPCS (reusable packaging system),
EXPS (expendable packaging system) and RENS (rental packaging system).

They

compare the different variables which impact on the cost differentials of three different
packaging systems.

106

When the RPCS and EXPS are compared in Table 38, cost ratio between
reusable and expendable containers, designed container life, cycle time, custom
charges, shipping distance, container weight, average daily volume, and back haul
volume factor are variables that have significant impact on the packaging system cost.

Table 38: Base regression analysis results comparing between reusable
packaging system and expendable packaging system costs
RPCS – EXPS

Standardized
coefficients (Beta)

Variables
(Constant)
RPC / EXP

-2.908

0.004

0.656

22.872

0.000

-11.269

0.000

Cycle time (days)

0.231

8.136

0.000

Custom Charges ($/ea.)

0.280

5.867

0.000

Shipping distance (miles)

0.155

5.337

0.000

Container weight (lbs./container)

0.100

3.533

0.000

-0.145

-3.029

0.003

0.085

2.983

0.003

Container quantity per pallet (tote/pallet)

-0.023

-0.800

0.424

Peak volume factor

-0.013

-0.473

0.637

Return rate (percent/year, RPC)

-0.003

-0.094

0.925

Average daily Volume (containers/day)
Backhaul volume factor

※

Sig.

-0.321

Designed container life (year, RPC)

R: .878
R Square: .772
Adjusted
R Square: .763

t

Note: Dependent variable: reusable packaging system cost – expendable packaging system cost

Statistically, based on the initial observations, not all factors are significantly
contributing the system cost differential.

Container cost ratio (reusable container cost /

expendable container cost) is the largest cost contributor based on the standardized
coefficients (Betas) results, which is a similar result to Mollenkopf, et al. (2005). The
positive relationship with the dependent variable, the cost differential, suggests that as
the relative cost of the reusable container increases, the expendable container systems
are more economically viable.

107

Designed container life, cycle time, custom charge, shipping distance, container
weight, and backhaul volume factor also have positive Beta value, meaning as these
factors increase, the EXPS is more economically viable.
Designed container life is the second important variable in determining which
container system to use. This factor has negative value along with container quantity
per pallet and average daily volume, meaning RPCS is more viable option as RPCS has
longer designed container life, more container quantity per pallet and more average
daily volume than EXPS.
Cycle time is the third most important variable, but still significant impact on
determining packaging systems.

But it is interestng to note that shipping distance is

less statistically significant than cycle time. A longer cycle time tends to increase the
number of containers in the system, so it is a more important factor to contribute to the
total cost of reusable packaging systems.
Custom charges rank fourth in importance. Custom charges largely depend on
the value of the products (not a package), and it is unavoidable for whatever types of
packaging system used during forward transportation.

However, it is a significant

factor for RPCS and RENS because it contributes to the cost of returns.

Container

quantity per pallet, peak volume factor and return rate were statistically not significant
factors in this scenario.
In Table 39, reusable (RPCS) and rental (RENS) packaging systems are
compared.

Interestingly, cycle time and cost ratio are not only the most important

factors that could impact on packaging system decision, but they impact on the system
in opposite way.

108

From the analysis, if cycle time increases, rental is a more viable option because
a company which operates RPCS has to invest more money to purchase reusable
containers.
On the other hand, cost ratio has a negative correlation with the dependent
variable which suggests that as reusable container costs increase, RENS is also
economically less desirable option.

Because RENS essentially use RPCs, increasing

container purchasing cost is likely increase RPC rental cost.

Table 39: Base regression analysis results comparing between reusable
packaging system and rental packaging system costs
RPCS – RENS

R: .862
R Square .743
Adjusted
R Square.733

Standardized
coefficients (Beta)

Variables
(Constant)
Cycle time (days)
RPC / REN
Designed container life (year, RPC)
Container weight (lbs./container)
Return rate (percent/year, RPC)
Backhaul volume factor
Shipping distance (miles)
Container quantity per pallet (tote/pallet)
Custom Charges ($/ea.)
Average daily Volume (containers/day)
Peak volume factor

0.601
-0.610
0.183
-0.088
0.061
0.048
-0.041
-0.022
-0.029
-0.024
0.010

t
-1.004
19.940
-19.876
6.072
-2.911
2.016
1.577
-1.314
-0.734
-0.564
-0.483
0.317

Sig.
0.316
0.000
0.000
0.000
0.004
0.045
0.116
0.190
0.463
0.573
0.630
0.751

※ Note: Dependent variable: reusable packaging system cost – rental packaging system cost

Table 40 compares the EXPS and RENS. In this case, too, container cost ratio
and cycle time are two most important factors and impact opposite way as Table 40,
meaning longer cycle time favors EXPS, and larger cost difference favors RENS.

109

Table 40: Base regression analysis results comparing between expendable
packaging system and rental packaging system costs
EXPS - RENS

R: .928
R Square: .861
Adjusted
R Square: .855

※

Standardized
coefficients (Beta)

Variables
(Constant)
EXP / REN
Cycle time (days)
Custom Charges ($/ea.)
Shipping distance (miles)
Backhaul volume factor
Average daily Volume (containers/day)
Designed container life (year, RPC)
Container weight (lbs./container)
Return rate (percent/year, RPC)
Container quantity per pallet (tote/pallet)
Peak volume factor

-0.673
0.629
0.134
0.061
0.061
-0.086
-0.015
0.010
0.009
-0.008
0.000

t

Sig.

-0.259
-30.234
28.315
3.595
2.740
2.737
-2.318
-0.667
0.444
0.404
-0.341
0.011

0.796
0.000
0.000
0.000
0.007
0.007
0.021
0.506
0.657
0.686
0.734
0.991

Note: Dependent variable: expendable packaging system cost-rental packaging system cost

While these results are interesting in themselves, the three packaging systems
are further analyzed to have better understanding of the sensitivity of the cost
differential and impact of each variable. This process also can help to generalize
research results (Mollenkopf, et al. 2005).
6.3. Results of regression analysis scenarios
Eleven scenarios are developed by altering the values of the independent
factors such as distance, cycle time (doubled and halved), custom clearance charge,
container quantity, average daily volume, backhaul volume factor, designed container
life, and combination of factors.

Results of the sensitivity analysis are presented in

from Table 41 to 74 to illustrate the impacts on the relative cost of packaging systems.
6.3.1.

Scenario 1: Distance doubled

It is considered that distance is one of the most important factors to decide
between EXPS and RPCS.

For every system, while container purchasing cost ratio is

still the most important factor, designed container life, shipping distance, cycle time, and
custom charge remains the most important variables to determine total packaging
110

system cost.

Distance contributes to cycle time greatly and becomes very important

for RPCS and RENS as expected.

Table 41 through Table 43 show the results of the

regression analysis when the distance is doubled.

Table 41: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 1: Distance
doubled)
RPCS-EXPS

R: .894
R Square: .799
Adjusted
R Square: .795

Standardized
coefficients
(Beta)

Variables
(Constant)
RPC / EXP
Designed container life (year, RPC)
Shipping distance (miles)
Cycle time (days)
Custom Charges ($/ea.)
Average daily Volume (containers/day)
Backhaul volume factor
Container weight (lbs./container)
Return rate (percent/year, RPC)
Peak volume factor
Container quantity per pallet (tote/pallet)

t

0.605
-0.272
0.272
0.226
0.327
-0.196
0.104
0.077
-0.010
-0.007
-0.001

Sig.

-3.827
29.551
-13.285
13.147
11.129
9.915
-5.922
5.091
3.749
-0.512
-0.339
-0.046

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.609
0.735
0.963

Table 42: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 1: Distance
doubled)
RPCS – RENS

R: .867
R Square .751
Adjusted
R Square: .745

Standardized
coefficients
(Beta)

Variables
(Constant)
Cycle time (days)
RPC / REN
Designed container life (year, RPC)
Container weight (lbs/container)
Return rate (percent/year, RPC)
Backhaul volume factor
Shipping distance (miles)
Container quantity per pallet (tote/pallet)
Peak volume factor
Average daily Volume (containers/day)
Custom Charges ($/ea)

111

0.614
-0.608
0.185
-0.064
0.050
0.026
-0.020
0.012
-0.007
-0.004
-0.004

t
-1.112
27.096
-26.634
8.118
-2.811
2.189
1.128
-0.868
0.535
-0.294
-0.108
-0.103

Sig.
0.267
0.000
0.000
0.000
0.005
0.029
0.260
0.386
0.593
0.769
0.914
0.918

Table 43: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 1: Distance
doubled)
EXPS - RENS

R: .932
R Square: .868
Adjusted
R Square: .865

Standardized
coefficients (Beta)

Variables
(Constant)
EXP / REN
Cycle time (days)
Shipping distance (miles)
Custom Charges ($/ea.)
Backhaul volume factor
Average daily Volume (containers/day)
Container quantity per pallet (tote/pallet)
Peak volume factor
Return rate (percent/year, RPC)
Container weight (lbs./container)
Designed container life (year, RPC)

-0.632
0.608
0.150
0.194
0.067
-0.098
0.014
-0.012
0.009
0.007
0.001

t
-0.902
-38.285
36.906
8.991
7.271
4.056
-3.667
0.835
-0.749
0.537
0.395
0.082

Sig.
0.368
0.000
0.000
0.000
0.000
0.000
0.000
0.404
0.454
0.592
0.693
0.934

As shown in Table 41 and Table 43, all values of the standardized coefficients
(Betas) for container purchasing cost ratio, cycle time, shipping distance, custom
charges, backhaul volume, and average daily volume decreased since doubling
shipping distance favors EXPS.

Doubling shipping distance is not critical factor of

choosing between RPCS and RENS as much as deciding between RPCS and EXPS or
between RENS and EXPS.
6.3.2.

Scenario 2: Cycle time doubled

Shipping distance is directly related to cycle time and the cycle time contributes
only for reusable and rental packaging systems because EXPS does not have the
return process. Thus, Scenario 2 doubled the cycle time for both RPCS and RENS as
shown in Table 44 through Table 46. As expected, container cost ratio and cycle time
are generally the most important variables. Note that designed container life is an
important factor when comparing between RPCS and EXPS and RPCS and RENS, but
this does not contribute much for between EXPS and RENS. This is because the
RENS is usually based on the unit rental price per certain rental period rather than
112

designed container life.

In many cases, unit rental price is directly based on the

purchasing cost of the container although the higher cost of container does not always
guarantee the longer use life of the container. Shipping distance and custom charge
are more important factors and show positive value, meaning as cycle time increases,
EXPS is more economical option.

Table 44: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 2: Cycle
time doubled)
RPCS – EXPS

R: .917
R Square: .840
Adjusted
R Square: .837

Standardized
coefficients (Beta)

Variables
(Constant)
RPC / EXP
Designed container life (year, RPC)
Cycle time (days)
Custom Charges ($/ea.)
Shipping distance (miles)
Average daily Volume (containers/day)
Backhaul volume factor
Container weight (lbs./container)
Return rate (percent/year, RPC)
Peak volume factor

0.625
-0.429
0.389
0.204
0.123
-0.098
0.056
0.049
-0.019
0.000

t

Sig.

-2.345
34.230
-23.511
21.444
6.940
6.666
-3.315
3.088
2.683
-1.036
-0.011

0.019
0.000
0.000
0.000
0.000
0.000
0.001
0.002
0.008
0.301
0.991

Table 45: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 2: Cycle time
doubled)
RPCS – RENS

R: .866
R Square .750
Adjusted
R Square: .744

Standardized
coefficients (Beta)

Variables
(Constant)
Cycle time (days)
RPC / REN
Designed container life (year, RPC)
Return rate (percent/year, RPC)
Container weight (lbs./container)
Backhaul volume factor
Shipping distance (miles)
Peak volume factor
Container quantity per pallet (tote/pallet)
Custom Charges ($/ea.)
Average daily Volume (containers/day)

113

0.616
-0.610
0.186
0.050
-0.038
0.025
-0.020
-0.007
-0.006
-0.004
-0.003

t

Sig.

