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Most photographs reproduce acceptably on positive microfilm or microfiche but lack the clarity on xerographic copies made from the microfilm. For an additional charge, 35mm slides of 6”x 9” black and white photographic prints are available for any photographs or illustrations that cannot be reproduced satisfactorily by xerography. 6714352 O b ly a , Alex O. EFFECTS OF RESOURCE CONSTRAINTS ON THE EXPANSION OF THE PALLET INDUSTRY IN LOWER MICHIGAN M ichigan Stale University University Microfilms International 300 N. Zeeb Road, Ann Arbor, Ml 48106 Ph.D. 1987 PLEASE NOTE: In all c a se s this material h as been filmed In th e b est possible w ay from the available copy. Problems encountered with this docum ent have been Identified here with a ch eck mark V 1. Glossy photographs or p a g e s _____ 2. Colored Illustrations, paper or p rin t_______ 3. Photographs with dark background 4. Illustrations are p o o r copy______ 5. P ages with black marks, not original c o p y ______ 6. Print shows through as there is text on both sides of p a g e _______ 7. Indistinct, broken o r small print on several pages ^ 8. Print exceeds m argin requirem ents______ 9. Tightly bound copy with print lost in s p in e _______ . 10. Computer printout p ages with Indistinct print______ 11. P age(s)____________ lacking w hen material received, a n d not available from school or author. 12. P ag e(s)____________ seem to b e missing in numbering only as text follows. 13. Two pages n u m b e re d 14. Curling and wrinkled p a g e s ______ 15. Dissertation contains pages with print at a slant, filmed a s received _ 16. Other__________________________________________________________________________ . Text follows. University Microfilms International EFFECTS OF RESOURCE CONSTRAINTS ON THE EXPANSION OF THE PALLET INDUSTRY IN LOWER MICHIGAN By Alex 0. Obiya A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Forestry 1SB6 ABSTRACT EFFECTS DF RESOURCE CONSTRAINTS ON THE EXPANSION OF THE PALLET INDUSTRY IN LOWER MICHIGAN By Alex 0. Obiya The purpose of this study was to analyze the potential f d t expansion of pallet industry in Lower Michigan; i.e.* spatial analysis of pallet plants vis-a-vis surplus wood areas and markets in the region. This allowed us to deter­ mine whether expansion of the pallet industry is likely to be profitable in the region, and if positive, what should be the potential locations for future pallet plants in the Lower Michigan area? This was accomplished in the content of resource constraint analysis and its impact on the loca­ tional aspects of the pallet industry in the region. The analytical tool used to solve the problem was firmlocation model tion). (actually concerned with establishment loca­ The purpose of using this model is to ascertain and determine the appropriate "spaces” (routes) that offer the optimal locations for the next pallet plant(s), trial expansion take place in the region. industry expands, should indus If the pallet then the results of locational analysis are linked with input-output multipliers to assess potential economic impacts of pallet plants. Hence for the purpose of economic development strategy, one could quantify income and employment impacts that would accrue to the region from additions of pallet establishments. However the main finding from analytical results is that the pallet industry in Lower Michigan has excess capa­ city; i.e., the production capacity of the current plants is not fully being utilized. Dnly when this excess capacity in the pallet industry is utilized, should an alternative of building new pallet plants in the region be considered. Hence any increased demand for pallet products could be met by increasing production within existing capacity. Though evidence suggests surplus timber in state forests, there is insufficient demand to justify further processing of timbe for pallet manufacturing. ACKNOWLEDGEMENTS I wish to express my sincere appreciation to Dr. Daniel E. Chappelle who as my major professor provided me counsel and continued support in the graduate program. Professor Chappelle u/as both the chairman of my guidance committee and the dissertation director. His advice on research and academic problems was always welcome. I'm particularly grate­ ful to his contributions in the initiation and development of this dissertation project. I'm also indebted to Dr. Paul Strassman, Professor of Economics; Dr. Glenn Johnson, Professor of Agricultural Eco­ nomics; Dr. Milton Steinmueller, Professor of Resource Develop­ ment; and Dr. Robert Marty, Development. Professor of Forestry and Resource All of whom served on my dissertation committee and critically reviewed the manuscript. I also appreciated comments and suggestions regarding practical mechanics of the pallet industry in the state offered by Mr. James Donald­ son of the Michigan Department of Commerce. Thanks are also due Dr. Larry Tambough for offering me financial support at a critical time in my research efforts. ii I also appreciated the programming assistance offered by Nilson Amaral, Research Assistant* Department of Resource Development. Last but not least, my debt also extends to my parents, Walter and Lusia, for giving me the emotional support and encouragement in the course of this academic endeavor. TABLE OF CONTENTS Page LIST OF T A B L E S ......................................... viii LIST OF F I G U R E S ......................................... CHAPTER I - INTRODUCTION ............................. GENERAL PROBLEM .................................. STUDY O B J E C T I V E S ................................ RESEARCH APPROACH ................................ PLAN OF THE S T U D Y ................................ CHAPTER II - DESCRIPTION OF THE STUDY REGION . . . . P O P U L A T I O N ...................................... I N C O M E ........................................... E M P L O Y M E N T ...................................... MANUFACTURING .................................... LAND U S E ......................................... A g r i c u l t u r e ......................... ' . . . M i n i n g ........................... Forestry . . . . . . . . . SUMMARY . . . . . . . . . . CHAPTER III - MICHIGAN PALLET INDUSTRY ............... TIMBER RESDURCE BASE FOR EXPANSION OF THE PALLET INDUSTRY .................................. NATURE OF THE PALLET I N D U S T R Y .................. A PALLET - AN INDUSTRIAL COMMODITY ............. PALLET MANUFACTURE IN MICHIGAN ................ VERTICAL INTEGRATION MODEL OF A PALLET FIRM . . Standard Pallet Manufacturing Process and Technology of aPallet Firm . . . . . CONSTRAINTS THAT AFFECT PALLET PRODUCTION AND CONSUMPTION ................................ Timber Species ............................. C o m p e t i t i o n .................. Demand ...................... Role of Price in the Pallet Industry . . . PALLET MARKET STRUCTURE ......................... COMPARATIVE ADVANTAGE ........................... iv xi 1 1 5 6 7 11 11 13 IB IB 21 21 22 25 26 30 30 36 36 30 41 41 47 47 50 51 53 54 55 Page CHAPTER 11/ - MODEL D E V E L O P M E N T ...................... GOAL OF THE FIRM-LOCATION M O D E L ................ RESEARCH CONTEXT ............................... MATHEMATICAL FORMULATION OF A FIRM-LOCATION M O D E L ............................................ N o t a t i o n ............. * .............. . . PRODUCTION EQUILIBRIUM ........................ FOREST RESOURCE B A S E .......................... TRANSFER COSTS . . . . * ............... MANUFACTURING COSTS ............................. PALLET PRICE ................................... PLANT CAPACITY AND CONSTRAINT COSTS . . . . . . DESCRIPTION OF THE COMPUTER PROGRAM .......... CONTRASTS BETWEEN THE LOCATION MODEL AND THE COMPUTER PROGRAM .......................... INPUT-OUTPUT MODEL ............................. Structure - Input-Output Model ........... MULTIPLIER ANALYSIS FROM STATE'S INPUT-OUTPUT MODEL . ........................................ PALLET INDUSTRY MULTIPLIERS IN THE STATE ... CHAPTER V - DATA, AGGREGATIONS, AND MODEL INPUTS . . 5B 58 58 59 59 62 63 63 64 65 65 67 69 71 73 74 76 B1 FUNCTIONING OF THE M O D E L ................. STUMPAGE SUPPLY TO THE INDUSTRY ............... SUPPLY REGIONS FOR RAW MATERIALS . ........ PLANT LOCATIONS IN LOWER MICHIGAN ............. WOOD PALLET M A R K E T S ................. DEMAND PRICES OF PALLETS ...................... ESTIMATING PALLET COST ........................ RAW M A T E R I A L S ................................... LABOR COST IN A PALLET F I R M .................... CALCULATION OF TRANSPORT COSTS ............... MEASURING ROAD HAUL D I S T A N C E S .................. TRANSFER COSTS ................................. PLANT C A P A C I T I E S ............................... TOTAL OUTPUT ............................. DATA S U M M A R Y ................................... .81 B2 83 90 93 96 99 100 102 104 105 107 108 109 Ill CHAPTER VI - ANALYSIS AND R E S U L T S .................. 112 RESULTS AND INTERPRETATION OF COMPUTER RUNS . . Benchmark Solution (RUN-I) ............. . Interpretation ...................... Increased Demand Solution Run (RUN-II) . . Interpretation ...................... Full Capacity Solution Run (RUN-III) . . . Interpretation ...................... Lou Capacity Solution Run (RUN-IV) . . . . ANALYSIS OF PALLET-FIRM LOCATION RESULTS ... v 112 116 121 122 122 123 123 124 124 Page DISCUSSION DR PROBLEMS DURING TESTING ......... Problems in Computer Model Structure . . . Computation Problems . . . . . . . . . . . COSTS OF MODELING AND PROGRAMMING'.............. Data Collection . . . . . Model Design . . . . . . . . .............. Costs of P r o g r a m m i n g ........... ADJUSTMENT OF THE MODEL AFTER TESTING ......... INCOME AND EMPLOYMENT IMPACTS FROM THE INPUTOUTPUT M O D E L .................................... Income Impacts Scenario... . ........ . . . Estimated New Employment ................ 135 137 13B CHAPTER VII - EVALUATION OF THE M O D E L .............. 139 CRITICISM OF THE IDEAL M D D E L .................. CRITICISM OF THE APPLIED MODEL ................ Temporal Dimension ......................... Aggregation Error ......................... Demand Function ........................... Transport Cost-Configuration . . . . . . . Pallet Unit D e f i n i t i o n .................... Supply and Demand ................ ASSESSMENT OF THE MODEL FOR POLICY ANALYSIS . . Timber S u p p l y .................. I n f r a s t r u c t u r e .................. Production Functions . ........... Agglomeration Economies .................. Assessment ............... CHAPTER VIII - SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ...................................... SUMMARY OF R E S U L T S ............................. C O N C L U S I O N S ...................................... RECOMMENDATIONS .................................. Strategies for Economic Development . . . SUGGESTIONS FOR FURTHER RESEARCH .............. 129 129 131 131 131 132 132 133 139 142 142 143 144 145 14B 147 148 149 ISO 151 152 153 155 155 158 161 161 164 APPENDIX A - SYMBOLS FOR F L O W C H A R T ................... 167 APPENDIX B - GROSS LOGICAL FLOWCHART 168 APPENDIX C - PROGRAM CODE N O T A T I O N ................... 172 APPENDIX D - DETAILED FLOWCHART ..................... 175 APPENDIX E - SOURCE CODE L I S T I N G ..................... 182 APPENDIX F - COMPILED DATA FOR THEM O D E L ............. IBB vi ................ Page LITERATURE CITED GENERAL REFERENCES ................................... 199 ................................. 204 uii LIST DF TABLES Table 1 2 3 4 5 6 7 0 Page POPULATION FIGURES BETWEEN 1960-1980 AND PERCENTAGE CHANGE INLOWERMICHIGAN .............. 12 HOURS AND EARNINGS OF MANUFACTURING PRODUC­ TION WORKERS IN MICHIGAN BY INDUSTRY GROUP: 1983 14 LABOR AND PROPRIETORS' EARNINGS COMPONENT OF PERSONAL INCOME BY INDUSTRIAL SECTORS IN MICHIGAN: 1982 15 EMPLOYMENT, PAYROLLS AND AVERAGE WEEKLY EARNINGS IN MCIHIGAN BY INDUSTRIAL SECTORS: 1983 17 DISTRIBUTION OF ESTABLISHMENTS BY EMPLOY­ MENT SIZE IN MICHIGAN: 1977... .................. 17 MICHIGAN MANUFACTURING DATA: & 1983 19 1977, 1981 MOTOR-VEHICLE RELATED EMPLOYMENT BY MAJOR MANUFACTURING INDUSTRIES IN MCIHGIAN: SEPTEMBER, 1963 ................................. 20 NUMBER OF FARMS AND FARM ACREAGE IN MICHI­ GAN 1940-1982 .................................. . 22 9 SUMMARY OF SELECTED AGRICULTURAL INFORMA­ TION ...............................................23 10 VALUE OF MICHIGAN MINERAL PRODUCTION BY PRODUCT: 1 9 B 2 ............. . .................... 24 11 COMMERCIAL FDREST LAND IN LDWER MICHIGAN REGION AND OWNERSHIP CLASS: 19B0 ............. 27 12 NET TIMBER VOLUME ON COMMERCIAL FDREST LAND BY SPECIES GROUP ANDREGION: 1 9 B 0 .................28 13 NET VOLUME OF GROWING STOCK ON COMMERCIAL FOREST LAND BY SPECIES GROUP AND AREA, MICHIGAN: 19B0 ............... viii 31 NET ANNUAL GROWTH AND REMOVALS OF GRDWING STOCK ON COMMERCIAL FOREST LAND BY SPECIES GROUP AND AREA, NORTHERN ■ J s a 2 I s I it: % ID Forest Resources Base fc(Inuentory) 'multiple use L (output alternatives) £— 'harvest cost r Wood Supply (Surplus) Region ,__at Roundwood Demand 'Inventory I r f ‘Access to raw materials Total Stumpage Supply 'resource class (size, class, species, etc.) 'Land base 'biomass production Pallet ^ Industry ----- a |Employment — X — Income fw' / k— £--- __ I'Industry production & capacity --- 1'transportation costs |£------- -------- ------------- ^ ] .. ---1'Plant location |'Labor requirements (skill) 1 * 'Inventory *exports/imports Fina Demand End-Use Markets FIGURE 2 - FLOWCHART OF RESOURCE CONSTRAINTS MODEL FOR INDUSTRIAL PRODUCTION AND EXPANSION OF PALLET INDUSTRY V tJ ______ — Regional Economy (I/O table) -Growth potential -Interde­ pendence -Competi­ tion [ Access to Markets type of Logs CHAPTER II DESCRIPTION OF THE STUDY REGION The region of study is the Lower Michigan region which covers both the Northern Lower Peninsula and Southern Lower Peninsula. Of the 83 counties in the entire state of Michi­ gan, only fifteen counties are in the Upper Peninsula. The latter is a rural area with a natural resource based economy. Hence one can conclude that this study covers the portion of state that has the bulk of human and economic activities in the region (see Figure 3). POPULATION According to the 1980 Census of Population, population lived in the Lower Michigan area. 9B.3JE of Whereas only about 3.756 of the population lived in the Upper Peninsula (this is about 374,000 people). The entire state of Michigan had a population of 9.2 million inhabitants in i960 (Bureau of Census, 1900). Table 1 shows population growth rates in the region between periods of 1960-1900. In 1980, the Michigan population could be classified as 11% urban and 29$ rural. This can be contrasted to U.S. 11 12 Region 1: Upper Peninsula C** j i * * A- v a l C L - — .. ' w-'.jF**ruse escct* Region 2: Northern Lower Peninsula MMWfi : jk \Wttra*J VMm* acts*4 -teas MCCkA Ira** ciwro* l*i4wt Ufir 14rj* Region 3: Southern Lower Peninsula LtfimUfH BMfrtt*** *C*§ FIGURE 3. MAP O F COUNTIES IN TH E STUB* REGION 13 TABLE 1 POPULATION FIGURES BETWEEN 1960-1980 AND PERCENTAGE CHANGE IN LOWER MICHIGAN Year Urban Rural Total 1960 5,739,132 2,084,062 7,B 2 3 ,194 £ Change 13.45 1970 6,566,483 2,308,600 8,875,083 4.36 1980 6,551,551 SOURCE: Bureau of Census, U.S. Census of Population: 1970. Number of Inhabitants, Michigan. (Washington, D.C.: 1982) 2,710,527 9,262,078 demographic trends where the population is approximately 75)6 urban and 2556 rural. Between 1960 and 1970 there uas 14J6 increase in population compared to 456 between 1970-1580. Also during periods between 1975-1980 there was a net out­ migration of about a half million people. This figure always tends to be buried in population statistics. In simple terms, this means that more people moved out of the state to other parts of the country than moued in during the same time periods. INCOME The state of Michigan had a mean family income of $12,296 in 1969, but taking into account inflation that is equivalent to about $14,876 in 1976 dollars (U.S. Dept, of Commerce, 1976). Personal income is the current income of residents 14 of an area from all sources. It is simply the sum of seueral hundred individually estimated component flows, both monetary and non-monetary, and it encompasses most forms of income flowing to persons including Federal* State and local govern­ ments, households, institutions, and foreign governments. In 1975 total personal income for the state was $57,142 mil­ lion whereas per capita personal income in the same period was $6,240 (U.S. Dept, of Commerce, 19B3). By 1983, total personal income for the state was $104,963 million and per capita personal income was $11,572. Table 2 shows work hours and earnings of a sample of production workers in Michigan. TABLE 2 HOURS AND EARNINGS OF MANUFACTURING PRODUCTION WORKERS IN MICHIGAN BY INDUSTRY GROUP: 1983 Average Industry Group weekly hours Durable goods Lumber and wood prod. Furniture & fixtures Primary metal products Fabricated metal prod. Nonelectrical machinery Electrical machinery Transportation equip. Motor vehicle & equip. Nondurable goods Food 4 kindred prod. Textile mill prod. etc. Paper & allied prod. Printing & publishing Chemicals & allied prod. TOTAL SOURCE: 42.B 41.B 40.B 41.4 42,3 41,2 41,9 44.2 44.3 41.5 41,4 42,4 44.5 37.6 41.1 42.5 hourly earnings weekly earnings $ 12.10 7.30 9.14 12.22 11.38 11.62 10.04 13.32 13.45 9.B3 9.B7 11. 81 10,63 8,91 11.41 11.62 $517.53 304.93 372.67 505.36 481.47 479.21 420.47 5BB.29 596.40 407.71 406.36 500.60 472.5B 335.59 457.38 494.02 Bureau of Labor Statistics. 19B4. Employment and Earnings. Washington, D.C. and Michigan Employment Security Commission. 15 When one looks at earnings from Industrial sectors, four industrial sectors form the bulk of earnings in the state. These are manufacturing sectors (38.29$), services (31.45$), retail trade (17,09$) and state and local governments (12.1B$). The auto industry accounts for the largest part of manufac­ turing output. distribution. Table 3 indicates the earnings and sectoral In the context of this study, the pallet indus­ try and other wood products industries are included within the manufacturing sectors. TABLE 3 LABOR AND PROPRIETORS' EARNINGS COMPONENT OF PERSONAL INCOME BY INDUSTRIAL SECTORS IN MICHIGAN: 19B2 Earnings millions of dollars SECTOR Manufacturing Nondurable goods Durable goods Services Retail trade Transportation $ public util. Wholesale trade Construction Finance, insurance $ real estate Farm Mining Agricultural seruices, forestry, & others State & local govt. Federal, civilian Federal, military TOTAL SOURCE: percentage 27,219 4,489 22,334 12,145 6,292 38.29 6.87 31.42 17.09 B.B5 4,290 3,915 2,659 6.04 5.51 3.74 3,060 749 320 4.32 1.05 0.45 16B 0,660 1,337 262 0.24 12.18 1.88 0.37 71,082 100.00 Bureau of Economic Analysis. 19B2. Regional Eco­ nomics System. U.S. Department of Commerce, Washing­ ton, D.C. IE EMPLOYMENT Table 4 indicates that the greatest employment by eco­ nomic activities is in the manufacturing sectors. It has been estimated that 035,980 persons tuere employed in Michigan's manufacturing establishments during 1903. The most important groups ranked by employment were transportation equipment, machinery except electrical, fabricated metal products and primary metal industries. These sectors account for 54$ of the State's 1983 manufacturing employment. These have been the same industries dominating the economic landscape as far as jobs are concerned since 1972. The most important counties in the state ranked by employment are U/ayne, Macomb and Oak­ land, all located in the southern portion of the state. These counties account for about 50$ of employment in Michigan. Three other significant sectors are wholesale and retail trade, services and transportation, ties. communication and utili­ Table 5 further classifies distribution of industrial establishments by employment size. Approximately 66$ of estab­ lishments employ 19 or fewer people. employers are small businesses. Hence most Michigan The pallet industry fits in this pattern - it is mostly composed of small establishments throughout the state. Nevertheless employment in wood-based manufacturing is estimated at 57,220. 5ome 78$ of the total employment is in southern lower Michigan because of the con­ centration of secondary manufacturing in this region. Primary manufacturing employment is spread all over the state with about 59$ of the total in Lower Michigan (James et al., 1982). 17 TABLE 4 EMPLOYMENT, PAYROLLS AND AVERAGE WEEKLY EARNINGS IN MICHIGAN BY INDUSTRIAL SECTORS: 1983 Average employment Agriculture, forestry & fisheries Mining Construction Manufacturing Transportation, comm. & utilities Wholesale & retail trade Services Finance, insurance & real estate TOTAL SOURCE: Payrolls (000) Average weekly pay 13,367 8,313 73,281 835,980 $ 36,602 56,954 414,029 5,726,046 $210.06 527,02 434.61 527.80 126,868 794,072 461.46 678,013 589,146 2,107,675 2,285,131 239.12 298.36 145,804 648,448 342.11 2,470,770 12,068,957 376.06 Bureau of Research and Statistics, 19B4, Michigan Employment Security Commission, special release. TABLE 5 DISTRIBUTION OF ESTABLISHMENTS BY EMPLOYMENT SIZE IN MICHIGAN: 1977 Number of Persons Employed MICHIGAN 1 to 4 5 to 9 10 to 19 20 to 49 50 to 99 100 to 249 250 to 499 500 to 999 1000 to 2499 2500 employees SOURCE: All Establishments 15627 5413 2227 2607 2621 1261 886 320 142 72 70 Bureau of the Census. 1977, Census of Manufacturers. U.S. Department of Commerce • 18 MANUFACTURING The total value added by manufacture for the state amount­ ed to $37,566 million in 1977, an increase of approximately 6156 from the 1972 figure of $23,376 million. are expressed in 1977 dollars. These data Table 6 summarizes important manufacturing statistics for the state. The auto industry forms the largest portion of the manu­ facturing sector in the state. In 1903 about 2 million cars and 697,060 trucks and buses urere produced in Michigan. These figures account for about 3056 of total cars, trucks, and buses produced in the United States (see Table 6). Three key cities or metropolises in Michigan are the production centers for the auto industry! Detroit, Flint and Lansing. Total employment in the motor vehicle industry was 951,100 in Michigan in 1983. Table 7 further highlights the impact of automotive sector on employment in the related industries in the 5tate. Since the subject of this study is the pallet industry which is included under the wood products industry uiithin the manufacturing sector, a brief outlook of wood-based manu­ facturing is necessary. The 198D population of wood-using mills is estimated to be 1,637. This includes 984 mills in the lumber and wood products group, 350 in wood furniture, and 363 in paper and allied products (James et al., 1982). Since we are not dealing with primary processing here, this section concerns secondary manufacturing where inputs from 19 TABLE 6 MICHIGAN MANUFACTURING DATA: 1977, Value Item/Year 177,279 Number of establishments (1903) 2,470,770 Number of employees (19B3) Payroll (all employees) ($1000) (1903) 51,637,560 Value added by manufacture ($1000) Cost of materials ($1000) Value of shipments ($1000) (1977) 37,566,000 (1977) 56,775,000 (1977) New capital expenditures ($1000) 93,757,100 (1977) 3,739,200 Value of export shipments (1901) ($1000) 10,275,000 Production of motor vehicles (1983) cars trucks and buses Percentage of U.S. auto-production cars Trucks and buses SOURCE: 19B1 & 1983 2,077,000 697,000 (1983) 30$ 29$ Bureau of the Census. Reports-1977, 19B1 & 1903. Census of Manufactures. U.S. Department of C o mmerce. 20 TABLE 7 M0T0R-VEHICLE RELATED EMPLOYMENT BY MAJOR MANUFACTURING INDUSTRIES IN MICHIGAN: SEPTEMBER, 1983 Industry (SIC code) Number of Employees Manufacturing Durable goods Lumber and wood Furniture and fixtures Metals Primary metals Fabricated metals Nonelectrical machinery Electrical machinery Motor vehicles and equipment Assembly Parts and accessories Other transportation equipment Other durable goods Nondurable goods Food and kindred Textile mill products and apparel Paper and allied Printing and publishing Chemicals, petroleum and related Other nondurables goods SOURCE: 438,000 □ 700 75,900 22,900 53,000 31,200 6,600 319,500 202,900 116,600 0 4,000 37,600 0 15,000 0 0 2,500 20,100 Bureau of Research and Statistics. 19B4. Michigan Employment Security Commission, special release. 