-0.964
27.109
-26.652
8.121
2.204
-1.654
1.110
-0.874
-0.310
-0.253
-0.099
-0.090

0.336
0.000
0.000
0.000
0.028
0.099
0.268
0.383
0.756
0.800
0.921
0.928

Table 46: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 2: Cycle time
doubled)
EXPS - RENS

R: .902
R Square: .814
Adjusted
R Square: .810

6.3.3 .

Standardized
coefficients (Beta)

Variables
(Constant)
Cycle time (days)
EXP / REN
Shipping distance (miles)
Custom Charges ($/ea.)
Backhaul volume factor
Return rate (percent/year, RPC)
Average daily Volume (containers/day)
Designed container life (year, RPC)
Peak volume factor
Container weight (lbs./container)
Container quantity per pallet (tote/pallet)

0.686
-0.579
0.060
0.094
0.032
0.026
-0.025
0.015
-0.009
-0.003
-0.001

t

Sig.

-0.684
35.025
-29.458
3.018
2.960
1.613
1.303
-0.786
0.752
-0.472
-0.163
-0.026

0.494
0.000
0.000
0.003
0.003
0.107
0.193
0.432
0.452
0.637
0.870
0.979

Scenario 3: Custom charges removed

As mentioned earlier, custom clearance charges are depending on the value of
the product for forward transportation, and many countries exempt import taxes if
reusable shipping containers are returned to the origin.

However, custom charges

becomes an important issue if the containers are not properly documented.
In this scenario, all custom charges for containers are removed assuming that
the company received tax exemptions or each export and import countries have a
mutual agreement (e.g. Free Trade Agreement) for their returning containers.
Tables 47 to Table 49 show that both cost ratio and cycle time are consistently
most important factors regardless of simulation scenarios. When comparing RPCS
and EXPS, cost ratio and cycle time are positive values, meaning that EXPS is better
option as cost ratio and cycle time increase. Cost ratios are negative for both RPCSRENS and EXPS-RENS, meaning RPCS and EXPS are still heavily favored in this
scenario compared to RENS.

114

Table 47: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 3: Custom
charges removed)
RPCS – EXPS

R: .872
R Square: .760
Adjusted
R Square: .755

※

Standardized
coefficients (Beta)

Variables
(Constant)
RPC / EXP
Designed container life (year, RPC)
Average daily Volume (containers/day)
Cycle time (days)
Shipping distance (miles)
Container weight (lbs./container)
Backhaul volume factor
Return rate (percent/year, RPC)
Peak volume factor
Container quantity per pallet (tote/pallet)

0.686
-0.307
-0.303
0.261
0.163
0.091
0.075
-0.012
-0.007
-0.003

t
-2.500
30.704
-13.710
-13.613
11.748
7.234
4.069
3.357
-0.548
-0.295
-0.129

Sig.
0.013
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.584
0.768
0.897

For models with dependent variable RPCS-EXPS, the following variables are constants or have missing
correlations: Custom Charges ($/ea). They are deleted from the analysis.

Table 48: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 3: Custom
charges removed)
RPCS – RENS

R: .867
R Square .751
Adjusted
R Square.746

Standardized
coefficients Beta

Variables
(Constant)
Cycle time (days)
RPC / REN
Designed container life (year, RPC)
Container weight (lbs./container)
Return rate (percent/year, RPC)
Backhaul volume factor
Shipping distance (miles)
Container quantity per pallet (tote/pallet)
Peak volume factor
Average daily Volume (containers/day)

0.614
-0.608
0.185
-0.064
0.050
0.026
-0.020
0.012
-0.007
-0.001

t
-1.164
27.129
-26.669
8.126
-2.815
2.192
1.130
-0.867
0.536
-0.294
-0.044

Sig.
0.245
0.000
0.000
0.000
0.005
0.029
0.259
0.387
0.592
0.769
0.965

Table 49: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 3: Custom
charges removed)
EXPS - RENS

R: .929
R Square: .863
Adjusted
R Square: .860

Standardized coefficients
Beta

Variables
(Constant)
EXP / REN
Cycle time (days)
Average daily Volume (containers/day)
Shipping distance (miles)
Backhaul volume factor
Container quantity per pallet (tote/pallet)
Peak volume factor
Return rate (percent/year, RPC)
Container weight (lbs./container)
Designed container life (year, RPC)

115

-0.661
0.635
-0.153
0.082
0.048
0.013
-0.012
0.009
0.009
0.004

t
0.227
-39.254
37.838
-9.076
4.809
2.822
0.793
-0.730
0.546
0.510
0.256

Sig.
0.821
0.000
0.000
0.000
0.000
0.005
0.428
0.466
0.585
0.610
0.798

A negative relationship with the dependent variable, standardized coefficients,
suggests that the average daily volume increases, RPCS becomes more viable.
However, if the logistics provider is required to pay significant amount of the custom
charges or deposits when they ships back empty containers from the oversea,
establishing international RPCS or RENS should be very difficult to operate.
6.3.4 .

Scenario 4: Quantity of containers on a pallet halved

Scenario 4 addresses the issue of container quantity on a pallet. A pallet is
generally regarded as a basic unit of international transportation, so more containers on
a pallet means the container is smaller and more economical in distribution process.
Even if average container quantity on a pallet halved, container cost ratio is still
the most important contributor on packaging system cost. Like previous scenarios,
designed container life and cycle time and are also significantly related to all packaging
systems.

Table 50 through Table 52 show the impact of the quantity of containers on a

pallet.

Table 50: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 4:
Container Q on a pallet halved)
RPCS – EXPS

R: .887
R Square: .787
Adjusted
R Square: .782

Standardized coefficients
(Beta)

Variables
(Constant)
RPC / EXP
Designed container life (year, RPC)
Cycle time (days)
Custom Charges ($/ea.)
Shipping distance (miles)
Container weight (lbs./container)
Average daily Volume (containers/day)
Backhaul volume factor
Return rate (percent/year, RPC)
Peak volume factor
Container quantity per pallet (tote/pallet)

116

0.674
-0.302
0.252
0.273
0.163
0.088
-0.136
0.074
-0.011
-0.006
-0.004

t

Sig.

-3.823
31.914
-14.274
12.023
8.017
7.643
4.153
-3.995
3.511
-0.518
-0.271
-0.198

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.605
0.786
0.843

Table 51: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 4: Container Q on
a pallet halved)
RPCS – RENS

R: .860
R Square: .739
Adjusted
R Square: .733

Standardized
coefficients (Beta)

Variables
(Constant)
Cycle time (days)
RPC / REN
Designed container life (year, RPC)
Container weight (lbs./container)
Return rate (percent/year, RPC)
Container quantity per pallet (tote/pallet)
Backhaul volume factor
Shipping distance (miles)
Peak volume factor
Average daily Volume (containers/day)
Custom Charges ($/ea.)

0.613
-0.607
0.185
-0.063
0.049
0.048
0.027
-0.020
-0.006
-0.005
-0.004

t
-1.369
27.046
-26.573
8.105
-2.779
2.154
2.108
1.163
-0.855
-0.261
-0.141
-0.109

Sig.
0.172
0.000
0.000
0.000
0.006
0.032
0.036
0.246
0.393
0.794
0.888
0.913

Table 52: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 4: Container Q
on a pallet halved)
Standardized coefficients
(Beta)

EXPS - RENS

Variables

R: .933
R Square: .870
Adjusted
R Square: .867

(Constant)
EXP / REN
Cycle time (days)
Custom Charges ($/ea.)
Shipping distance (miles)
Backhaul volume factor
Container quantity per pallet (tote/pallet)
Average daily Volume (containers/day)
Peak volume factor
Return rate (percent/year, RPC)
Container weight (lbs./container)
Designed container life (year, RPC)

6.3.5 .

-0.656
0.628
0.153
0.083
0.048
0.042
-0.060
-0.011
0.009
0.008
0.003

t

Sig.

-1.017
-39.977
38.369
5.763
5.006
2.939
2.554
-2.254
-0.680
0.549
0.505
0.195

0.310
0.000
0.000
0.000
0.000
0.003
0.011
0.025
0.497
0.583
0.614
0.846

Scenario 5: Average daily volume doubled

Scenario 5 addresses the issue of average daily volume.

See Table 53 to

Table 55. When average daily volume doubled, cost ratio is consistently the most
important contributors on each packaging system.

Designed container life and cycle

time are also significantly important for RPCS. Shipping distance is one of contributors
for the cost of EXPS compared to RPCS and RENS while container weight and return
rate is more important when comparing RPCS and RENS.

117

Table 53: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 5: Average
daily Volume doubled)
RPCS – EXPS

R: .881
R Square: .777
Adjusted
R Square: .772

Standardized
coefficients Beta)

Variables
(Constant)
RPC / EXP
Designed container life (year, RPC)
Cycle time (days)
Custom Charges ($/ea.)
Shipping distance (miles)
Container weight (lbs./container)
Average daily Volume (containers/day)
Backhaul volume factor
Return rate (percent/year, RPC)
Container quantity per pallet (tote/pallet)
Peak volume factor

0.725
-0.320
0.274
0.165
0.099
0.094
-0.064
0.035
-0.011
-0.004
-0.003

t

Sig.

-3.992
33.549
-14.828
12.771
4.745
4.520
4.346
-1.831
1.623
-0.500
-0.204
-0.162

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.068
0.105
0.617
0.839
0.871

Table 54: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 5: Average daily
Volume doubled)
RPCS - RENS

R: .867
R Square: .751
Adjusted
R Square: .745

Standardized
coefficients (Beta)

Variables
(Constant)
Cycle time (days)
RPC / REN
Designed container life (year, RPC)
Container weight (lbs./container)
Return rate (percent/year, RPC)
Backhaul volume factor
Shipping distance (miles)
Container quantity per pallet (tote/pallet)
Peak volume factor
Average daily Volume (containers/day)
Custom Charges ($/ea.)

0.614
-0.608
0.185
-0.064
0.050
0.026
-0.020
0.012
-0.007
-0.004
-0.004

t
-1.110
27.097
-26.635
8.118
-2.811
2.189
1.128
-0.869
0.535
-0.294
-0.116
-0.113

Sig.
0.268
0.000
0.000
0.000
0.005
0.029
0.260
0.386
0.593
0.769
0.908
0.910

Table 55: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 5: Average
daily Volume doubled)
EXPS - RENS

R: .934
R Square: .873
Adjusted
R Square: .870

Standardized
coefficients (Beta)

Variables
(Constant)
EXP / REN
Cycle time (days)
Custom Charges ($/ea.)
Shipping distance (miles)
Backhaul volume factor
Container quantity per pallet (tote/pallet)
Average daily Volume (containers/day)
Peak volume factor
Return rate (percent/year, RPC)
Container weight (lbs./container)
Designed container life (year, RPC)

118

-0.673
0.644
0.094
0.047
0.027
0.013
-0.020
-0.011
0.010
0.008
0.006

t
-0.722
-41.462
39.797
3.567
2.887
1.658
0.794
-0.770
-0.662
0.635
0.474
0.360

Sig.
0.470
0.000
0.000
0.000
0.004
0.098
0.428
0.442
0.508
0.526
0.636
0.719

6.3.6 .

Scenario 6: Backhaul volume halved

Scenario 6 addresses the issue of backhaul volume factor.

If a reusable

container has a function to reduce its volume by any stacking methods such as
collapsible, foldable, nesting, etc., it is assumed that this could be one of the saving
factors for reusable and rental packaging systems since these systems require return
process (backhaul logistics).

See Table 56 through Table 58.

Table 56: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 6: Backhaul
volume halved)
RPCS – EXPS

R: .884
R Square: .781
Adjusted
R Square: .776

Standardized
coefficients (Beta)

Variables
(Constant)
RPC / EXP
Designed container life (year, RPC)
Cycle time (days)
Custom Charges ($/ea.)
Shipping distance (miles)
Container weight (lbs./container)
Average daily Volume (containers/day)
Backhaul volume factor
Return rate (percent/year, RPC)
Container quantity per pallet (tote/pallet)
Peak volume factor

0.715
-0.316
0.270
0.211
0.107
0.093
-0.073
0.034
-0.011
-0.004
-0.004

t

Sig.

-3.914
33.393
-14.777
12.704
6.125
4.935
4.319
-2.105
1.578
-0.508
-0.195
-0.189

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.036
0.115
0.612
0.846
0.850

Table 57: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 6: Backhaul
volume halved)
RPCS – RENS

R: .867
R Square .751
Adjusted
R Square: .745

Standardized coefficients
(Beta)

Variables
(Constant)
Cycle time (days)
RPC / REN
Designed container life (year, RPC)
Container weight (lbs./container)
Return rate (percent/year, RPC)
Backhaul volume factor
Shipping distance (miles)
Container quantity per pallet (tote/pallet)
Peak volume factor
Average daily Volume (containers/day)
Custom Charges ($/ea.)

119

0.614
-0.608
0.185
-0.064
0.050
0.028
-0.020
0.012
-0.007
-0.004
-0.004

t
-1.138
27.106
-26.636
8.118
-2.812
2.198
1.212
-0.869
0.533
-0.290
-0.109
-0.104

Sig.
0.255
0.000
0.000
0.000
0.005
0.028
0.226
0.385
0.594
0.772
0.913
0.917

Table 58: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 6: Backhaul
volume halved)
EXPS – RENS

R: .935
R Square: .873
Adjusted
R Square: .870

Standardized
coefficients (Beta)

Variables
(Constant)
EXP / REN
Cycle time (days)
Custom Charges ($/ea.)
Shipping distance (miles)
Backhaul volume factor
Average daily Volume (containers/day)
Container quantity per pallet (tote/pallet)
Peak volume factor
Return rate (percent/year, RPC)
Container weight (lbs./container)
Designed container life (year, RPC)

-0.669
0.641
0.117
0.052
0.028
-0.025
0.013
-0.011
0.010
0.008
0.006

t
-0.713
-41.307
39.677
4.478
3.159
1.719
-0.938
0.794
-0.672
0.635
0.467
0.341

Sig.
0.476
0.000
0.000
0.000
0.002
0.086
0.349
0.427
0.502
0.526
0.641
0.733

When backhaul volume factor is halved, cost ratio and cycle time were still
important factors, and custom charges and shipping distance are important for RPCSEXPS and EXPS-RENS comparison.

In RPCS-EXPS comparison, although reducing

backhaul volume factor has little influence on the regression results, it contributes on
average daily volume of containers.
6.3.7 .

Scenario 7: Designed container life doubled

It is expected that designed container life can impact greatly on container cost
for a reusable packaging system. When the container life is doubled, cost ratio,
designed container life, custom charges, shipping distance, cycle time, average daily
volume, container weight and backhaul volume factor contribute significantly on RPCS
and EXPS costs.
Again, increased container life does not affect RENS-EXPS cost comparison
since RENS is based on the container rental cost in this scenario.
through Table 61.

120

See Table 59

Table 59: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 7:
Container life doubled)
RPCS – EXPS

R: .846
R Square: .716
Adjusted
R Square: .710

Variables
Standardized coefficients Beta)
(Constant)
RPC / EXP
0.667
Designed container life (year, RPC)
-0.191
Custom Charges ($/ea.)
0.305
Shipping distance (miles)
0.182
Cycle time (days)
0.139
Container weight (lbs./container)
0.110
Average daily Volume (containers/day)
-0.156
Backhaul volume factor
0.082
Peak volume factor
-0.009
Container quantity per pallet (tote/pallet)
-0.005
Return rate (percent/year, RPC)
-0.005

t
-4.239
27.411
-7.850
7.780
7.387
5.729
4.518
-3.954
3.383
-0.383
-0.211
-0.189

Sig.
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.702
0.833
0.851

Table 60: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 7: Container life
doubled)
RPCS – RENS

R: .860
R Square .740
Adjusted
R Square.734

Standardized
coefficients (Beta)

Variables
(Constant)
Cycle time (days)
RPC / REN
Designed container life (year, RPC)
Container weight (lbs./container)
Return rate (percent/year, RPC)
Backhaul volume factor
Shipping distance (miles)
Container quantity per pallet (tote/pallet)
Peak volume factor
Custom Charges ($/ea.)
Average daily Volume (containers/day)

t

0.676
-0.545
0.099
-0.072
0.045
0.025
-0.020
0.015
-0.005
-0.006
-0.003

Sig.

-0.744
29.161
-23.364
4.237
-3.075
1.928
1.055
-0.860
0.652
-0.224
-0.148
-0.076

0.457
0.000
0.000
0.000
0.002
0.054
0.292
0.390
0.514
0.823
0.882
0.939

Table 61: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 7: Container
life doubled)
EXPS - RENS

R: .933
R Square: .870
Adjusted
R Square: .867

Standardized
coefficients
(Beta)

Variables
(Constant)
EXP / REN
Cycle time (days)
Custom Charges ($/ea.)
Shipping distance (miles)
Backhaul volume factor
Average daily Volume (containers/day)
Container quantity per pallet (tote/pallet)
Peak volume factor
Return rate (percent/year, RPC)
Container weight (lbs./container)
Designed container life (year, RPC)

121

-0.657
0.629
0.153
0.083
0.048
-0.059
0.013
-0.012
0.010
0.008
0.003

t
-0.733
-40.029
38.447
5.781
5.005
2.905
-2.221
0.802
-0.717
0.586
0.475
0.193

Sig.
0.464
0.000
0.000
0.000
0.000
0.004
0.027
0.423
0.473
0.558
0.635
0.847

Note cycle time is less significant for RPCS-EXPS comparison in this case.
While positive value of cycle time shows that longer cycle time is still one of the
disadvantages for RPCS, but longer container life can reduce the negative impact of
using reusable containers.
6.3.8 .

Scenario 8: Reusable and rental costs doubled

Table 62 through Table 65 shows that the impact of reusable and rental
packaging costs compared to expendable packaging costs. When reusable container
cost and rental rate are doubled compared to EXPS, cost ratio and cycle time are still
the most important cost contributors for any scenario.

Note return rate becomes more

important than other scenarios because of the high cost of reusable packaging
containers.

It is no surprise that more effective container management is necessary

since the container owner has to invest more money on purchasing or leasing the
containers.

Table 62: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 8: Reusable
cost doubled)
RPCS – EXPS

R: .917
R Square: .841
Adjusted
R Square: .838

Standardized
coefficients (Beta)

Variables
(Constant)
RPC / EXP
Designed container life (year, RPC)
Cycle time (days)
Custom Charges ($/ea)
Shipping distance (miles)
Average daily Volume (containers/day)
Container weight (lbs/container)
Backhaul volume factor
Return rate (percent/year, RPC)
Peak volume factor
Container quantity per pallet (tote/pallet)

122

0.621
-0.433
0.392
0.203
0.122
-0.097
0.058
0.056
-0.020
0.000
0.000

t

Sig.

-2.284
34.097
-23.759
21.681
6.925
6.645
-3.297
3.180
3.054
-1.089
-0.015
0.001

0.023
0.000
0.000
0.000
0.000
0.000
0.001
0.002
0.002
0.277
0.988
0.999

Table 63: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 8: Reusable cost
doubled)
RPCS – RENS

R: .878
R Square .771
Adjusted
R Square.766

Standardized
coefficients Beta)

Variables
(Constant)
RPC / REN
Cycle time (days)
Designed container life (year, RPC)
Return rate (percent/year, RPC)
Container weight (lbs/container)
Backhaul volume factor
Shipping distance (miles)
Peak volume factor
Container quantity per pallet (tote/pallet)
Average daily Volume (containers/day)
Custom Charges ($/ea)

-0.697
0.447
0.353
0.058
-0.052
0.027
-0.018
-0.009
0.005
-0.006
0.000

t
-1.861
-31.802
20.540
16.129
2.635
-2.385
1.219
-0.828
-0.423
0.252
-0.168
-0.006

Sig.
0.063
0.000
0.000
0.000
0.009
0.017
0.224
0.408
0.673
0.801
0.867
0.995

Table 64: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 8: Rental cost
doubled)
RPCS - RENS

R: .860
R Square .739
Adjusted
R Square: .733

Standardized
coefficients (Beta)

Variables
(Constant)
Cycle time (days)
RPC / REN
Designed container life (year, RPC)
Container weight (lbs/container)
Return rate (percent/year, RPC)
Backhaul volume factor
Shipping distance (miles)
Peak volume factor
Custom Charges ($/ea)
Container quantity per pallet (tote/pallet)
Average daily Volume (containers/day)

0.678
-0.547
0.099
-0.046
0.045
0.024
-0.020
-0.006
-0.005
-0.002
-0.002

t
-0.602
29.178
-23.384
4.239
-1.972
1.942
1.039
-0.865
-0.238
-0.145
-0.101
-0.061

Sig.
0.547
0.000
0.000
0.000
0.049
0.053
0.299
0.388
0.812
0.885
0.920
0.952

Table 65: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 8: Rental cost
doubled)
EXPS - RENS

R: .902
R Square: .814
Adjusted
R Square: .810

Standardized
coefficients Beta)

Variables
(Constant)
Cycle time (days)
EXP / REN
Shipping distance (miles)
Custom Charges ($/ea)
Backhaul volume factor
Return rate (percent/year, RPC)
Average daily Volume (containers/day)
Designed container life (year, RPC)
Peak volume factor
Container weight (lbs/container)
Container quantity per pallet (tote/pallet)

123

0.686
-0.579
0.060
0.094
0.032
0.026
-0.025
0.015
-0.009
-0.003
-0.001

t
-0.685
35.025
-29.458
3.018
2.960
1.612
1.303
-0.786
0.752
-0.472
-0.164
-0.026

Sig.
0.494
0.000
0.000
0.003
0.003
0.108
0.193
0.432
0.452
0.637
0.870
0.979

6.3.9 .