21 the former stage are processed further. stage that accounts for This is the dominant of all wood-using mills in the state and is mainly concentrated in the southern lower Michi­ gan. Value added by manufacture in Michigan's wood-processing industries was $1,972 million for 190D. This can be divided into $403 million in lumber and wood products, $404 million in wood furniture and fixtures and $1,165 million in paper and allied products. If other forward and backward economic linkages are tied to the wood products industry the value added to the economy is in excess of $4 billion (James et al., 1982). LAND USE Agriculture Certainly one of the major land-uses in rural Michigan .is agriculture. Table 8 shows that since 1940 total land areas in farms decreased from 18 million acres in 1940 to about H i million acres in 1902. This is a reflection too of a national trend where there has been a tremendous shift of populations from rural to urban settings. Ironically, the average size of farms has increased over the years while the total number of farms and total land area has been decreasing. Corn, soybeans, and alfalfa form the bulk of production yields. drybeans Of all the crops, corn gives the greatest total yields as shown in 22 TABLE 8 NUMBER QF FARMS AND FARM ACREAGE IN MICHIGAN 1940-1982 Year Number of farms Average size of farm (acres) Total land in farms (000 acres) 1940 190,000 97 18,400 I960 118,000 131 15,400 1970 84,000 151 12,700 1980 66,000 173 11,400 1982 65,000 177 11,500 SOURCE: Table 9. Michigan Agricultural Reporting Service, Michigan Agricultural statistics. In 1981 net farm income for the state was 406 million while net income per farm was $7*361. Cash receipts from livestock and livestock products netted $1,319 million in 1982. Mining There is a substantial amount of mining done in Michigan though not as much as in some parts of the country. The state ranks tenth largest amongst all other states. In 1983 the mining sector employed 9,991 people in 512 establishments. Petroleum refining represented 41 .956 of value of output in mining in 1982 (Michigan Department of Natural Resources, 23 TABLE 9 SUMMARY OF SELECTED AGRICULTURAL INFORMATION Year 19B2 1981 1982 1902 SOURCE: Title Number of workers on farms family hired Total ($000,000) gross income production expense net farm income Selected major field crops (yields-bushels) corn soybeans oats wheat Yields per acre of the crops (bushels/acre) corn soybeans oats wheat Number/Value 50.000 45.000 3,321 2,093 4B3 307,380,000 32.240.000 28.350.000 24.600.000 109.0 19.2 63.0 63.0 Michigan Department of Agriculture* Michigan Agri­ cultural Statistics, and Bureau of Economic Analysis, Regional Economics Information. 24 1 9 B2). In the entire state 152,000 barrels of oil per day were produced. Of these, one refinery in Detroit and another in Alma account for 72$ of daily amount of crude refined in the state - both produce 111,000 barrels daily. Table 10 shous value of Michigan mineral production of various types. TABLE 10 VALUE OF MICHIGAN MINERAL PRODUCTION BY PRODUCT: 1982 Mineral ($000) Iron ore Cement Petroleum Sand & gravel Nat. salines Copper Salt Stone Lime Nat. gas Gypsum Clay & shale Peat 333,000 155,400 1,036,277 72,400 195,063 35,926 B 6 ,901 70,910 32,599 460,594 6,913 4,005 5,144 TOTAL 2,249,132 SOURCE: Michigan Department of Natural Re­ sources. 1982. Michigan Mineral Pr o ­ ducers, Lansing, MI. 25 F orestry The State of Michigan is well endowed with forest re­ sources, ranking fifth in the nation with 17.5 million acres of commercial forest land. The State owns 22% of the total with this ownership concentrated in the northern portion of the State. Historically, Michigan's forests have been heavily used and by 1935 were reduced to 19.1 million acres of relatively low quality stands from an original area of 35.5 million acres. Restoration began in 1920's and the current situation is one of surplus for major species. The economic situation in the state in recent years has lead to renewed interest in developing the forest resource to broaden the industrial base and provide employment for Michigan residents. For the purpose of this study, I shall look only at the forest resources in the Lower Michigan area. This includes the Southern Lower Peninsula (SLP) where there is a majority of the state's population and agricultural land. put on land by increasing populations, Pressure urbanization and agri­ culture have gradually eroded the forest base and left large portions of remaining timber in small woodlots of diverse ownership (Gray, Ellefson and Lothner, 1905). Hence the demand for land has led to higher stumpage prices in the Southern Lower Peninsula than elsewhere in the state. The other portion of state considered in this analysis is the Northern Lower Peninsula (NLP). While certainly more heavily 26 forested and less densely populated than the SLP, it has experienced increasing pressure for recreational land uses which has driven up land values and property taxes, frac­ tionalized ownership and subsequently increased stumpage prices in the region. Despite these conditions, however, 41$ of industrial roundwood output comes from the NLP com­ pared to 50$ from the Upper Peninsula. liJhen fuelwood use is considered the relative shares become 40$ and 37$ respec­ tively. Table 11 indicates commercial forest land in the Lourer Michigan region by ownership class. Commercial forests in Northern Lower Michigan cover 6.7 million acres and Southern Lower Michigan has 2.8 million acres. Although until the latter part of the nineteenth century, Michigan forests were predominantly softwood, the current timber inventory is dominated by hardwood species, which account for about 72$ of the total volume. Of the hardwoods, maple species dominate followed by aspen species and red oak. Among softwoods, northern white cedar species dominate (though not commercially useful) followed by balsam fir and pine species. Table 12 shows that timer volume is concentrated in the NLP although SLP has substantial volume of hardwood timber. SUMMARY Hence Lower Michigan is the more industrialized and populous portion of the state in comparison to the Upper 27 TABLE 11 COMMERCIAL FDREST LAND IN LOWER MICHIGAN REGION AND OWNERSHIP CLASS: 1900 Ownership Northern Lower Michigan Southern Lower Michigan (thousand of acres) National forest (<150 659 (13%) 13 1,825 (27%) 149 (650 Other public 53 (<150 60 (256) Forest industry 76 (lit) n .a. 474 (7*) 141 State Corporation (6J£) Farm 1,275 (195E) 1,151 (4750 Other private 2,133 (3250 949 (3910 TOTAL (all ownership) 6,695 SOURCE; Raile, G. K. and W. B. Smith. Statistics. USDA For, Serv. Exp. 5ta. Res. Bull. NC-67. 2,463 1963* Michigan Forest North Central For. 28 TABLE 12 NET TIMBER VOLUME ON COMMERCIAL FOREST LAND BY SPECIES GROUP AND REGION: 1980 Species group Northern Lower Michigan Growing stock Southern Lower Michigan (million cu. ft.) softwood 1,707 172 hardwood 5,118 2,207 6,825 2,459 TOTAL (million bd. ft.) Sawtimber softwood 3, B39 519 hardwood 11,245 7,427 15,082 7,946 TOTAL SOURCE: Raile, G. K. and IV. B. Smith. 1983. Michigan Forest Statistics. USDA For. Serv. North Central For. Exp. Sta. Res. Bull. NC-67, 29 Peninsula. Largest amounts of jobs by economic activities are in the manufacturing sectors* with the automobile industry leading all other sectors. sectors, With respect to wood products the number of wood-using mills is estimated to be around 1*637. About 6056 of these mills belong under the category Df lumber and wood products firms which includes the pallet industry. Regarding land-use, endowed with forest resources. Michigan is well It ranks fifth in the nation with 17.5 million acres of commercial forest land. The Northern Lower Peninsula is more heavily forested and less densely populated than the urbanized Southern Lower Peninsula, CHAPTER III MICHIGAN PALLET INDUSTRY TIMBER RESOURCE BASE FDR EXPANSION DF THE PALLET INDUSTRY Forest land in Michigan covers an area of about IB mil­ lion acres or approximately 50% of the total land area. The state's volume of growing stock on commercial forest land increased from 15.1 to 19*1 billion cubic feet between 1966 and 198D, a 26.49% increase (Spencer, 1963). The volume of softwood increased 34$ compared to 24% of hardwoods. During this period the largest volume of growing stock occur­ red in the Northern Lower Peninsula with 6,8 billion cubic feet followed by Southern Lower Peninsula with 2.5 billion cubic feet (see Table 13). Increases in growing stock have added substantially to the state's sawtimber volume which increased 118% for softwoods and 94% for hardwoods since 1955. Sawtimber is defined as the portion of growing stock which contain at least one 12-foot sawlog or two 8-foot sawlogs. Softwoods must be at least 9" d.b.h. while hardwoods must be at least 11". The increase in sawlog volumes implies a shift in the size class of growing stock in Michigan commercial forest although in 1980 poletimber accounted for 44% of the total stand size classes. 3D 31 TABLE 13 NET VOLUME OF GROWING STOCK ON COMMERCIAL FOREST LAND BY SPECIES GROUP AND AREA, MICHIGAN: 1900 (In thousand cubic feet) Species group All units SOFTWOODS White pines Red pine Jack pine White spruce Black spruce Balsam fir Hemlock Tamarack Northern white cedar Other soft woods TDTAL Northern Lower Peninsula 176,763 452.495 335,318 3B.705 35,972 115,899 64,960 26,795 431,116 28,428 1,707,051 Southern Lower Peni nsula 66,928 48.790 13,278 1,718 --- 185 6,B10 4,010 3,929 25,939 171,595 HARDWOODS Select white oaks Select red oaks Other red oaks Hickory Yellow birch Hard maple Soft maple Beech Ash Balsam poplar Cottonwood Bigtooth aspen Quaking aspen Basswood Yellow-poplar Black walnut Black cherry Butternut Elm Paper birch Other hardwoods TOTAL 200 94,087 278 30,117 256,960 ID,705 5,117,615 309,312 337,906 110,632 103,632 7,627 114,660 398,435 47,446 227,908 10,053 59,906 78,743 76,160 83,569 19,425 26,573 116,348 3,304 58,000 20,122 76,828 2 ,287,549 All species 6,624,666 2 ,459,144 SOURCE: 251,160 721,458 IS O ,1B9 1,838 37,604 692,984 748,355 121,609 251,092 98,0B3 10,261 653,639 630,943 325,173 --- Spencer, John S. 1983. Michigan's Fourth Forest Inventory: Timber Volumes and Projections of Timber Supply. U.S.D.A. Forest Service Res. Bull. NC-72, 32 Currently in Michigan the increase in growing stock is 2.4 times the volume of annual timber removals. For Nothern Lower Peninsula it is 2.5 times while in Southern Lower Peninsula it is 2.4 times as indicated by Table 14. This means that timber harvest can be more than doubled without jeopardizing the long term sustained yield capacity of the state's timber resource. Hence as Table 14 indicates, for major species there is a substanti al gap between net annual growth and removals which implies existence of a large surplus of usable wood. These figures must be interpreted with care, it is not the existence but the availability, however, as location and concentration of timber that will be important in development plans for the pallet industry or other forest products industry. Because of varying management objectives for public and private fo rests and the realities of harvesting and transportation of t imber, the entire resource is not available for timber utiliza ti o n . Ownership of commercial forest land in Michigan varies considerably by area with the Upper Peninsula Lower Peninsula (UP) and Northern (NLP) having more public land than the Southern Lower Peninsula (SLP) where more private land prevails. Land ownership patterns tend to influence the size of ounership, productivity and use of timber resources. □wnership of forest land in the UP is dominated by the forest industry, the state and national forest land. The NLP area contains more state land that the UP but substantially less forest 33 TABLE 14 NET ANNUAL GROWTH AND REMOVALS OF GROWING STOCK ON COMMERCIAL FOREST LAND BY SPECIES GROUP AND AREA, NORTHERN (N L P ) AND SOUTHERN (SLP) LOWER MICHIGAN: 1900 (In thousand cubic feet) NLP Species group SOFTWOODS White pine Red pine Jack pine White spruce Black spruce Balsam fir Hemlock Tamarack Northern white cedar Other softwoods TOTAL SLP NLP Net annual grouth/Annual timber removal 872 3,105 7,999 07 B0 042 105 438 1,633 125 1,326 74 10 7,359 32,209 14,508 2,483 1,081 1,591 1,360 -1,568 14,711 2,584 76,390 2,766 3,181 586 507 —-6 104 39 247 1,256 0,692 5,845 21,2B1 4,565 50 799 23,690 42,752 1,37B 13,059 1,226 322 28,441 20,759 9,876 --------- --- 1,731 2,579 41B 09,275 626 110 1, B81 35,303 104,524 37,272 HARDWOODS Select white oaks Select red oaks Other red oaks Hickory Yellow birch Hard maple Soft maple Beech Ash Balsam poplar Cotton wood Bigtooth aspen Quaking aspen Basswood Yellow poplar Black walnut Black cherry Butternut Elm Paper birch Other hardwoods TOTAL 3 5,076 7 -2,502 9,155 267 106,649 6,892 9,190 3,023 2,821 1B6 3,162 IB,397 605 11,900 245 2,157 3,630 4,906 2, 092 4D0 793 6,056 92 -3,351 713 2,795 79,112 All species 263,047 87,804 --- ----- 15,249 26 104 83 221 1, 969 3,006 11,720 3, 541 15 40 5,310 8,639 1,539 2,090 784 454 25,206 20,023 1,96D 5,938 7,311 2,532 632 14 3,233 5,079 756 2,664 9 B37 8B3 614 1,179 --- APPARENT SURPLUS (NLP + SLP) -- [GROWTH - REMOVALS] SOURCE: SLP 205 ----- = 209,055 Spencer, John S. 1983, Michigan's Fourth Forest Inventory: Timber Volumes and Projections of Timber Supply. U.S.O.A. Forest Service. 