Scenario 9: Return rate 20 percent dropped

Table 66 through Table 68 show that if the return rate of reusable and rental
containers are dropped by 20 percent, cost ratio and cycle time are still the most
important cost contributors for any scenario. Designed container life of a reusable
container is a key cost contributor for reusable packaging system, so it shows when
RPCS is compared with EXPS.
Table 66: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 9: Return
rate 20 percent dropped)
RPCS – EXPS

R: .896
R Square: .802
Adjusted
R Square: .798

Standardized
coefficients Beta)

Variables
(Constant)
RPC / EXP
Designed container life (year, RPC)
Cycle time (days)
Custom Charges ($/ea)
Shipping distance (miles)
Average daily Volume (containers/day)
Container weight (lbs/container)
Backhaul volume factor
Return rate (percent/year, RPC)
Peak volume factor
Container quantity per pallet (tote/pallet)

0.670
-0.329
0.281
0.261
0.157
-0.130
0.080
0.071
-0.007
-0.005
-0.003

t
-3.893
32.927
-16.160
13.875
7.970
7.633
-3.962
3.913
3.512
-0.329
-0.250
-0.128

Sig.
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.743
0.803
0.898

Table 67: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 9: Return rate 20
percent dropped)
RPCS – RENS

R: .869
R Square .755
Adjusted
R Square: .749

Standardized
coefficients Beta)

Variables
(Constant)
RPC / REN
Cycle time (days)
Designed container life (year, RPC)
Container weight (lbs/container)
Return rate (percent/year, RPC)
Backhaul volume factor
Shipping distance (miles)
Container quantity per pallet (tote/pallet)
Peak volume factor
Average daily Volume (containers/day)
Custom Charges ($/ea)

124

-0.626
0.593
0.211
-0.061
0.046
0.026
-0.020
0.011
-0.007
-0.003
-0.002

t
-1.030
-27.573
26.340
9.344
-2.681
2.038
1.129
-0.885
0.487
-0.300
-0.094
-0.066

Sig.
0.304
0.000
0.000
0.000
0.008
0.042
0.259
0.376
0.626
0.764
0.925
0.947

Table 68: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 9: Return rate
20 percent dropped)
EXPS - RENS

R: .933
R Square: .870
Adjusted
R Square: .867

Standardized coefficients
Beta)

Variables
(Constant)
EXP / REN
Cycle time (days)
Custom Charges ($/ea)
Shipping distance (miles)
Backhaul volume factor
Average daily Volume
(containers/day)
Container quantity per pallet
(tote/pallet)
Peak volume factor
Return rate (percent/year, RPC)
Container weight (lbs/container)
Designed container life (year, RPC)

t

Sig.

-0.657
0.630
0.154
0.083
0.048
-0.059

-0.831
-40.031
38.445
5.787
4.996
2.915
-2.214

0.406
0.000
0.000
0.000
0.000
0.004
0.027

0.013

0.797

0.426

-0.012
0.011
0.008
0.003

-0.712
0.693
0.476
0.189

0.477
0.489
0.634
0.850

For the case of RENS, return rate does not affect the cost much in this scenario since
a pool company should have responsibility for returning containers. The cost of RENS
may vary depending on whether the cost of returning containers is included in the
contract between a pool user and a pool company.
6.3.10.

Scenario 10: Cycle time halved
Table 69 through Table 71 show that the impact of the cycle time changes.

Cost ratio, designed container life and cycle time are still the most important cost
contributors for any scenario.

It should be noted that custom charges are statistically

more important factors than designed container life and cycle time when RPCS is
compared EXPS.

Because of increased frequency of delivery due to shorter cycle time

and shipping distance, reducing custom charges should be very important for RPCS.
Compare to basic analysis, changing cycle time does not impact much on RPCS-RENS
and RENS-EXPS.

125

Table 69: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 10: Cycle
time halved)
RPCS – EXPS

R: .846
R Square: .716
Adjusted
R Square: .710

Standardized
coefficients Beta)

Variables
(Constant)
RPC / EXP
Custom Charges ($/ea)
Designed container life (year, RPC)
Shipping distance (miles)
Cycle time (days)
Container weight (lbs/container)
Average daily Volume (containers/day)
Backhaul volume factor
Peak volume factor
Container quantity per pallet (tote/pallet)
Return rate (percent/year, RPC)

0.667
0.305
-0.191
0.182
0.139
0.110
-0.156
0.082
-0.009
-0.005
-0.005

t

Sig.

-4.240
27.411
7.780
-7.850
7.388
5.729
4.519
-3.953
3.382
-0.383
-0.211
-0.188

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.702
0.833
0.851

Table 70: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 10: Cycle time
halved)
RPCS – RENS

R: .869
R Square .754
Adjusted
R Square: .749

Standardized
coefficients Beta)

Variables
(Constant)
Cycle time (days)
RPC / REN
Designed container life (year, RPC)
Container weight (lbs/container)
Return rate (percent/year, RPC)
Container quantity per pallet (tote/pallet)
Backhaul volume factor
Shipping distance (miles)
Peak volume factor
Average daily Volume (containers/day)
Custom Charges ($/ea)

0.609
-0.603
0.184
-0.116
0.049
0.048
0.026
-0.020
-0.006
-0.005
-0.004

t

Sig.

-1.406
27.044
-26.573
8.104
-5.115
2.154
2.109
1.163
-0.857
-0.263
-0.141
-0.109

0.160
0.000
0.000
0.000
0.000
0.032
0.035
0.245
0.392
0.793
0.888
0.914

Table 71: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 10: Cycle time
halved)
EXPS – RENS

R: .935
R Square: .874
Adjusted
R Square: .872

Standardized
coefficients Beta)

Variables
(Constant)
EXP / REN
Cycle time (days)
Custom Charges ($/ea)
Shipping distance (miles)
Backhaul volume factor
Average daily Volume (containers/day)
Container quantity per pallet (tote/pallet)
Container weight (lbs/container)
Peak volume factor
Return rate (percent/year, RPC)
Designed container life (year, RPC)

126

-0.716
0.497
0.226
0.109
0.067
-0.103
0.032
0.023
-0.014
-0.014
-0.014

t

Sig.

-0.558
-44.359
30.919
8.657
6.701
4.131
-3.933
1.959
1.418
-0.889
-0.868
-0.849

0.577
0.000
0.000
0.000
0.000
0.000
0.000
0.051
0.157
0.374
0.386
0.396

6.3.11.

Scenario 11: Combination of Distance Doubled, Return rate 20

percent dropped and Cycle time doubled
Scenario 11 is a combination of worst cases for RTPS and RENS to see how
the combination of important factors such as distance, return rate and cycle time impact
on the systems.

Cost ratio and cycle time are still the most important cost contributors

for any scenario (Table 72 through Table 74).
Table 72: Variations in the regression analysis as compared to the Base Analysis
between reusable and expendable packaging system costs (Scenario 13:
Combination of Distance Doubled, Return rate 20 percent dropped and Cycle time
doubled)
RPCS – EXPS

R: .918
R Square: .842
Adjusted
R Square: .839

Standardized
coefficients
Beta)

Variables
(Constant)
RPC / EXP
Designed container life (year, RPC)
Cycle time (days)
Shipping distance (miles)
Custom Charges ($/ea)
Average daily Volume (containers/day)
Backhaul volume factor
Container weight (lbs/container)
Return rate (percent/year, RPC)
Container quantity per pallet (tote/pallet)
Peak volume factor

0.574
-0.428
0.391
0.201
0.238
-0.139
0.077
0.035
-0.009
0.002
-0.001

t
-2.502
31.602
-23.568
21.650
10.952
8.118
-4.731
4.270
1.902
-0.520
0.108
-0.051

Sig.
0.013
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.058
0.603
0.914
0.960

Table 73: Variations in the regression analysis as compared to the Base Analysis
between reusable and rental packaging system costs (Scenario 13: Combination
of Distance Doubled, Return rate 20 percent dropped and Cycle time doubled)
RPCS – RENS

R: .868
R Square .754
Adjusted
R Square: .748

Standardized coefficients
Beta)

Variables
(Constant)
RPC / REN
Cycle time (days)
Designed container life (year, RPC)
Return rate (percent/year, RPC)
Container weight (lbs/container)
Backhaul volume factor
Shipping distance (miles)
Peak volume factor
Container quantity per pallet (tote/pallet)
Average daily Volume (containers/day)
Custom Charges ($/ea)

127

-0.627
0.595
0.212
0.047
-0.034
0.025
-0.020
-0.007
-0.007
-0.003
-0.002

t
-0.886
-27.590
26.352
9.348
2.059
-1.511
1.112
-0.891
-0.316
-0.310
-0.076
-0.062

Sig.
0.376
0.000
0.000
0.000
0.040
0.131
0.267
0.374
0.752
0.757
0.940
0.951

Table 74: Variations in the regression analysis as compared to the Base Analysis
between expendable and rental packaging system costs (Scenario 13:
Combination of Distance Doubled, Return rate 20 percent dropped and Cycle time
doubled)
EXPS - RENS

R: .903
R Square: .815
Adjusted
R Square: .811

Standardized
coefficients Beta)

Variables
(Constant)
Cycle time (days)
EXP / REN
Shipping distance (miles)
Custom Charges ($/ea)
Backhaul volume factor
Return rate (percent/year, RPC)
Average daily Volume (containers/day)
Designed container life (year, RPC)
Peak volume factor
Container weight (lbs/container)
Container quantity per pallet (tote/pallet)

0.679
-0.571
0.098
0.119
0.044
0.030
-0.047
0.014
-0.009
-0.004
0.000

t

Sig.

-1.006
34.756
-29.149
4.956
3.764
2.220
1.534
-1.491
0.691
-0.485
-0.188
-0.005

0.315
0.000
0.000
0.000
0.000
0.027
0.126
0.137
0.490
0.628
0.851
0.996

Note that container weight becomes less significant factor for RPCS-EXPS
comparison, but other factors remain constant in terms of weight of importance.
6.4.

Summary of comparative regression analysis results
By breaking down each analysis, major findings from eleven scenarios are

summarized as below.
a. Container cost ratio and cycle time are the most significant and consistent
cost drivers for all scenarios.

In other words, container cost and cycle time

are major cost contributors in this model. This is a similar result found by
(Mollenkopf, Closs, et al. (2005, 191-192). Shipping distance and
designed container life are also consistently more important factors than
other variables such as peak volume factor, return rate, backhaul volume
factor.
b. Secondary to container cost ratio, cycle time is the most significant cost
factor to choose a packaging system.
128

Time is more important than

physical distance since it requires an increased number of containers.
Therefore, EXPS is more economical option for prolonged supply chains.
c. Designed container life is constantly the third most important factor after
cost ratio when reusable and expendable packaging system costs are
compared.

Although expensive containers do not always guarantee the

better quality and durability of the reusable container, longer container life
usually increases unit purchasing cost.

Setting an optimum cost vs.

container durability for RPC should be critical to save total packaging and
logistics costs.
d. Custom charges for forward transportation should not be a factor for this
research because the custom charges is based on a product, not a package.
However, custom charges can be a decisive factor if a company wants to
implement a reusable packaging system internationally since this is one of
the most important cost drivers for RPCS and RENS.

Proper

documentation and mutual agreement by both parties and governments are
necessary to avoid any unnecessary customs duties and the like and delays
due to customs formalities.
Although this regression analysis is meaningful since this can help to identify
important cost factors and impacts on other variables, the problem of this analysis is
that this result does not represent a real world scenario. This calculation limits itself
showing simple relationships with each other variables. It is hard to include the activity
and time factors that the packaging system consumed. An international supply chain
environment is far more complicated and dynamic, so this type of analysis can only

129

useful to analysis domestic and simple logistical distribution environment. Mollenkopf
(2005) also noted that the dynamic simulation method would be suitable for more
complicated and real world solution.
The next chapter attempts to solve this problem with a dynamic simulation
model.

Based on each activity cost, the total cost of an activity, process, and system

cost are calculated. Depending on various packaging system types, the results of
activity cost simulation model show that the total costs can be changed by various
supply chain activities and can show more realistic cost model for an international
supply chain.

130

CHAPTER 7
DYNAMIC SIMULATION MODELING
A dynamic simulation model can help measure quantitative performance of a
supply chain; cost minimization is the most widely used objective. Cost can be
analyzed for entire supply chain or for the particular manufacturing or distribution
processes (Beamon, 1998).
This study is trying to cover the complete supply chain from the inbound logistics
activities at the distribution warehouse to the oversea manufacturing center of H
automotive company.