34 industry land and mare farmer and individual land. The SLP area is dominated by farmer owned and private land (refer to Table 11). Because of the heavy use of the s t ate’s forests and fire damage of the early 1920's many of M i c higan’s forests are in stands from 41 to BD years of age. in the state are 5D years or younger. Half the stands Of the major species the maple-birch type* a long lived species, contains 23# in stands over 90 years of age, the suggested rotation age. Aspen, a shorter lived species, which deteriorates at about 40-60 years, of age. contains about IB# of stands exceeding 60 years Of the softwoods a substantial area are in older stands which are more susceptible to disease, fire. insects and These factors and others result in a high mortality rate of 20# amongst timber species in the state. In conclusion, while analysis of the timber resource base in the study region suggests that there is a substantial surplus of many species suitable for utilization by the pallet industry, the actual availability of this timber is what will be important to industrial development. Management practices of landowners and ownership objectives of these parties will affect how much of the s tate’s timber resource is available for different uses, including pallet manufacture, now and in the future. Inventory and silvicultural condition of the forest resource base in the state are summarized in Table IS. TABLE 15 REMOVALS, NET ANNUAL GROWTH AND INUENTORY DF GROWING STOCK ON COMMERCIAL FOREST LAND, MICHIGAN: 1980 (in millions cubic Feet) Growth Removals Inventory All spec. Soft­ woods Hard­ woods All spec. Soft­ woods Hard­ woods All spec. Soft­ woods Hard­ woods NLP 104.5 15.2 89.3 263.0 76.4 186.6 6,B24.6 1,707.0 5,117.6 SLP 37.2 1.9 35.3 87.5 8.4 79.1 2,459.4 171.6 2,287.8 REGION 141.7 17.1 124.6 350,5 84.8 265.7 1 9,284.0 1,078.5 7,405.4 SOURCE: Spencer, John S. 1903, Micrth inventory: Timber volumes and projections of timber supply. U.S.D.A., Forest Service, Res. Bull. NC-60. 36 NATURE DF THE PALLET INDUSTRY The pallet industry is one of the newest among the second­ ary wood processing industries. An extensive pallet industry has become established as a result of the rapidly expanding use of mechanical handling equipment. Unitized loads of industrial and agricultural products are handled by a variety of mechanical handling equipment such as lift trucks, conveyors, 1971). slings, racks, and booms (Forest Products Laboratory, Pallets provide one of the foundations upon which to assemble these loads. A PALLET - AN INDUSTRIAL C0WV)00ITV A pallet is an industrial good destined for use in pro­ ducing other goods and services. tially a packaging device, The wooden pallet is essen­ a generic name for platforms usually made of wood and primarily used as a base for unit loads of material. According to National Wooden Pallet and Con­ tainer Association, wooden pallets fall into three major categories, as follows: i) Permanent, reusable pallets, cost per use. which provide the lowest They can be employed by captive use by one company or in cooperative pallet sharing pools. About 60$ of wooden pallets comprise of this type. ii) Expendible shipping pallets, used do a single trip to carry a unit load from a manufacturing plant to 37 a delivery point. About 30$ of wooden pallets are expendible type, iii) Special purpose pallets, including lightweight re­ usable pallets for handling bulky materials of low density, drum or keg pallets and pallets that permit entry of forks over and under the deckboards. This type of pallets accounts for about 10$ of pallets. Some of the advantages of using pallets are: a) they form an efficient package that is compatible with land, sea, and air carriers, b) they move easily over conveyors and into automatic palletizers, c) their unique nature makes them suitable for rapid movement by a variety of mechanical equipment such as conventional forklifts, hand pallet jacks, overhead cranes and slings, and d) their production from low grade lumber make them economically feasible. Ninety percent of all pallets sold are wooden stick built units. of plastic, molded wood. The remaining 10$ of the market is comprised aluminium, steel, foam, corrugated medium and The most common pallet sizes are AS” x A O 1', A 2 ,f x 42 n and 48" x 4 8 u because of their easy use across railroad freight cars and the average truck body. Table IB shows that 43,8$ of pallets sold in the market place in­ cludes a variety of sizes, each representing under 1$ of total productions. This accounts for the fact that there are literally thousands of pallet size classifications and designs. 30 TABLE IB THE TEN MOST COMMON PALLET SIZES USED IN U.S.: Sizes 48" 42" 40" 48" 48" 40" 36" 36" 48" 44" All % of total production x 40" x 42" x 48" x 48" x 42" x 40" x 48" x 36" x 36" x 44" others 28.5 5.4 4.8 4.2 3.2 2.9 2.4 2.2 1.3 1.3 43.8 TOTAL SOURCE: 1901 100.0 National Llooden Pallet and Container Association. 1901. PALLET MANUFACTURE IN MICHIGAN In all of Michigan in 1900, there were 190 pallet plants. Of these only 15 firms operate in the Upper Peninsula, remaining 185 firms are in the Lower Michigan area, of the study region (Heinen et al., 1903). the the focus Hence the produc­ tion locations follow trends similar to most industries in the state as indicated in earlier sections. In 19S2 the total value of shipments for the state was $71.8 million, as shown in Table 17. Approximately 40# of value of ship­ ments is a result of value added by manufacture* In terms of number of establishments, Michigan with 190 ranks second nationwide to Ohio (21B establishments). 39 TABLE 17 STATISTICS OF WOOD PALLETS AND SKIDS SECTOR FOR MICHIGAN: 19B2 Title Value/number All employees number (100Dfs) annual payroll (millions) 1.3 14.2 Production workers number (1000Ts) hours (millions) wages (millions) 1.1 1.9 10.4 Value added by manufacture (millions $) 2B.9 Costs of materials (millions $) 41.B Value of Shipments (millions $) 71.8 New capital expenditure (millions) SOURCE: U.S. Department of Commerce, 182, Census: Census of Manufacturing. 1.5 Bureau of the AO Michigan is follaued by Pennsylvania with 1B5 establishments. Evidently one can observe that all those states are tradi­ tionally heavy industrial states that manufacture heavy machinery, agricultural equipment, automobiles, steel and so forth (Bureau of the Census, 1982). According to the 1983 (second quarter) Bureau of Labor Statistics, total employment in the pallet industry (SIC 2AA8) in Michigan was 1AAB with total wages of $3.83 million. Table 18 indi­ cates number of establishments by employment-size classes. As can be seen, 82% of firms employed 19 or fewer employees in 1982. TABLE 18 NUMBER OF ESTABLISHMENTS BY EMPLOYMENT-SIZE CLASS Average number of employees 1 to A A0 5 - 9 25 10 - 19 21 20 - A9 IB 50 - 99 0 2A9 1 100 - SOURCE: ff of establishment U.S. Department of Commerce. 1982. Census: Census of Manufacturing. Bureau of the 41 VERTICAL INTEGRATION MODEL OF ft PALLET FIRM Standard Pallet Manufacturinq of a Pallet Firm Process and Technology The purpose of drawing a pallet firm's technological and organizational structure is to realize how resources, machines, materials, and human capital are mobilized for economical production. The diagram of a plant's structure traces how a unit of pallet product starts out as a tree species in a forest ecosystem and is eventually transformed through use of capital and labor into the ultimate product that a consumer buys in the market. In short, this is a vertical integration model of a typical firm used in research topic. to the Forest Products Laboratory According this (1971), such a pallet plant would have a maximum capacity not exceeding 500 units per B-hour day. As an estimate this requires a lumber supply of between 10,000 to 15,000 board feet per day. be 16-1B people. The employment size for the firm might Occupational profile and skills can be divided as follows: 1 Supervisor and Repairman 4 Operators for cutup and lumber breakdown operation 3 Cutup and residue off bearers 6-8 Nailers 1 Nailing off-bearer 1 Lift truck operator 42 Raw material that is fed into the production process is in the form of logs* cut-to-size lumber or roundwood that is further reduced to size by sawmill or planer. The flow process diagram permits visualization of the movement of material on a floor plan. It indicates the production process materials go through to manufacture pallets from logs. All the equipment in the operation are connected by a system of conveyors which makes for an efficient opera­ tion. Unskilled or semi-skilled labor can be used to operate the assembly. There is ample space around each machine to allow for operation of equipment* convenience of workers, installation of handling devices, maintenance, and repair needs. Also storage areas are provided where finished pro­ ducts can be stored for two reasons: in case of a plant breakdown, surpluses. (a) emergency needs and (b) inventory for temporary In the plant a certain degree of flexibility is shown in the flow diagram: some deckboards are chamfered and then directed to the assembly to be used as bottom lead­ ing edge deckboards or stringers that can be diverted to the notching operation for construction of pallets. Pallet assembly techniques have vastly improved in recent years. Wailing technology has advanced from the original hand nailing method of assembly in the 1950s to present pneumatic guns, nailers and stapling machines. This equipment is conducive for short production runs, spe­ cial pallet designs, pallet repairs and salvage operations. 43 Plant layout takes into account plans for future expan­ sions or means of increasing production to take advantage of increased sales and market growth. Provision is also given to the manufacture of other items that make use of all or most of the same rsu materials and machinery. related products as car blocking and bracing* ture squares, Such dunnage, furni­ cut stock, box, and crate material can also be manufactured. At a Lansing pallet firm the author toured, the plant produced box and crate materials in addition to manufacture of pallets. □ne of the by-products in pallet manufacture is the residue. A typical pallet manufacturing plant that processes 15,000 board feet of rough lumber daily mill generate 18 to 24 tons of residue (Eichler, 1976). residue, In disposing of efforts should be made to locate or develop uses to promote maximum whole tree utilization. Pallet plants located in areas where dairy farms are located can usually dispose of green sawdust and shavings to farmers as animal bedding. Also systems are in operation that use this residue as fuel, especially those manufacturers that operate their own dry kilns and need to stay on a year-around basis. The economies of scale concept enters into consideration as only plants that require large amounts of steam will find this method economically sound. Sometimes efficient wood residue utilization results in increased profit revenue for a wooden pallet manufacturer. However rules and regula­ tions regarding environmental pollution and solid waste 44 disposal impacts utilization level of residue and hence economic feasibility of its use. Some of the terms defining different equipment and production processes are too obvious to illustrate separately. However some of the technical terms unique to the pallet manufacturing process are defined below in the diagrams. Also, principal parts of a wooden pallet are included in the definitions. Figure 4 shows the flow process chart involved in the manufacture of pallets. Figure 5 illustrates the key features of a wooden pallet as a product (National Wooden Pallet and Container Association, 1982), DECKBOARDS - These are structural members that make up the faces of a pallet. deckboards* load. There are referred to as top and bottom The top deck is the surface that carries the The bottom deck is the surface that helps distribute the load when the pallet is at rest. CHAMFERER - This is a machine that produces chamfer. This is a beveled edge on the top side of bottom deckboards for purpose of easing entry and exit of forks and pallet truck load wheels. Chamfers are also required on the underside of top deckboards or reversible pallets. CUTOFF - This is a remote control trim and cutoff saw designed to cut rough lumber cants to exact length. RIPSAW - This is a single or double roughing planer used for sizing cants to proper thickness. RESIDUE OR LUMBER STORAGE * Cutto Size Lumber Cutoff Drill Notcher Forest Wood Resource — ? Supply Base Region Chamferer Nailing and Assembly Area Round Saw­ mill Finished . Pallets ^ [Outputs] Covered Storage and Future Expansion WOOD SUPPLY DIFFERENT INPUTS TECHNOLOGY AND PRODUCTION PROCESSES FIGURE 4 - MANUFACTURING PROCESS OF A PALLET FIRM SOURCE: ] Adapted from U.S. Forest Product Laboratory, 1971. Pallet Plank Layout, p. 9B. 46 fia t* S,f'"ser 0 „ , 8 n top deck botras ”**T S 0P 47 RESAW - This is a single or preferably double arbor multiple gang machine that is commonly used to process cants into pallet parts. PLANER - If a pallet manufacturer is involved in logging or buying stumpage the planer reduces small logs into cants for processing in a circular gang resaw or band resau, NAILING - Next to the material used, fasteners or nails (gun or hand nails) constitute the most important element of wooden pallet construction. The acceptable number of nails at each joint or bearing points varies with the width of the deck­ boards * STRINGERS - These are wood runners-structural members to which deckboards are fastened. NQTCHER - This is a machine that produces notched stringers. A notched stringer is a stringer that has openings cut out for insertion and withdrawal of pallet lifting equipment, CONSTRAINTS THAT AFFECT PALLET PRODUCTION AND CONSUMPTION Timber Species Though normally pallet parts originate from the lower grades of either hardwood or softwood species, of timber species cannot be underestimated. are made from hardwoods, softwoods (or both), Each source offers certain advantages. significance Wooden pallets and plywood. Strength and quality of a particular timber species is closely related to its density and weight. The moisture content of wood that goes 40 into pallets is significant. Many pallets are built from green or partially green lumber because of lower cost. Mois­ ture content also varies with the type of tree species. Usually dense hardwoods are used at high moisture contents to facilitate nailing. On the other hand, the lower density hardwoods and moist softwoods are easily nailed regardless of moisture content. The average weight of some commercial species at 20 percent moisture content are listed in Table 19. The table represents the moisture condition that might be reached by lumber in stickered outdoor piles after drying from 3 months to a year (Forest Products Laboratory, 1971). This table also typifies most species of wood used in the production of pallets. The softest textured softwoods fall into Class A, the intermediate species in Class B and the densest hardwoods in Class C. The lumber in any pallet should not contain any defects that might weaken the part or hinder proper fastening or nailing (i.e . , there should be no knots in nailing areas), in the middle portions of the deckboards (where the greatest bending stresses are imposed), stringers or blocks. and in certain portions of Nevertheless, not all lumber imperfec­ tions affect the structural strength of pallet parts, and therefore are acceptable within limitations. season checks, edges), These include pinworm holes, mineral streaks, wanes (barky and stains (Sardos, 1902). TABLE 19 CLASSES AND WEIGHTS DF WOOD GROUPS USED IN PALLET CONSTRUCTION Species Weight (lb.) per 1,000 board feet at 20% moisture content Species Weight (lb.) per 1,000 board feet at 20% moisture content CLASS A Aspen (popple) Basswood Buckeye Cedar Cottonuood Fir, subalpine Fir, balsam Fir, noble 2,250 2,060 2,180 2,250 2,370 2,000 2,180 2 ,370 Fir, white Hemlock, eastern Pine (except southern) Redwood Spruce Willow 2,370 2,470 2,470 3,110 2,370 2,180 CLASS B Ash (except white) Baldcypress Butternut Douglas-fir Elm, soft Gum, sweet Hemlock, western Larch, western 3,100 2,720 2,310 2,940 3,000 3,000 2,720 3,120 Magnolia Maple, soft Pine, southern Sycamore Tamarack Tupelo Yellow-poplar 3,000 2, B70 3,290 3,000 3,170 3,ODD 2 ,590 CLASS C Ash, white Beech Birch, yellow Elm, rock Hackberry SOURCE: 3,620 3,680 3,620 3,750 3, 1BD Hickory Maple, hard Oak Pecan U.S. Forest Products Laboratory. 1971. 4,240 3,680 3,680 3,960 50 Competition As emphasized earlier, are mood pallets. ninety percent of all pallets The remaining ten percent are metalic, plastic, corrugated medium, molded wood pallets, and combina­ tion of materials (Wallin, 1985). Hence this indicates exis­ tence of elements of substitutability and complimentarity of the products to satisfy a given demand in the market. Amongst the major competition to the wooden pallet is the pallet made from molded uiood. In the molded wood market the Inca pallet dominates. This is a pallet molded from wood fiber particleboard. The process has been used in Europe since the early 1970's and pallets are now produced by this process at the Litco plant in Dover, Ohio. United States. It is the only plant of its kind in the In comparison to wooden pallets, advantages of Inca pallets are: a) it is 60/! lighter than a comparable wood pallet, b) it is reusable, c) it is stackable in storage and transportation, and d) it is an identifiable specialized product which facilitates effective marketing strategy (hetero­ geneity). For the pallet industry in Michigan, the Inca pallet offers further competition due to its strategic plant location in Ohio. The location is ideal for serving the Eastern and Midwestern industrial centres and drawing on plentiful wood supplies of the surrounding region, especially Michigan, which is well endowed with surplus timber. The Inca pallet market can stretch to a 500-mile radius and still 51 be cost competitive in contrast to the narrow distance of wood pallets (1D0-150 miles). Also another innovation is the PALLETECH technology developed by the Institute of lilood Research at Michigan Tech­ nological University (MTU). This is a patented process for the manufacture of industrial grade* molded wood* materials handling pallets. According to Nies (1985)* pallet would offer; a) superior strength characteristics* b) marketable qualities of uniformity, reusability, the resultant 3) design flexibility, c) nestability, d) and f) neat appearance. In addition it can be designed and manufactured to accommo­ date almost all material handling situations. Although the PALLETECH concept would offer competition to the Inca molded pallet, they nevertheless provide revolutionary change in materials handling approach and would cut into the traditional wooden pallet market. They threaten to offer the customer greater price stability and quality consistency in comparison to the traditional wood pallets. Demand A pallet is an industrial commodity in that it is a good that derives its demand from the demands for final con­ sumer goods. During boom times in the business cycle buyers often buy pallets to accommodate inventory buildup as well as actual consumer demand. Dn the other hand during reces­ sion or slow economic growth periods, pallet buying is low 52 due to factors such as inventory reductions, activated use of reusable pallets and high costs of warehousing and trans­ portation (Brindley, 19B2). In essence pallet demand fluctu­ ates more than typical consumer demand, which is a common characteristic of industrial goods. In economic theory one can postulate that the overall industry-wide demand for pal­ lets is inelastic. But at the same time, in regional or local markets such as cities in Michigan, pallet manufacturers would find that their demand curves are elastic. This means that a significant increase in pallet prices would result in large decrease in demand at a particular site. Schuller and Wallin (1903) conducted several studies using economic models to analyze U.S. pallet markets. With respect to our current analysis it is irrelevant to get into the mechanics of their models but it suffices to highlight several key findings with respect to the pallet demand functions. They found that the key variables that affect pallet demand nation­ wide (and could be applied to Michigan in general terms) were pallet price, the industrial and food production index and the relative price of pallets to wage rates for laborers in materials handling. Since our study region is heavily dependent on the automotive and food industries, one can assume that production output (now and future) of these sec­ tors form some of the key predictors for pallet demand and use according to findings from previous studies. 53 Hole pf Price in the Pallet Industry The significance of price for a pallet depends upon the buyer's understanding of the nature of the product. A pallet* which is mostly a homogenous product tends to be very competitive. manufacturers* Because of low differentiation between the pallet buyer has little loyalty to speci­ fic pallet producers. being generic. Buyers see a homogenous product as With relative abundance of surplus timber and heavy industrial activity in the state, there is bound to be stiff competition between firms to survive and operate profitably. Dominance by any one firm in any market is deter­ mined by its economic advantage of location, production efficiency. resources and The one crucial issue that affects all pallet business operators is the wood price. Pallet selling price is the single most important market factor in the pallet industry. Price based on intended use and the result of competitive bidding is the criteria for most pallet purchases. The last section emphasized how sensitive pallet prices are to the economic health of the economy. According to Nies (1905), current delivered unit pallet prices fall into three ranges: I) $2.50 to $4.50 For a firm this market is characterized by order of 10 to 500 units mostly of expendible sizes from 2 0 ” x 28” to 3B” x 4 0 ” . Small pallet firms tend to dominate this portion of market and face fierce price competition. 54 II) $5.00 to $7.00 This is typified by orders of 1000 to 60,000 units, considerably fewer sizes. This market has a signifi­ cant number of large producers, face stiff competi­ tion and undetermined mixture of expendible and reusable pallets. Part of the reason prices may be higher is because they are reusable. Ill) $7.00 to $12.00 This market is comprised of federal agencies buying for the U.S. Government and private industry ordering special use pallets. In Michigan two agencies that would be major consumers of pallets are the Defense agency and Federal Prison System. PALLET MARKET STRUCTURE Unique to the pallet industry is that buyers rather than sellers dictate nature of product and terms of trade in the market place. They control the market in terms of price and delivery schedules. differentiate products. Buyers rather than sellers This condition results in the pro­ liferation of different pallet types and sizes found in the market place. Relative to the economic size of the seller firm, the pallet buyer or customer is usually very large and diversified. Hence the purchaser establishes the type of product required for their business venture. 