Arena Rockwell software by Rockwell Inc. is used for the

development of supply chain model.
The findings from the simulation show that the model calculates activity-based
costs of each different packaging type in a predictable manner. The results are
different depending on different cycle time and material costs.
This model can be utilized to study the total packaging system cost of an
existing supply chain and to find the opportunities to improve overall financial
performance.
7.1 Simulation model
Figure 10 shows the structure of the simulation model created by using Arena
software.

131

Figure 10: Simulation model for an international automotive part packaging system

132

The model represents the international packaging supply chain process of
automotive part manufacturing from receiving packages (containers).

The model was

divided into ten main sub-modules. The operation of each sub-model is described next.
The starting point of this simulation is assigning entity types for three different
packaging systems, i.e., expendable, reusable, and rental shipping containers. Three
entity types are created and assigned their types and variables.

Following sub-

models contain each activity (for example, loading/unloading, moving,
palletizing/unpalletizing, sorting, etc.) at each destination.

Upon arrival, packages are

processed at a 3PL distribution center (Figure 11: the first sub model) in South Korea.
The second sub model (Figure 12) is a set of activities in KD center where automotive
parts are packaged and shipped to the port.

Figure 11: The first sub model: processes at a 3PL distribution center

133

Figure 12: The second sub model: processes at a Knock Down center

After port processing (Figure 13: third sub model), packaged automotive parts
are shipped to the US port (Figure 14: fourth sub model) at a certain route time (days).
All parts go to 3PL consolidation center and are prepared to feed the assembly line of
the car manufacturer (Figure 15: fifth sub model).

After being emptied and

disassembled at the manufacturing process (Figure 16: sixth sub model), all containers
are shipped back to the 3PL consolidation center.

Figure 13: The third sub model: processes at an international port

134

Figure 14: The fourth sub model: processes at a port in the US

Figure 15: The fifth sub model: processes at consolidation center in the US

Figure 16: The sixth sub model: line feeding and disassembling at the
manufacturing plant

135

At the 3PL consolidation center, the containers are separated out into different
process by “N-way by chance” distribution.

If the containers are reusable or rented,

they follow a TRIA distribution (80, 90, 95) to be reused.

For containers that are not

suitable to be reused by damages, loss, etc., they are sorted by “2-way by chance”
distribution; 90 percent for recycle and 10 percent disposal.

Reused containers are

also separated by “2-way by chance” distribution if they feature different type; straight
wall and collapsible types (Figure 17: seventh sub model).

Figure 17: The seventh sub model: returning process at 3PL consolidation center
If the containers are expendable, 90 percent are recycled and 10 percent are
disposed.

Recycled and disposed containers are transferred to the recycling plant.

Only reusable containers are shipped back to the distribution center (Figure 20: tenth
sub model) via port and shipping process (Figure 18 and 19: eighth and ninth sub
model).

136

Figure 18: The eighth sub model: Shipping process of reusable containers (at the
US)

Figure 19: The ninth sub model: Shipping process of reusable containers (at the
oversea port)

Figure 20: The tenth sub model: reconditioning and repairing of reusable
containers

137

All data are based on the activity cost model which was developed in Chapter 5.
For simplification, although the entity is a package (or container), it is measured as a
pallet load of containers.

For example, if 150 pallet loads of containers arrive every

day and each pallet contains 30 containers, this means 4,500 containers are received
every day.
The Simulation model collects statistics in each area on entity, transfer time and
transfer cost, process and resource utilization.

More importantly, this model calculates

the number of total containers required and the number of reused containers that are
returned to the 3PL distribution center.
To reduce unnecessary variation of uncertainty of simulation, process time and
resources for each activity are predetermined from actual measurement and data from
ABC analysis (for example, loading/unloading, moving, palletizing/unpalletizing, and
sorting, etc.).

However, to demonstrate the random nature of simulation, transfer time

follows a triangular (TRIA) distribution.

Each independent simulation runs is set to 5

because the results are not significantly different after 5 replications, as shown in Table
75.

Law and McComas (1991) recommended making at least 3 to 5 independent runs

for each case.

As shown in the Table 75, comparing 1, 5 and 100 runs shows 5 runs is

enough since data from 5 runs to 100 runs are almost identical and statistically
insignificant.
Based on the observation, the following parameters were specified:
(i) Length of each simulation run= 260 days (per year).
(ii) Number of independent simulation runs = 5

138

Table 75: Data comparison based on the number of replications
EXP

1 run
(A)

A/B,
percent

5 runs average
(B)

100 runs
average (C)

A/C,
percent

No of Required
New Pkg

39,260

39,260.00

100.0percent

39,260.00

0.0percent

No of Total Pkg

39,260

39,260.00

100.0percent

39,260.00

0.0percent

Disposed

3,543

3501

101.2percent

3522

0.0percent

Recycled

31,065

31193

99.6percent

31120

0.0percent

REN
No of Required
New Pkg
No of Total Pkg

1 run
(A)

A/B,
percent

5 runs average
(B)

100 runs
average (C)

A/C,
percent

16,681

16,515.00

101.0percent

16553

0.0percent

44,491

44,539.00

99.9percent

44554

0.0percent

4,588

4546

100.9percent

4554

0.0percent

No of Returns

712

715.6

99.5percent

715

0.1percent

Disposed

179

150.8

118.7percent

148

0.8percent

Recycled

1,327

1333.8

99.5percent

1337

0.1percent

Loss

RPC
No of Required
New Pkg
No of Total Pkg
Loss
No of Returns
Disposed
Recycled

7.2.

1 run
(A)

A/B,
percent

5 runs average
(B)

100 runs
average (C)

A/C,
percent

16,490

16550

99.6percent

16563

0.0percent

44,350
4,548
709
159.0000
1295.00

44,466.00
4515.4
714
150
1302.6

99.7percent
100.7percent
99.3percent
106.0percent
99.4percent

44602
4555
716
147
1329

0.0percent
0.0percent
0.1percent
0.7percent
0.1percent

Results of simulations
No computational model will ever be fully verified, guaranteeing 100 percent

error-free implementation.

Verification is concerned with building the model correctly.

It is utilized in the comparison of the conceptual model to the computer representation
that implements that conception. Verification is done to ensure that:
– The model is programmed correctly
– The algorithms have been implemented properly
– The model does not contain errors, oversights, or bugs
As Kelton et al (2010, p. 555) suggested, the best way to verify the simulation
139

model is comparing the results from my model to the results from the real packaging
system.

In this study; comparisons between the simulation and exiting results are

made. The associated parameters are set constant for calculating the packaging system
costs by using deterministic data. This includes fixed cost data and activity cost data.
7.2.1. Simulation 1: Impact of expected life of reusable containers
Although unit cost of EXPS (Expendable Packaging System) is cheapest, we
need to take account that the reusable packaging system (either RPCS or RENS) has
value over the years of operation. One question is what would be a break-even point
of the reusable container system if this can be operated for extended period of time, e.g.
10 years.
Assuming the loss and damaged reusable containers are replenished during the
operation, the expected useful life of the reusable containers are up to the end of days
of operations, the working days are 260 days per year, and the results of the simulation
are shown in Table 76.
Table 76: Impact of expected useful life of reusable containers

1yr

2yr

Required
new pkg

39,260

78,520

Total pkg

39,260

Disposed
Recycled
Loss

EXP
3yr

REN
3yr

10yr

1yr

2yr

117,780

392,600

16,515

24,781

78,520

117,780

392,600

44,539

3,543

7,542

11,508

38,961

31,065

66,282

101,580

349,071

RPC
3yr

10yr

1yr

2yr

10yr

32,351

86,321

16550

24,840

32,390

87,360

93,241

141,361

478,241

44,466

92,080

141,390

483,410

151

425

631

2,154

150

386

602

2,058

1333.8

3,307

5,166

18,444

1303

3,186

5,113

18,722

4546

10,327

15,990

55,106

4515

10,166

15,858

55,697

※ Note: Loss rate: 5 percent for every cycle

From Table 76, For example, REN requires only 16,550 containers per year
although total accumulated containers are 44,466 containers to run the first year of
operation. This means that the containers are reused for 2.67 times (44,466 / 16,550=
140

2.67) per year.
For one year of the operation, the number of required containers for
expendables is 39,260, but 16,515 for rental and 16,550 for reusable system. The
number difference is even larger over the time.

For 10 years of operations, EXPS

requires 392,600 containers, but RENS requires 86,321 and RPCS requires 87,360
containers.
For this particular system, the break-even point of reusable packaging system is
about 2.2 years compared to the expendable system (see Figure 21).

Interestingly,

rental system is almost equal or cheaper than reusable packaging system, but the cost
rate increases much faster over the time and passes after 6.6 years of operations.
This means if a company expects the useful life of a reusable container is less than
about 6.6 years, it would be beneficial to use rental system, but if the expected useful
life is longer than 6.6 years, they should consider having their own reusable packaging
system.
The biggest difference between EXPS, RENS and RPCS is the number of
containers used.

As a result of the larger number of total containers used, total cost of

EXP system is $98.9 million US dollars after 10 years’ operation (Table 77).

141

120.00
y = 9.8854x + 3E-05
R² = 1
y = 8.5385x - 0.8902

100.00

R² = 0.9965

US Mil. $

80.00

Expendable

60.00
y = 6.7153x + 7.5775
R² = 1

40.00

Rental
Reusable

20.00
0

2

4

6
Years

8

10

12

Figure 21: Total cost comparison of different packaging systems depending on
the different expected useful life of the container
Table 77: Unit and total cost comparison of three types after simulating 1st to
10th years of operations
Expendable

Reusable

Rental

Days

1st
year

2nd
year

3rd
year

10th
year

1st
year

2nd
year

3rd
year

10th
year

1st
year

2nd
year

3rd
year

10th
year

Total Pallet used

39,260

78,520

117,780

392,600

32,980

49,680

64,780

174,720

33,362

49,562

64,702

172,642

Containers on pallet

30

30

30

30

30

30

30

30

30

30

30

30

CC

1.44

1.44

1.44

1.44

4.79

4.79

4.79

4.79

0.72

1.44

2.16

7.2

AC

0.27

0.297

0.297

0.297

0.30

0.374

0.374

0.374

0.27

0.348

0.348

0.348

FTC

4.59

4.593

4.593

4.593

4.59

4.593

4.593

4.593

4.59

4.593

4.593

4.593

BTC

-

2.02

2.019

2.019

2.019

2.02

2.019

2.019

2.019

WC

2.1

2.073

2.073

2.073

2.55

2.478

2.478

2.478

2.35

2.274

2.274

2.274

AUC

0.44

0.438

0.439

0.440

0.02

0.018

0.018

0.019

0.02

0.018

0.019

0.019

UC

8.84

8.393

8.393

8.393

14.27

14.253

14.253

14.253

9.97

10.673

11.393

16.433

TC (US Mil $)

9.9

19.8

29.7

98.9

14.1

21.2

27.7

74.7

10.0

15.9

22.1

85.1

※ Note: CC: Container cost, AC: Administration cost, FTC: Forward transportation cost, BTC: Backhaul
transportation cost, WC: Warehousing cost, AUC: After use cost, UC: Unit cost, TC: Total cost

142

For RPCS and RENS, total costs of 10 years’ operation are $74.7 and $85.1
million US dollars, respectively.

Due to the high initial investment cost for purchasing

containers, the RPC system requires longer period to reach the break-even point than
RENS, but it gains more financial benefits for long term operation.
7.2.2. Simulation 2: Impact of distance (port to port comparison) – for 2 years of
operations
The Impact of distance between two ports is examined. There is great
relationship between distance and route time (shipping from port A to port B), so route
time is pre-determined depending on the distance difference.

Relationships between

distance and route time are compared in Table 78.
Table 78: Distance vs. route time (port to port)
Distance (miles)
8,000
500
5,000
10,000

Min.
25
1
10
30

The most notable data is route time.

Route Time (day, port to port)
Avg.
30
1.5
15
35

Max.
35
2
20
40

From Table 79, for shipping distance of 500

miles, the route time of RPCS varies ranging from 3.19 days to 184.84 days.

Even

average route time is 25.59 days compared to 2.34 days for EXPS. If the shipping
distance extends up to 10,000 miles, the average route time is about 194 days for both
RPCS and RENS, but the maximum route time can be reached up to 513.46 days for
RPC and 512.79 days for RENS. This could happen in many reasons: a company
could have very poor container management system, a product has very slow inventory
turnover, a company uses container for their warehouse inventory purpose, etc.

143

Consequently, more containers are required due to these non-productive activities
although shipping distance remains same.
Table 79: Impact of shipping distance changes on route time and the number of
packages required
EXP

RPC

REN

miles

500

5000

10000

500

5000

10000

500

5000

10000

Avg. Time

2.34

15.67

35.88

25.59

131.57

194.53

25.15

132.38

194.01

Min. Time

1.55

10.99

30.86

3.19

23.17

63.17

3.31

22.40

62.05

Max. Time

3.68

21.25

41.20

184.84

515.37

513.46

185.57

509.71

512.79

Required
New Pkg

78,520

78,520

78,520

14,981

20,201

25,731

14,640

20,270

26,060

Total Pkg

78,520

78,520

78,520

89,701

95,761

91,101

86,820

94,410

92,870

10,355

10,709

9,892

10,173

10,717

10,046

Loss
Disposed

7,638

7,605

7,231

377

404

322

400

359

333

Recycled

70,602

68,523

65,873

3,515

3,607

3,081

3,450

3,492

3,166

The Table 80 compares the unit costs and total costs of each case and shows the
impact of shipping distance on three packaging types.

Unit cost is the combined cost

to deliver a product including a container cost and other costs associated to logistical
activities such as administration, transportation, warehousing, etc.
Table 80: Unit cost comparison of three when shipping distance changed
EXP
Miles

500

CC
AC
AUC
BTC
FTC
WC
UC
TC
(US Mil
$)

5000

RPC
10000

500

REN

5000

10000

500

5000

10000

1.44

1.44

1.44

4.79

4.79

4.79

1.44

1.44

1.44

0.30
0.44
0.00
2.72
2.07
6.959

0.30
0.44
0.00
3.84
2.07
8.083

0.30
0.44
0.00
5.09
2.07
9.333

0.35
0.02
0.71
2.72
2.48
11.060

0.35
0.02
1.49
3.84
2.48
12.972

0.35
0.02
2.37
5.09
2.48
15.096

0.37
0.02
0.71
2.72
2.27
7.531

0.37
0.02
1.49
3.84
2.27
9.443

0.37
0.02
2.37
5.09
2.27
11.567

16.4

19.0

22.0

9.9

15.7

23.3

6.6

11.5

18.1

※ Note: CC: Container cost, AC: Administration cost, FTC: Forward transportation cost, BTC: Backhaul
transportation cost, WC: Warehousing cost, AUC: After use cost, UC: Unit cost, TC: Total cost

According to the simulation result, RPC becomes the most expensive option when
the route distance is extended to 10,000 miles.
144

REN also becomes more expensive

and less attractive option with longer travel time.

From the linear regression expressed

on Figure 22, total cost of RPC equals with total cost of EXP when the shipping distance
is 7,824 miles. When the distance is up to 14,682 miles, total cost of REN equals to
that of EXP.
25.00
20.00
Total Cost
US MIL $

y = 0.0014x + 9.0502
R² = 0.9929

y = 0.0005x + 16.092
R² = 0.9554

15.00
y = 0.0012x + 5.8147
R² = 0.996

Expendable
Rental

10.00

Reusable
5.00
0

2000 4000 6000 8000 10000 12000
Distance, Miles (Port to Port)

Figure 22: Total cost comparison of different packaging systems depending on
the different route (shipping) time of the container

7.2.3. Simulation 3: Impact of loss rate (return rate) of reusable containers
Losing reusable containers clearly impact on the total cost of the container
operation for both RPC and REN.

As shown in Table 81, unit cost does not change,

but the total cost changes because the total number of new packages needed increases
as loss rate increases. In case of RPCS, the packager needs only 20,840 new RPCs
with average loss rate of 5 percent (TRA2 (90, 95, 99)), but the packager needs 29,520
new packages when 20 percent of containers are lost (TRA (70, 80, 90)).

2

TRA: Triangular Distribution (low, medium, high)

145

Table 81: Cost comparison of RPC and REN when loss rate changed
RPC

REN

Loss rate

TRA(70, 80, 90)

TRA(80, 90, 95)

TRA(90, 95, 99)

TRA(70, 80, 90)

TRA(80, 90, 95)

TRA(90, 95, 99)

New Pkg

29,520

24,840

20,840

29,201

24,781

20,981

Total Pkg

88,630

92,080

97,730

87,931

93,241

98,681

Loss

16,652

10,166

4,865

16,509

10,327

4,936

UC
TC
(US
Mil. $)

14.25

14.25

14.25

10.72

10.72

10.72

25.24

21.23

17.81

18.78

15.94

13.49

※ Note: UC: Unit cost, TC: Total cost

This difference is compared to EXPS graphically as shown in Figure 23.
30

Total cost, US Mil $

25

20

Reusable

15

Rental
Expendable

10

5

0
80

90
Retention rate, percent/year

95

Figure 23: Total cost comparison of different packaging systems depending on
the different loss rate of the container

The total cost of RPCS is already exceeding EXPS when the loss rate is up to
10 percent (TRA (80,90,95)) and the total cost of REN is also approaching rapidly to
that of EXPS. When the total cost of EXPS is 19.77 million US for 2 years of
operations and only the loss rate is considered, the minimum threshold of RPCS and
146

RENS should be less than 8.23 percent and 22.5 percent respectively based on the
linear regression result. (Linear regression equation of RPC is y = -0.4813x + 63.939, R²
= 0.9791 and Linear regression equation of REN is y = -0.3428x + 46.35, R² = 0.9787)
7.2.4. Simulation 4: Impact of container cost for 2 years of operations
Container costs impact directly on unit costs, and it is the most significant
reason to increase reusable packaging costs. Since most RPCS and RENS containers
are generally more expensive than expendables, relative container cost should be
always considered as the most important factor to decide the packaging type.
As shown in Table 82, as the rental period increases, total REN cost increases
dramatically.

Rental cost for 100 days is similar with RPCS, but the rental cost for 300

days is almost double of RPCS cost. This simulation shows that packaging and
logistics managers should take account of the relationships between rental period and
container purchasing cost as well as long term total cost impacts.
Table 82: Cost comparison when container costs changed
Expendable
CC

Reusable

Rental (100 days)

Rental (300 days)

0.5

1.5

3

2

5

10

2

5

10

6

15

30

0.30

0.30

0.30

0.35

0.35

0.35

0.37

0.37

0.37

0.37

0.37

0.37

FTC

4.59

4.59

4.59

4.59

4.59

4.59

4.59

4.59

4.59

4.59

4.59

4.59

BTC

-

-

-

2.02

2.02

2.02

2.02

2.02

2.02

2.02

2.02

2.02

WC

2.07

2.07

2.07

2.48

2.48

2.48

2.27

2.27

2.27

2.27

2.27

2.27

AUC

0.44

0.44

0.44

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

UC

7.90

8.90

10.40

11.46

14.46

19.46

11.28

14.28

19.28

15.28

24.28

39.28

※ Note: CC: Container cost, AC: Administration cost, FTC: Forward transportation cost, BTC: Backhaul
transportation cost, WC: Warehousing cost, AUC: After use cost, UC: Unit cost, TC: Total cost

147

7.2.5. Simulation 5: Average daily volume
Average daily volume (ADV) explains the number of packaging containers
needed. In this case, EXPS is competitive when the ADV is low, but the RPCS and
RENS systems are more competitive when ADV is increased.
The RENS has competitive advantage over both EXPS and RPCS regardless of
average daily volume because a company can reduce reconditioning activities (cleaning,
repairing, etc.) for packaging containers. See Table 83 and Figure 24.

Table 83: Cost comparison when average daily volume of containers changed
Expendable

Reusable

Rental

Daily volume

1500

3000

7500

1500

3000

7500

1500

3000

7500

Total Pallet used

78,520

156,520

390,520

49,562

89,182

208,382

49,680

90,220

208,060

No of Containers on pallet

30

30

30

30

30

30

30

30

30

CC

1.44

1.44

1.44

4.79

4.79

4.79

1.44

1.44

1.44

AC

0.297

0.297

0.297

0.374

0.374

0.374

0.348

0.348

0.348

FTC

4.593

4.593

4.593

4.593

4.593

4.593

4.593

4.593

4.593

2.019

2.019

2.019

2.019

2.019

2.019

BTC
WC

2.073

2.073

2.073

2.478

2.478

2.478

2.274

2.274

2.274

AUC

0.438

0.438

0.438

0.018

0.018

0.018

0.018

0.018

0.018

UC

8.832

8.832

8.832

14.271

14.271

14.271

10.691

10.691

10.691

Total Cost (US Mil $)

20.8

41.5

103.5

21.2

38.2

89.2

15.9

28.9

66.7

※ Note: CC: Container cost, AC: Administration cost, FTC: Forward transportation cost, BTC: Backhaul
transportation cost, WC: Warehousing cost, AUC: After use cost, UC: Unit cost, TC: Total cost

148

Total packaging system cost,
US MIL $

120.00
100.00
80.00
Expendable

60.00

Reusable
40.00

Rental

20.00
1500

3000
Average daily volume, ea

7500

Figure 24: Total cost comparison of different packaging systems depending on
the different average daily volume of containers used

7.2.6. Simulation 6: Number of containers on a pallet
In this simulation, pallet size for each type of containers is considered the same,
but the size and number of containers on a pallet are changed.

Regardless of

container types, the number of containers on a pallet impacts greatly on the unit
logistics cost as shown in Figure 25.

Note that the graph is nonlinear, and unit cost

drops significantly when the number of containers on a pallet increases from 20 to 60.
This simulation illustrates a company could utilize the best cost efficient option
when it designs a container size to fit on a pallet or a sea container perfectly.
result also implies the important of unit load standardization.

This

A well-designed

container should be a perfect fit to a unit load and standard size of base pallets, sea
containers, cargos, trucks and other loading devices.

149

25.000

Unit cost, $

20.000
y = 95.942x-0.55
R² = 0.9826

15.000

Expendable
Reusable

10.000

Rental
5.000

y = 156.42x-0.775
R² = 0.9956

y = 110.78x-0.733
R² = 0.9938

0.000
0

20

40
60
80
100
Number of containers on a pallet

120

140

Figure 25: Unit cost comparison of different packaging systems depending on the
different number of containers on a pallet
7.2.7. Simulation 7: Comparison of transportation cost between straight wall and
collapsible container types
Reusable plastic containers vary depending on their function and customers’
needs.

Collapsible and nesting features of RPCs especially contribute to reducing

backhaul volume.

As shown in Table 84, container quantity per pallet during backhaul

transportation process should be dramatically increased and will reduce backhaul
packaging system cost.
Consequently, the simulation result is similar to Simulation 6 although the total
cost difference is not significant.

Adding the fact that the relative RPC unit cost with

collapsible and nesting features is usually higher while packing quantity is usually less
than straight-wall RPCs, backhaul volume reduction by adding collapsible or nesting
functions on RPCs does not provide a significant cost saving in terms of total packaging
system cost.