55 Since pallets are leu value products, manufacturers tend to locate 100 to 150 miles of industrialized centers. In Michigan some of these centers are Detroit, Flint, Lansing, Grand Rapids, and Kalamazoo. Since quoted pallet prices include shipping and handling costs, manufacturers close to the market are at a competitive advantage when seeking neu business. As distance diminishes from the customer and producer increases, the advantage too diminishes. This fur­ ther supports the effort in this study to cover only the Lower Michigan regions which includes the metropolis and industrial centers. For the most part, of pallets such as the auto industry, the major consumers food industry and govern­ ment are located in the southern portion of the state. COMPARATIVE ADVANTAGE No discussion of the Michigan pallet industry would be complete without some reference to the State's comparative advantage. Looking at the number and sizes of pallet plants in the state, it is apparent that it is a highly volatile and competitive industry. The firms' economic growth rates should reflect the ability of the pallet industry in the state to compete effectively with other states in the Midwest for raw materials and market share. The relatively simplis­ tic nature of a pallet makes it fairly easy for an operator to enter the industry on a small scale (easy entry and exit). Other inducements are; relatively simple production technology 56 minimal startup capital, cheap unskilled labor, and low grade raw materials. The competitive nature (and hence its comparative advan­ tage) of the industry makes it imperative that a pallet manu­ facturer keep abreast of technological changes, raw material supplies and market locations. Incidentally Michigan happens to have abundance of each of these resources due to economies of scale (concentrated industrial centers), surplus timber resources and large markets such as Detroit, Flint, Lansing and Kalamazoo. In summary, this means that the pallet industry in the state would offer industrial customers greater price stability. Pallet price is the one key variable that determines the competitive edge for the state's pallet industry. This price should not only reflect the consequences of cost dynamics involving intra-firm competition between wood pallets and other pallet forms but also inter-industry competition with other wood products sectors in the state. The latter is a result of inter-industry competition for timber supplies. As an example of competition, industries such as pulp and paper and wood furniture might compete successfully against the pallet industry. Some of their advantages might be gene­ rally lower transportation rates and ability to outbid small competitors such as pallet firms for stumpage values with still enough profit margin left to operate efficiently. Nevertheless, it still appears that with manipulation of forest resources and ample technological advances in the 57 state, resource owners and business operators could exert greater influence on pallet costs, hence pallet prices to the advantage of pallet industry. CHAPTER IV MODEL DEVELOPMENT GOAL OF THE FIRM-LOCATION MODEL The essential goal of this model is to assess spatial distribution of existing firms and wood supply regions in order to determine potential site(s) plant(s) in the region. for locating the next The computer .model that is used in this study is a modification of Hoover's Industrial Locaw tion Model Number 6 . RESEARCH CONTEXT Since the objective of the study is to assess the sus­ tainability of current pallet industry’s operation need and for expansionf this model attempts to tell where there is room for the expansion of pallet industry and at which produc­ tion point it should be established. The solution should indicate which "space” or "distributional channel" is avail­ able for an additional plant and where it should be established given supply and demand factors. Pallets are not only low-value Hoover, Edgar M. 1967. Some programmed models of industry location. Land Econ. 43(3): 303-311. 5B 59 products but their economic life-cycle or duration of their utility to consumers is short-lived. Hence the product is not only sensitive to market conditions, but more significant­ ly, to variability in production costs. Apart from labor costs, major components of costs involve transportation costs of materials between supply sources and market locations. This explains the emphasis on transportation costs in the location model. MATHEMATICAL FORMULATION OF fl FIRM-LOCATION MODEL Since ue are dealing with a natural resource product which involves both physical and biological variables, Hoover's model is modified to encompass these elements. Hence firm- location model emphasizes spatial and temporal interrelation­ ships between economic activities. tions: Also note three assump­ a) a standard unit of analysis (of the product) is a pallet unit which is equivalent to 19 board feet per pallet; b) stumpage supply or raw material is measured in board feet; c) costs or prices are in 1984 dollars ($) terms. Notation The following notation will be used in the mathematical structure for the location model: y s timber yield projection by years--where (y=1980, 1905, 1995......... V) 60 t = production time periods 30, where {t = ID, 20, ■ ■ i , T) k = supply region---- where (k = 1, 2, 3, . . . , K) 1 = production location . * , where (l = 1, 2, 3, L) m = market areas op loci where (m = 1, 2, 3, . . M) i = timber type or raw material 3, . . . where (i = 1, □, I) j = product good manufactured where (j = V = raw material/product ratio in tons MC = marginal cost of pallet production MR = marginal revenue of a pallet unit RE = volume of forest resource base in board feet FDj = final demand of product j in standardized pallet units {19 b d .ft./pallet) X. = wood pallet j sold or consumed in pallet units J = inventory of product j at plant location 1 TC . = total transfer costs in delivering product j to jm market m Tikt = assembly cost--access to raw materials from sup­ ply region k to plant location in time period t flikt = cost of raw materials i including harvest cost at supply region k in time period t P lt = ProductiQn cost (capital + labor costs including normal profits) at plant location 1 in time period t 61 Ti t = distribution cost--access to market places from lmt production location 1 to market m in time period t MF J^ = manufacturing cost of product j at plant location 1 Eikt = total stumpage supply of i at each supply region k in time period t in board feet Fjit = total output j at each production points 1 in time period t in pallet units D . = demand price of the product j at a market m jm = capacity of each plant or firm at location 1 in tons Clif^ = capacity constraint cost (initial limit) in tons at location 1 C1 = maximum costs beyond initial capacity limit at location 1 The purpose of using this model is to ascertain and determine the appropriate "space" that offers the optimal location for the next plant in the region. In order to arrive at an answer* the solution procedure will have to process the following mathematical operations. In each instance the underlying assumptions are described because these are significant if one is to understand the rationale behind the model, as well as the limitations of the results of this study. 62 PRODUCTION EQUILIBRIUM The formula set below assumes that the final demand levels can be satisfied from the production levels and the inventory stock: Further to guarantee this result* an inventory is assumed to be kept for two purposes: (a) to mitigate unforseen short­ ages* and (b) for storage for temporary or slack demand. Minimal imports into Michigan are assumed. In other words* supply is equated to demand under ideal conditions. Given the nature of pallet industry in Michigan with its numerous firms and varying scales of business operations {establish­ ments with less than ID employees to those with hundreds of employees), the industry displays characteristics of pure competition between firms to survive. Also this model deals with one homogenous product* the wood pallet. In this respect the model considers competitive relationships between firms in the same industry. Other criteria for perfect competition also apply but to a lesser extent. Hence the firms tend to be competitive in an effort to maximize returns to their investments. Their profit maximization condition would be given by: MR = MC (la) A firm will produce units of output up to the point at which the increment to the revenue provided by the last unit sold (MR) is precisely equal to the cost of producing 63 it. Similar results apply for the hiring of inputs or for any other decision that firms must make* FOREST RESOURCE BASE Assumption of the formula below is that the volume of raw materials (inputs) available in any supply region is affected by the physical attributes of the forest resource base and other social constraints such as multiple use legis­ lation . E ikt < RE <2 > Given these constraints only certain specified timbersheds have surplus wood that serve as sources for roundwood or raw materials. TRANSFER COSTS Transfer costs involves the shipment costs from raw material supply areas to production plants, to the markets. then ultimately Distances between raw materials areas, pro­ duction locations and markets are measured via highways, assuming shortest routes between towns. The formula below indicates this situation: S p lkt k + S Z T lnt m = TC (3) Hence transport costs would reflect rates charged by respec­ tive modes utilized. Since firms are assumed to maximize profits and materials and products are completely standardized; 64 each production location gets its materials from sources that can supply them cheapest and each market is supplied by the production center that can deliver the product at lowest cost. MANUFACTURING CD5TS The equation below sums costs of raw materials and pro­ duction processes. u # 2ikt - p it ■ MF <4 > The material/product ratio represents the amount of input that goes into making one unit of output. Manufacturing costs of the product include both captial and labor costs. Supplies of raw materials as well as products depend on their total costs. Major costs involved in producing and delivering a unit of the product to a consumer, apart from transporta­ tion costs, species, are harvest costs, stumpage prices of various capital costs, and labor costs. such as labor, Variable costs transportation, and raw material costs are important in the long run (say ID to 4D years) from the busi­ ness point of view because they help guide business decisions such as investments, basis. rate of return, etc. on a day to day Supply functions are affected by such factors as pallet price, hardwood lumber prices (only hardwood used in pallet manufacture), ing labor costs. housing starts and pallet manufactur­ 65 PALLET PRICE For each space the total costs are compared with the product demand price at the specific market place. The formu­ la below equates production costs to demand price of a unit output: T C Jm + "F jl * °jm (5) The price of pallets is a reflection of the demand function of the product in both the regional and national economies. Its demand amongst other variables is affected by pallet price, industrial and food production indexes, and so forth. substitutes, The state of the automotive economy (such as sales of automobiles in Michigan and the nature of the business cycle in the national economy (e.g. rapid aggregate economic growth) affects demand for pallets. Pallets are used mostly as a packaging device for material/product hand­ ling and shipping. Also, the average production costs which includes labor costs have to be less than the product price so as to leave enough margin for at least normal profit. Normal profit is assured by maintaining positive price-cost differential, the margin not falling below zero at any given time. PLANT CAPACITY AND CONSTRAINT COSTS Plant capacity imposes a constraint on the volume of output or product processed. Hence only a certain limit of volume can be produced at any given period. The formula 66 below assumes that a production plant cannot produce any desired output without being limited by capacity constraint: Xj < (tons) (6) The capacity constraint could also be translated into economic costs of production by equating capacity constraint output to equivalent production costs. Hence the capacity constraint limit could also appear as cost constraint limit: Xj < CW^ (cost in dollars) (6a) However the above formula (6 or 6a) can still be modified to guarantee that the initial capacity constraint is not absolute. Capacity at a production location can be expanded but only at a cost. The costs of expansion (investment) are assumed to be uniform per unit of added output beyond the initial capacity (6 or 6a). Hence for each production point* there is a twD-step cost function; one level of costs prevails up to the inital capacity limit and higher cost thereafter. X. > CU1 < C rUlx (6b) To simplify the problem and guard against excessive data needs, an assumption is made in this model, that either formu­ la (6a) or (6b) shall yield the same results when the initial capacity is considered non-expansible by setting costs for any output beyond that limit at a prohibitive level when entering data inputs. 