150

Table 84: Comparison of total packaging system cost between straight wall and
collapsible container types for 1 year
Cost

Reusable
BVF
0.7

0.5

4.79
0.35
0.02

4.79
0.35
0.02

0.71
2.72
2.48
11.06
100
9.9

1
Container cost
Administration cost
Forward transportation
cost
Backhaul transportation
cost
Warehousing cost
After use cost
Unit cost
Cost difference, percent
Total cost
(for 1 year)

7.3.

1.0

Rental
BVF
0.7

0.5

4.79
0.35
0.02

1.44
0.37
0.02

1.44
0.37
0.02

1.44
0.37
0.02

0.49

0.35

0.71

0.49

0.35

2.72
2.48
10.85
98
9.8

2.72
2.48
10.71
97
9.6

2.72
2.27
7.53
100
6.6

2.72
2.27
7.32
97
6.4

2.72
2.27
7.18
95
6.3

Summary of scenarios
By breaking down each analysis, major findings from 7 different scenarios are

summarized as below.
a. The dynamic simulation provides more realistic and real-time situation at
each logistical phase which can be used to inform packaging and logistics
decisions. By showing realistic material flow throughout supply chain, a
packaging or logistics manager can identify possible activities that cause
bottlenecks in its supply chain such as long lead time at the ports.
b.

This dynamic simulation was used to estimate the break-even point of
each packaging system in terms of total packaging system costs. Such
analysis can be used by packaging and logistics managers to decide a
better packaging option that may be beneficial for their company.

For

example, if a company estimates the break-even point of reusable
packaging system that is 2 years, but this company changes the product’s
151

outside dimension every 1.5 years, this company may not get any cost
savings by using a reusable packaging system.

Contrary, if the company

tends to keep product dimension and does not need to change packaging
specifications beyond the break-even point for reusable packaging system,
the company may save total packaging system costs over the years.
c. Distance is an important factor when reusable packaging system is
considered and it is directly related to transportation cost and cycle time
which are critical factors for implementing returnable packaging system.
Longer cycle time means more containers required within the loop. Longer
cycle time is usually associated with poor container management that can
easily slow down inventory turnover, replenishment speed, etc.

It is

notable that RPC is a worse option for long distance delivery than EXP and
REN because the average route time (194 days) is too long to own
containers.

However, both RPCS and RENS are much economical for

shorter distance and cycle time delivery (e.g. 500 miles with less than 30
days).
d. Losing reusable containers during return process cause a significantly
important, but unnecessary and non-value added activity: purchasing and
replenishing new containers.

RPCS with good management (average loss

rate of 5 percent) can save 29.4 percent of total packaging system costs
compared to poor container management case (average loss rate of 20
percent).
e. Container cost is the most important factor, regardless of packaging options,

152

and this should be the first thing to consider when reusable packaging
system is considered. Especially, for long distance when high loss rate is
expected, EXP or cheaper RPC container should be considered foremost.
A packaging or logistics manager should try to reduce loss rate by
implementing proper container fleet management system, and determine
the optimum investment level before make a packaging decision.
f.

When the average daily volume of container used is considered, economy
of scale is in favor for RPC and REN options compared to EXP.

Initial cost

of implementing RPC and REN (e.g. new mold cost for RPC, basic contract
cost for REN, etc.) is a significant barrier for packagers and logistics
managers, but this can be compensated when numbers of required
containers are large.
g. Like above, economy of scale is an important cost factor for the number of
containers on a pallet.
h. The general expectation is that the total packaging system costs with
collapsible and nestable containers should be much lower than the straight
wall containers. This is a true statement, but the total cost difference is not
significant because a container with collapsible and nestable features is
usually more expensive while losing inside volume significantly compared to
the container with straight wall.
Finally, the findings and conclusions of the study are discussed in the Chapter 8
of this paper.

153

CHAPTER 8
CONCLUSIONS AND DISCUSSION
8.1.

Importance of the study
Total packaging costs cannot be measured and justified without understanding

logistics costs and performance interrelationships between packaging components and
logistics activities.
a material cost.

In many cases in industry, the packaging cost is only considered as

It is important to include packaging cost in total supply chain cost, so

that packaging managers can evaluate and compare packaging options and identify the
value of packaging in the supply chain.
Packaging is more than a material cost, but is related to many business
activities such as logistics and marketing.

Improper use of packaging and packaging

design could cause numerous unnecessary logistics activities and costs.

Furthermore,

reducing packaging cost does not necessarily reduce logistics cost. Without identifying
accurate packaging and logistics activity costs and their interactions, managers have a
hard time to identify the cause and effect of packaging changes on total logistics costs.
This study explored whether reusable packaging can be a viable option to
replace expendable packaging in a global supply chain.

This research demonstrates

the importance of using a scientific approach in assessing the financial performance of
reusable packaging, using a combination of case studies, ABC method, regression
analysis and dynamic simulation, so the industry can significantly reduce its risk when
making such packaging decisions.

By analyzing results and comparing possible

options, key opportunities and constraints for implementing a reusable packaging
system for international trade have been discovered.
154

8.2.

ABC analysis
First, this research develops a framework for visualizing packaging costs in a

supply chain using the ABC method. Logistics activities are very labor intensive, so
ABC analysis can lead to identifying, removing, replacing or reducing non-value
activities.

ABC can help to show the interaction of each activity and to guide better

packaging and logistics decisions.
This method identifies packaging activities, measures packaging costs, and
quantifies the total packaging costs as well as logistics costs.

This study breaks down

each process into activities and determined the cost of each activity.
By breaking down cost drivers to find out cost generator, ABC shows the resources and
costs movement (up and down) throughout supply chain, so it helps firms to identify
non-value added activities. As Dubiel (1996) pointed out, it may be almost impossible to
identify packaging cost from total supply chain cost in traditional accounting systems,
but this can be achieved using ABC method.
Three packaging system costs (expendable, reusable, and rental packaging
systems) were established using ABC.

For each packaging system, five cost types

were categorized based on the logistics processes and activities involved in an
automotive company shipping overseas: container purchasing cost, administration cost,
transportation (outbound and inbound) cost, warehousing cost, and after use
cost/revenue.

Each process, activity, cost driver and cost unit of the cost driver was

identified based on each process in the cost type.
Because activity cost drivers can be measured quantitatively and used to
calculate total packaging costs, this is used for analysis comparing three packaging

155

system options, namely expendable, reusable and rental packaging system. The
results can also be used to analyze and remove excessive resource-consumption
patterns or to develop alternatives to reduce overall cost in the future.

For example,

company provided data for this study neglected many warehousing activities and
repacking activities that consume resources and increase total system costs.
8.3.

Regression analysis
Second, this research developed a static simulation to reveal interrelationships

between the packaging and supply chain costs.
Based on the activity cost calculations, a comparative and static simulation
demonstrated the relative importance of various factors when evaluating the cost of
container system alternatives.

The developed simulation model was used to evaluate

the relative influence of the various factors and perform a comparative analysis of
reusable and expendable packaging systems.
Eleven scenarios were tested to learn how relative cost changes in one or more
of the variables would influence the total cost. The use of regression model does not
provides relative cost advantages one over the other, but it is useful to bring the general
idea of interactions among each factors.

Overall, container cost ratio and cycle time

were found to be decisive factors in determining the packaging system and it was a
similar result found by (Mollenkopf, et al. (2005, 191-192).
It was found that cycle time is more important factor than shipping distance,
meaning that time is more important than physical distance since it requires an
increased number of containers. Therefore, expendable packaging is more
economical option for prolonged supply chains.

156

Setting an optimum cost vs. container durability for RPCS should be critical to
save total packaging and logistics costs.

Custom charges were an important factor for

company-owned as well as rented reusable packaging.
Although this regression model is based on a certain logistical route by a
specific automotive company and a logistics provider, using the factor ranges and the
generalized relationships among each factor could help other business sectors to use
this regression analysis method.
8.4.

Dynamic simulation
Third, this research evaluated reusable and expendable shipping containers

using a dynamic simulation method. The dynamic simulation shows real-time status of
packaging inventory and helps a packaging and logistics managers to understand the
control of containers as assets. A packager and logistics manager should know not
only how many containers are being shipped, but also they need to know when ordered
containers should arrive.

They need real-time transit data for efficient container

management, so they can reduce a number of containers being used in a reusable
packaging system and prevent loss of containers due to poor management.
For the reusable packaging system, two options (company-owned and rental)
were compared.

Employee interviews, current supply chain flows and cost data

provided by the company are used to construct the simulation model.

Variables

considered include costs of shipping containers, distance and transport time required for
minimum 2-year operation. The results for a 2-year analysis of the operation of the
reusable shipping containers reveal packaging system costs over time. The results
show the expected performance of different packaging types and operation options.

157

ARENA software was used to calculate the number of RPCs and costs for three
international supply chain routes of a company.
Total packaging system costs are carried out based on activity costs calculated
by the ABC cost model and fixed costs provided by the company.

The cost model

calculates the values of system time and cost, resource utilization for the process and
number of entities processed in the process. Express and Statistics functions of
ARENA software are used for calculating the unit cost of each packaging system. The
results of this simulation can help to analyze the interactive and coherent behavior of
packaging and supply chain systems.
8.5.

Validation
The dynamic simulation technique was used to verify the cost calculations of the

static simulation and ABC based cost model. It visualizes actual cost flows depending
on the packaging activities throughout the international supply chain.
Table 85 compares the company-provided data, the calculated ABC model and
simulation for the first year of operation.

Notably, the “Warehousing cost” calculated by

the simulation model and ABC model are relatively higher than company data. The
main reason is that the simulation and ABC model were taking account of time and
activity together while the company-provided data is based on a simple allocation of
costs.

Compared to the company-provided data, all calculated data of ABC and

simulation model are slightly higher.

This may have happened due to using different

metrics when converting the company-provided data to ABC and simulation models.
There also could be a bottleneck in the simulation model that were not obvious in the
company-provided data.

Overall, total cost is little difference among the company-

158

provided data, the ABC model and the dynamic simulation.
Table 85: Cost comparison based on the company-provided data, ABC model,
and dynamic simulation
Company-provided
data

ABC model

Simulation model

Container
types

EXPS

RPCS

RENS

EXPS

RPCS

RENS

EXPS

RPCS

RENS

Container cost

1.44

4.79

0.72

1.44

4.79

0.72

1.44

4.79

0.72

Administration
cost

0.22

0.27

0.26

0.17

0.28

0.25

0.27

0.30

0.27

4.57

4.58

4.57

4.43

4.43

4.43

4.59

4.59

4.59

0

2.12

2.12

-

2.12

2.12

-

2.02

2.02

Warehousing
cost

1.86

1.96

2.11

2.7

2.98

2.66

2.1

2.55

2.35

After use cost

0.4

0.02

0.02

0.4

0.02

0.02

0.44

0.02

0.02

Unit cost

8.49

13.74

9.8

9.14

14.62

10.2

8.84

14.27

9.97

Unit cost
difference from
company data,
percent

100

100

100

108

106

104

104

104

102

Forward
transportation
cost
Backhaul
transportation
cost

※ Note: EXPS = Expendable packaging system cost, RPCS = Reusable packaging system cost, RENS
= Rental packaging system cost

8.6.

Recommendations for reusable packaging optimization
The total costs estimated by the dynamic simulation cost model were generally

higher than the company-provided data and ABC model. Interestingly, forward
transportation costs were higher for the simulation model, but the backhaul
transportation costs for reusable and rental container systems were lower than the
company-provided data. This means that the company is underestimating the forward
transportation cost, but overestimating the backhaul transportation cost.
Similarly, warehousing cost is significantly different. This is because the
159

company data does not take account of cost of each activity while the ABC based
simulation cost models handled every each activity cost and resource.

It means the

company is underestimates these costs.
However, unit cost is cheapest when a company uses an expendable packaging
system while company-owned reusable packaging system cost is highest. Rented
packaging cost is between these. This trend remains same with all cost models.
The total costs estimated by the dynamic simulation cost model were not
significantly different from other models, but some cost elements (e.g. backhaul
transport cost and warehousing cost) are significantly different. This is because the
cost calculations based on company data and ABC analysis are static and more likely
optimal conditions, so it does not present realistic situations.

It is easy to assume that

the time-related costs may vary in the actual supply chain situation due to unexpected
delay such as loading and unloading time in the port or warehouse.
The result shows the possibility of using reusable shipping containers for
international trade although there are several important points shown below that should
be considered before selecting a packaging system.
8.6.1. Container cost
Whether it is a domestic or international operation, container purchasing cost is
the most important factor to decide the profitability of reusable packaging system.

As

shown in Figure 22, RPCS need more than 2.2 years to reach the break-even point to
EXPS, meaning the container life should last longer than 2.2 years in order to gain any
benefit of using RPCS. Using RENS would be a good option for a company that does
not have enough financial resources or distribution networks to operate RPCS.

160

8.6.2. Cycle time
Cycle time is directly related to the amount of containers in the reusable
packaging system.

Delays at the ports, plants or warehouse can also increase cycle

time significantly.
8.6.3. Custom charges
Custom charges such as tariffs matter significantly depending on where
products are shipped. In Korea, tariffs of pallets and packages imported are exempted
if these are re-exported within one year.

Custom charges can be removed if trading

countries agree a free trade agreement.
This can promote using reusable packaging system in international trade only if
country A and B can trade almost same amount of reusable shipping containers with
each other.

However, if a reusable shipping container is shipped to third country, C,

the exporter cannot get the paid tariffs back.
8.6.4. Management options
Users may consider pooling or rental option for international operations.
Management options for returnable shipping containers for international logistics vary
considerably depending on the decision factors such as distance, types of contents,
level of standardization, etc.

Depending on the company’s logistics strategy, an OEM

(manufacturer) may own its containers or let a third party logistics provider handle the
returnable packaging for it.
For the automotive industry, the trend varies depending on the level of
standardization.

In Europe, automotive manufacturers tend not to own the returnable

packaging, but later to share the standard containers by pooling them.

161

However, in

North America, the car manufacturer tends to own its returnable containers as assets;
so generally, the standards vary depending on needs (Coia, 2013). Often, owning
shipping containers can save money because the manufacturer can directly buy the
containers and avoid any hidden purchasing costs or complexity of reimbursement of
rental costs.

Of course, the company must have high visibility on container fleet

movements and excellent packaging management system. There are also a possibility
to lose money by several non-valuable activities such as shipping the empty container
back to the plants, cleaning and sorting containers for the line input, etc.
8.6.5. Geographical location
Because labor, land and material costs tend to be less in developing countries,
where to handle returnable containers is also an important issue during international
logistics process.

For example, average labor cost in a developing country may be just

$5 USD while it could be $40 USD in the US. It would be economical to do most
cleaning, repairing and other reconditioning works in developing countries.
8.6.6. Freight balances between inbound and outbound freights
For example, China has a lot more outbound freight to the US, causing the US
partners to ship empty containers back to China in order to balance. If in each
direction a standard shipping container can be used for different products, there is more
opportunity to balance out the number of containers.
For example, South Africa has many inbound containers for automotive parts,
but not a lot of outbound ones.

By using standardized containers, they can ship in with

automotive parts and out with juice or rubber (Coia, 2013). This would be very difficult

162

for an individual company to manage this, but it could be a good opportunity for global
rental business.
8.6.7. Standardization
Various sizes, structures and processes due to lack of packaging standards lead
to more complexity in supply chain process. Standardization of reusable packaging
systems can boost international trade opportunities and logistical efficiency.

Standards

are being developed for dimensions, structures and procedures including:

•

performance requirements, specification, and test methods.

•

procedures for tracking and tracing of returnable packaging.

•

improved overall quality control and management system providing
harmonized standards for better communication, quality control, safety,
sanitation, international trade and other relevant issues.

•

contributing the environmental aspects of the packaging

•

improving visibility of reusable packaging throughout the supply chain.

A global standard for returnable packaging system is a critical issue to facilitate
an international logistics process. The fact that the standard pallet footprint in North
America is imperial compared to the metric system in Europe and Asia, it requires
additional work to regroup the unit load and is inefficient from logistical standpoint.
International trade also requires a shipping container to fit both land (truck or railroad
cars) and sea container.

Table 86 shows relevant standards and guidelines that have

been developed for reusable packaging.

163

Table 86: Standards and guidelines developed on returnable packaging
International
standards or
guidelines

Organizations
ISO TC122

CEN TC261
Regional /
industrial
standards or
guidelines

ASTM

EPCglobal,
RTI Interest Group
Korean Standards

ISTA 7 Series (under
development)

AIAG
Joint Automotive
Industry Forum
(JAIF)

Standard titles
ISO 17364:2009: Supply chain applications of RFID -Returnable transport items (RTIs)
ISO 17350: Direct Marking on Plastic Returnable Transport
Items (RTIs)
EN 13199 standards: Small Load Carrier Systems, Reusable
EN 13117 standards: Rigid plastics distribution boxes
ASTM D6179-07: Standard Test Methods for Rough Handling
of Unitized Loads and Large Shipping Cases and Crates
ASTM D6881/D6881M-03(2008)e1: Standard Classification
for Standard Plastics Industry Bulk Box/Pallet Unit Size
Classified By Bulk Density
RTI(Pallet tagging) Interest Group Guideline
KS T 1081: Plastic Returnable Containers
KS T 1347, 1348 - Reusable, rigid plastics distribution boxes
— Part 1: General purpose application, Part 2: Testing
methods
Project 7A: Open Reusable Transport Containers for Loads
of 60 lb (27 kg) or Less and Unitized for Shipment on a Pallet
- compression and shock testing
Procedure 7B: Closed Reusable Transport Containers for
Loads of 150 lb (68 kg) or Less - fixed displacement or
random vibration, shock testing, compression and
atmospheric pre-conditioning
Procedure 7C: Reusable Intermediate Bulk Containers atmospheric conditioning, compression, random vibration and
shock testing
Returnable Containers Transported by Truck Guideline
Standard Returnable Fastener Container System
Global Guideline for Returnable Transport Items Identification

The International Organization for Standardization (ISO) Technical Committee
122 (Packaging) approved Working Group 13 for the Returnable Transport System and
two new work item proposals for reusable rigid plastics distribution containers have
been accepted (International Organization for Standardization, 2012).
The two new work item proposals (NWIP 18616 Part 1 and 2) are based on
existing European standard (EN 13117 series on Reusable, rigid plastics distribution
boxes. Part 1: Transport packaging - Reusable, rigid plastics distribution boxes 164

General purpose application, Part 2: Transport packaging - Reusable, rigid plastics
distribution boxes - General specifications for testing) and should be in the committee
draft stage as of November 2014.

Key difference from existing EN standards should

be that these standard shipping container dimensions are based on the modular area
600 mm×400 mm, 600 mm×500 mm, 550 mm×366 mm and subdivisions of it in order to
comply with the guideline from ISO 3394:2012- Packaging -- Complete, filled transport
packages and unit loads -- Dimensions of rigid rectangular packages.

In addition to

NWIP 18616 Part 1 and 2, ISO TC122 / WG13 will develop series of returnable
packaging standards as follows.

•

Packaging - Small load carrier systems - Common requirements and test
methods

•

Packaging - Small Load Carrier Systems - Part 2: Column Stackable System
(CSS)

•

Packaging. Small load carrier systems. Bond Stackable System (BSS)

•

Returnable flat pallets: Principal requirements and test methods

•

Returnable large plastic containers: Principal requirements and test methods

•

Returnable large mesh containers: Principal requirements and test methods

•

Returnable Transport System for Packaging: Guideline for RTIs Tracking and
Tracing

•

Returnable Transport System for Packaging: Guideline for RTIs Safety
Requirements in handling

•

Other standards regarding handling and managing RTIs
165

Developing international standards can reduce technological, economic and
social barriers for international trade.

For a company to have a reusable packaging

system, development of international standards and conforming to the guidelines set by
international standard organizations such as ISO can certainly help to improve business
productivity and logistics efficiency throughout its supply chain. (International
Organization for Standardization, 2013).
8.7.

Key trade-offs
Last, this research identifies key trade-offs for implementing a reusable

packaging system for an international supply chain. While the benefits are numerous,
applying reusable packaging system is not for every supply chain.

The results of this

research show the great possibility of using reusable packaging systems depending on
following conditions.
8.7.1. Cost ratio between expendable and reusable containers
Because of relatively longer cycle time and distance, number of reusable
containers are required.

Even if collapsible or nestable features of containers can save

significant volume during returning process, container purchasing costs should be the
first thing to be considered. Reusable packaging may last longer, but reduction of
container costs is necessary.
8.7.2. Cycle time
A quick turnaround times of containers with great frequency is essential.
Avoiding unnecessary logistical activities during trans-shipment and custom process
should be critical.

166

8.7.3. Standardization
Standardization is an essential element to reduce material costs while improving
operational efficiency throughout supply chain.

It does look easy, but it is actually not

when different countries and logistics environments are involved.

Production lines,

loading/unloading docks, and even pallet sizes can be a big obstacle to apply for
international routes.
8.7.4 Risk assessment:
Uncertainty is always greater during international trades and is also important
element to determine an international packaging system.
other policy changes can affect the cost greatly.

Free Trade Agreement and

Risk of product or production line

changes can limit the uses of reusable container system.
In conclusion, this research tries to provide the most financially sound packaging
solutions for the target industries such as global manufacturing companies, packaging
and logistics providers, and other involved parties.

It advances the use of a multi-

methodology that combines case studies, ABC and simulation, as a new tool for
packaging research that can be generalized to other packaging applications.
As shown in Figure 26, any elements of either logistics or packaging processes
will cause changing each activity, cost driver, each cost and eventually other costs.
This study concerned with economic impact by adopting different packaging systems in
the international supply chain.

167

Figure 26: Cost identification loops and interaction between logistics and
packaging decision
8.8.

Limitations and recommendations for future research
ABC based cost simulation
imulation and dynamic simulation modeling can allow the user

to analyze the future performance of the network and to understand the complex
relationship between the parties involved. The present simulation model has been
tested by using current automotive part logistics system between Korea and the US.
US
The model can be further modified for simulating different logistical networks and parties
involved to help improve the international supply chain performance.
This research contributes to the knowledge base in reusable packaging
management by proposing a simulation modeling approach under a realistic scenario.
This study shows the usefulness of using a simulation to save time and money while
analyzing
alyzing the effects and contribution of each variable at different conditions. It can be
used to determine areas
as were the performance is above and below the expected level

168

of operations in terms of choosing right packaging types and systems. This research
contributes to optimize overall network performance by changes in impact variables
such as packaging types, routes, distance and other logistics services.
This research has some limitations, providing opportunity for further research.
First, this cost model is focused on an automotive part supply chain from a single
supplier to a single customer, and back.

It is assumed that reusable containers are

shipped back to the supplier as being empty. Return transportation costs should be a
lot lower if the company can fill the containers with other products.
In addition, other aspects of reusable packaging needs to be considered in
future study.

Environmental performance such as packaging waste generated and

greenhouse gas emissions should be taken account in the model. Ergonomics for
handling, hygiene and cleaning issues for food product containers, safety and fatigue of
reusable containers during handling and stacking are also important subjects for
successful establishment of reusable packaging system internationally.

169

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