67 DESCRIPTION OF THE COMPUTER PROGRAM Though location model is the basis of this computer program, it has its variations and modifications from the Location model (see comparisons in the next section). This l model has its inputs as timber or lumber and’ its outputs being pallets. It starts out by introducing into the system total stumpage supplies of available timber for pallet produc­ tion in louier Michigan, These supplies are then allocated to the selected wood supply regions. From there, timber is shipped to plant locations for processing into pallets. Finally pallets are shipped to the markets for consumption. Hence, technically one can say that if there are (k) supply regions, (1) production locations, and (m) market places in all, there would be (klm) different "spaces" (locations) which can be followed (from wood supply areas to plant loca­ tions to market areas) in producing and marketing a unit of output (pallet). The program starts by examining all these spaces in turn and for each one computes and compares the results according to economic criteria. The two estab­ lished economic criteria ensure that the "spaces" or loca­ tions chosen by the model are suitable for either expansion of pallet firm or introduction of a new pallet firm. These criteria are: First, from each space the total costs of manufacturing and delivering a unit of pallet are compared with demand price 66 in market (m) when sales in that unit are just one unit. The formula is shown below: V Where: » A. k + TC + MC = D, jm V = raw material/product ratio fl^ = cost of raw material (harvest cost) = assembly and distribution costs--access TC to raw materials plus movement to market areas NC = manufacturing/production cast including labor cost Djm Secondly* = demand price of product at market there should be guarantee that even if there is positive demand price over cost* there would be no expansion of the pallet industry if current capacities of firms are not fully utilized. This forms the key decision variable that determines possibilities of expansion or building a new pallet establishment. Hence some "spaces" or locations based on above criteria are eliminated from any consideration in the course of the program. Remaining spaces would show positive results. This means that another firm(s) is needed to fulfill extra demand or alternatively, expansion of the pallet industry is possible. In this research study this is the space that indicates the "optimum" location zone where the next pallet firm(s) could be established. This would be a suitable place to build a plant in order to maximize returns on investment. 69 A key variable in the firm-location model is the capa­ city constraint of pallet firms uhich determine whether under­ utilization is the problem. Alternatively the solution could warrant building more plants to satisfy excess demand for pallets • CONTRASTS BETWEEN THE LOCATION MODEL AND THE COMPUTER PROGRAM In five key respects, the computer program. the location model differs from These are: fa) Unit of analysis In the location model one unit of input is equal to a production output of a typical pallet plant processing 1.3 million board feet of lumber or 6B,D00 board feet of pallet in a given area. Derivation of production, distribu­ tion, and consumption figures are reduced to the same common denominator. Whereas in the location computer program, is on per ton-hourly basis. tribution, unit of analysis Corresponding production, dis­ and consumption figures are reduced to the same basis accordingly. (b) Inputs Though the number of established markets are the same in both the location model and the computer program, the number of supply regions and production locations are dif­ ferent. The number of supply regions and production locations are twelve and nine respectively in the location model. 70 With regards to the computer program, the number of supply regions and production locations are both nine. This was because the computer program uas structured in such a way that supply and production points had to be equal to or less than ten. (c) Production Cost The location model has constant processing and manufac­ turing cost depending on a given location. However, the location program has constant processing costs regardless of location but variable processing costs depending on loca­ tion. (d) Capacity Assessment Concerning the location model, initial capacity was considered non-expansible by setting costs for any output beyond that limit at a prohibitive level when entering data inputs. Also capacity values are only set at production plant or location. The computer program handles this problem in a different manner by considering initial capacity expansible but making second capacity limit non-expansible. It accomplishes this by creating at each source and production point a distinctive two-step cost function. One cost level prevails up to the initial capacity limit and a higher cost thereafter. In addition these capacity values are set at both raw material regions and production plants. 71 (e) Transport Cost Transport cost formula for the location model is general in that it can be adapted to most data sets: ^klt + ^lmt Where: Tkit = Assembly cost from raw material source k to production plant 1 at time t. ^lmt = Distribution cost from production plan 1 to market m at time t. Since this study involves pallet industry* transport cost formula for the computer program has two shipping stages; (l) rau material as timber* and (2) lumber ready for pallet manufacture in a vertically integrated industrial structure: (1) lumber shipping: $ Cord = 9.35 + $ MBF = 10.25 + 0.0B (miles) (2) lumber shipping: 0.14 (miles) IMPUT-DUTPUT MODEL One of the most uidely used tools for making estimates of economic contribution at regional levels is the inputoutput (I/O) model. In an effort to better understand the economic role of forest based industries in Michigan an inputoutput model provides a tool of analysis for impact analysis. Input-output models provide a great deal of detail on the economic transactions that take place within an economy 72 and offer some understanding as to hou impacts originating in one sector are transmitted throughout the economy. The I/O technique is a tool uhich can be utilized to determine economic impacts of changes in final demand given complete quantitative input-output accounts uhich express intersec­ toral linkages. In an input-output analysis some assumptions have to be qualified in order to derive the structure of the model. The key assumptions are: a) each industry in the local eco­ nomy is dependent upon every other industry; b) sales by firms are dichomatized into intermediate and final uses; c) production functions for each industry are linear and homogenous so that economies and diseconomies of scale are disallowed and inputs must be in fixed proportions; d) prices and wages are assumed constant and no supply constraints exist, with these assumptions, we can represent a typical input-output structure mathematically as (Pleeter, 1979): s t Y^ J- i Where; + e^ (l — 1 , 2 , 3 , . . . s ) J X- ■ *J Y.^. = sales of regional industry to regional industry = sales of regional industry to regional final demand sector e. = export sales of regional inudstry X. =total sales of regional industry i s = number of industries t - number of final demand sectors excludign ex­ ports 73 The input side of the model is represented by s t + Where: X. m . J total purchases in industry J value-added by final payment sector in industry imports by industry Structure - Input-output model Input-output model depicting forest products sectors mas recently completed (Chappelle, et al., 1986). It focussed on interactions of the forest products sectors with one an­ other and with other sectors of the State's economy. Primary data for the year 1900 from firms in forest-based industries were combined with secondary data for other sectors to con­ struct an input-output model of the State. Michigan's economy was organized into endogenous sectors according to the Standard Industrial Classification (SIC) system. Sectors were high­ lighted so as to provide detailed information on the forest products industry. The sectors were aggregated into ten Michigan forest products industry sectors plus three addi­ tional sectors representing roundwood producers (stumpage sellers) developed from primary data collection. In addition, remaining sectors of the model were reduced to 38 sectors based on to their relative size in Michigan's economy as measured by value-added, shipments. employment, payroll, and value of Exogenous sectors of the model included three major categories: households, government and out-of-state 74 trade. Government sectors are split into federal, state and local components. on cpaital, The payment row includes depreciation profits, and any other activity not accounted for by available data. The final demand column includes inventory accumulation, capital formation, and other sales not determined by available data. MULTIPLIER ANALYSIS FROM STflTEfS INPUT-OUTPUT MODEL Chatterji (1903) notes that input-output analysis has two major advantages. First, it is an excellent accounting method, which brings out a clear picture of the economy and points out sources of data inadequacy. Secondly, and more importantly as used in the Michigan study is in deciding planning strategy - i.e. to find out the required output levels of the sectors so that the stipulated final demands can be satisfied. To do so from the input-output table, the input-output coefficients are first estimated as: a ij Where: = = *ij f Xj total amount of input coming from i sector to the j sector output of the j sector If A - stands for the direct production coefficient matrix, then the fundamental equation is: X ( I - A ) F F = the vector for final demands X = the vector of total output I = an identity matrix One should note that the inverted Leontief matrix (l-A) is a multiplier matrix itself in that it provides information on the amount of sales generated by all sectors of the regional economy when final demands is increased by one dollar. As emphasized by (Chappelle, et al., 1B8G): Normally in economic development analysis use are interested in calculating various types of multi­ pliers ushich will indicate magnitudes of impacts likely to occur in the regional economy if a cer­ tain strategy is pursued in contrast to some other strategy• There are three basic types of multipliers: 1) A sales (output) multiplier for a column or industry can be computed by adding up the entries of a column of the in­ verted Leontief matrix. excluded. The household sector is normally However some authors calculate both Type I and Type II sales multipliers. a given industry, Sales Multiplier indicates, for the level of economic transactions that results from a dollar of sales in the economy. Higher output multipliers show higher degree of interdependence among in­ dustries in the economy, 2) Income multipliers, can be categorized into two ways. The type I multiplier indicates the direct and indirect changes in income by the next dollar of final demand. further to portray the totality of direct, Type II goes indirect and induced 76 changes in income as a result of a dollar's expenditure in the economy. In the type II multiplier process the house­ hold is endogenous or within the processing sector. 3) Employment multipliers form another set of multipliers. They assess impacts on employment from dollar sales on the economy. These multipliers are significant in regional analy­ sis because one can quantify the resultant employment from say, a policy of industrial expansion (Diamond, 1977). The approach to the analysis can be that of the Moore and Peter­ son method (Moore & Peterson, 1955). Employment - production functions sector-by-sector are calculated. Type I and type II multipliers are then determined similar to the procedure followed in calculating type I and type II income multipliers. They are derived by multiplying the State productivity ratios by employment. Employment then becomes a function of income since changes in employment reflect changes in demand. PALLET INDUSTRY MULTIPLIERS IN THE STATE Table 20 below indicates sales transactions between wood pallets and skids sector and all other endogenous sec­ tors (thirty six sectors) of the Michigan economy. The re­ sults are from the input-output study conducted by Chappelle et al., (1986). The table is read by going down the column; and first finding the amount of sales the pallet sector trans­ acts in the economy and how much other sectors purchase from the pallet industry. 77 TABLE 2D TRANSACTIONS MATRIX FDR THE PALLET INDUSTRY IN MICHIGAN. Selling sector (Thousand dollars) Wood Pallets and Skids 1900 73320 Buying sectors 1 2 3 4 5 6 7 6 9 10 11 12 13 1 it 15 IB 17 10 19 20 21 22 23 2 it 25 26 27 28 29 30 31 32 33 34 35 36 SOURCE: (Thousand dollars) -final M.5.U. sectors2994 Livestock; other ag. prod. 0 Metals, Minerals, Crude petro, etc. 2449 Construction Meat prod; Dairy; Beverages; Grain; etc. 52 Textile & Apparel D National forests 0 State forests 0 Other stumpage sellers 0 Logging contractors 0 1987 Sawmills and planing mils 750 Millwork, flooring, structural members 730 Wood furniture £ Fixtures 9399 Wood pallets and skids 4664 Veneer & plywood; other lumber £ wood prods. 4704 Integrated pulp £ paper; Particle board Paper mills (non-integrated), except 2437 building paper mills; Building paper £ building board mills 13024 Paperboard containers £ boxes 199 Other paper products; Converted paper £ paper board products Printing £ Publishing 47 3720 Chemicals; Plastics; Drugs; Allied products Petroleum refining 0 Rubber £ leather products SOB Stone, clay, galss, £ concrete products 390 Primary metal industries 0 Fabricated metal products, except machinery 0372 and tans, equipment Machinery 415 Trans, equipment 1026 Misc. manufacturing 13224 Transportation £ Communication 416 Electrical £ gas utilities 209 0 Water £ sanitary service 01 Wholesale £ retail trade Finance, insurance and real estate (F.I.R.E.) 3 Other services 270 Government Enterprises 152 Households 304 Chappelle, E. E.; Heinen, S. E,; James, L. E.; Kittleson, K. M. and Olsen, D. D. (1986). Economic impacts of Michgian forest industries; A partially survey-based input-output study. 78 Table 21 indicates technical coefficient matrix derived from transactions matrix in Table 20. The sectors numbered are the same ones as in the transactions table* Reading the table down the column should add to the value of 1 or thereabout. It shows how the average dollar of expenditure by wood pallet and skids sector (purchasing sector) buted to selling sectors. is distri­ In other words, it reflects input expenditures used to produce a product or service (in this case a pallet unit). Purchases from all other payments and imports (sector 37) is included in this table. Sales multipliers described earlier are determined on both type I and II basis. UJood pallets and skids sector have a type I multiplier of 1.875 and type II multipliers of 2.875. Uhile sales or output multipliers are not as use­ ful as income and employment multipliers, they nevertheless portray magnitude of direct and indirect requirements per unit of final demand. Income multipliers that show the amount generated by additional dollar of final demand have a type I multiplier of 1.917 and type II multiplier of 2.491 pertaining to the wood pallets sector. As a matter of fact, it has the highest multipliers of any forest product sectors in the State. Hence it is the forest products sector having the capability to contribute the highest income per dollar spent on it. Wood pallets and skids sector have type I employment mutiplier of 1.732 and type II employment multiplier of 2.069. 79 TABLE 21 TECHNICAL COEFFICIENT MATRIX FOR PALLET INDUSTRY IN MICHIGAN Purchasing sector Selling sectors SOURCE: li/ood pallets and skids -Final M.S.U. sectors 1 2 3 A 5 G 7 B 9 10 11 12 13 14 15 16 17 IB 19 20 21 22 23 24 25 26 27 2B 29 30 31 32 33 34 35 36 37 Chappelle, D. E.; Suzanne, Kittleson, K. M. and D. D. impacts of Michigan forest survey-based input-output 1 0 .00367 0 0 0 .00128 .0180B .02846 .02757 .15496 .01483 .00062 .09288 .01590 0 0 0 0 .00030 0 .00679 .00037 0 .00007 .OOBBQ .00294 .01030 .00558 .03139 .03156 .00062 .008B2 .02649 .00746 .03937 .22242 *23759 S. E.j James, L. II,; Olson. (13B6). Economic industries: A partially study. 80 In summary, when one ranks the wood pallets sector amongst the ten forest product sectors in the State, it is found overall to rank first in income multipliers {type I and II) and ranks second in sales multipliers (type I and II) based on the Michigan study. Taking into account shortcomings of multipliers such as their ignoring effects of economies of scale, impacts of input constraints and so forth, the input-output analysis indicates that the wood pallet sector is a worthwhile venture to explore for economic development purposes. If income maximization is the policy objective then it should have priority. CHAPTER V DATA, AGGREGATIONS, AND MODEL INPUTS FUNCTIONING OF THE MODEL Input or exogenous variables in the model are: (1) wood supply; (2) harvest costs; (3) transport costs--hauling distances; (A) production costs; (5) plant capacities--scenarios include 2556, 5056, 7556 & 10056 capacities; (6) spatial units--on the basis of aggregation of coun­ ties . Decision variables or endogenous variables influence the results of the model* These are: (1) uood surplus areas; (2) current plant locations or production points; (3) end-use markets or loci; (A) demand prices of pallets; The model should then be able to answer the following solu­ tion : Bl 82 Is there 11space11 for expansion of pallet industry and where should the next firm(s) be “optimally11 located in the region? In other words, the model should solve for the potential production or market point that would give the firm(s) the highest returns in terms of both: a ) profits and positive margin of demand price over cost, b) capacity in terms of available capacity. 5TUMPAGE SUPPLY TD THE INDUSTRY Pallets are generally constructed using lower grades of either hardwood or softwood lumber. generally come from two sources: Lumber for pallets a lower grade of lumber from “grade1' sawmills and a mixed quality, ungraded material from logs and bolts sawn at pallet mills (Pepke et al., 1977). The Michigan pallet industry in 1S81 consumed in total 280,8 million board feet of lumber (Table 22). According to these data, 72 percent of the supply is furnished by hard­ wood lumber, the rest by softwood. This agrees well with a national trend of about 75 percent of a pallet unit being made from hardwood. Hence the pallet industry is one of the largest users of hardwood per unit product basis. Accord­ ing to these data, it can be seen that the total lumber con­ sumption by the pallet industry in Michigan has remained about constant since 1973. B3 TABLE 22 LUMBER USED IN PALLET MANUFACTURING IN MICHIGAN (Millions Bd. Ft.) Hardwood Lumber Softwood Lumber Total Percentage of Hardwood 1981 2G0.B BO 280.8 71.5 1977 196.9 CD W Year 260.4 70.3 1973 190.0 79.2 277.2 71.4 50URCE: Grey* Ellefson and Lothner. 19B5. Timber supply and demand! A Lake States Regional Perspective. Apart from lumber, another source for pallet manufacture is Iouj quality plywood and veneer. Table 23 indicates that in 19B1 44.6 million square feet of plywood and veneer was utilized in pallet manufacturing. As opposed to lumber where there was consistency in the consumption rate between 1977 and 1981, plywood and veneer increased about 20$ in consump­ tion in the same time period. SUPPLY REGIONS FDR RAW MATERIALS An in-depth look was taken of forest inventory survey data for the Lower Michigan area, This permitted determina­ tion of volumes of timber available for utilization by forest industry. basis. Stumpage is further divided on a county level The stumpage level is assumed to cover raw materials 84 TABLE 23 PLYWOOD AND VENEER USED IN PALLET MANUFACTURE IN MICHIGAN (3/8-inch basis) SOURCE: Year State Consumption (million s q . f t .) 1981 44.6 1977 37.1 1973 26.1 Grey, Ellefson and Lothner. 1985. Timber supply and demand: A Lake States Regional perspective. or logs that are more than adequate to sustain both current capacities of the pallet plants and also fulfill future demand if pallet industrial expansion takes place in the region. Forests in Michigan cover an area of about 18 million acres or approximately SOjE of the total land area (Spencer, 1984). Commercial forests account for nearly 17.5 million acres which can be divided as shouin in Table 24. Although the largest area of commercial forest is in the Northern Louer Peninsula, the most concentrated commer­ cial forest is in the Upper Peninsula (Table 25). Neverthe­ less, Northern Lower Peninsula had the greatest increase in volume between 1966 (4945 million cubic feet) and 1980 (6825 million cubic feet). Therefore that area experienced the greatest percentage volume increase of 38/6, as opposed B5 TABLE 24 AREA OF LAND BY FOREST SURVEY UNIT AND LAND CLASS, MICHIGAN 19BD {thousands of acres) Forest Survey Unit Commercial Forest Land Eastern Upper Peninsula 3801.6 Western Upper Peninsula 4529.6 Northern Lower Peninsula 6694.6 Southern Lower Peninsula 2463.4 SOURCE: Spencer Jr. 1963. tory: Area. Michigan’s Fourth Forest Inven- TABLE 25 AREA OF COMMERCIAL FOREST LAND AND PERCENTAGE OF THE TOTAL LAND AREA BY SURVEY UNIT, MICHIGAN 1980 Survey Unit Area of commercial f orest (millions of acres) Commercial forest as a % of total land area Eastern Upper Peninsula 3.8 76 Western Upper Peninsula 4.5 82 Northern Lower Peninsula 6.7 59 Southern Lower Peninsula 2.5 17 17.5 48 SOURCE: Spencer, John and J, T. Hahn, 1984. Michigan’s Fourth Forest Inventory: Timber volumes and pro­ jections of timber supply. Be to an average of 25)6 for Upper Peninsula and 2656 for Southern Lower Peninsula in the same period (Spencer, 19B4). Since 4156 of rounduiood output comes from the Northern Lower Peninsula as opposed to a very low percentage from the heavily urbanized Southern Lower Peninsula, that area should be the focus as a source of timber for use as raw materials for the pallet industry. AI s d a study of apparent annual timber surpluses in the northern two-thirds of the state showed further abundance of timber in the Northern Lower Peninsula (Michigan Department of Natural Resources, 1982). Evidence shown in Table 26 suggests that substantial opportunity exists to base new industry or expand existing industry on the timber surplus. The apparent timber surplus was determined by totaling annual net growth and mortality. deducted from this total. Timber trend removals were In addition, fiber requirements of recent major industrial expansions were deducted from this total. Mortality of 2556 is considered significant be ­ cause some of it is potentially available as low quality fiber. Further, both increased efficiency in silvicultural treatment and utilization in wood harvesting could convert mortality value into a useful raw material and hence increase wood supply to the forest products industry. It is also important to utilize low quality material to open the growing space for better trees. Since the unit of analysis in this study is the county, attempt is made to focus on the timber resources data available 07 TABLE 26 APPARENT TIMBER SURPLUSES NORTHERN LOWER PENINSULA: 19B0 (volume in cords) Hardwood Softwood 881,138 339,381 Poletimber M,t 195,444 374,657 Mortality 486,316 126,562 Sub-total 1,562,898 040,620 Products groups Sawtimber TOTAL SOURCE: H 2,403,518 Michigan Department of Natural Resources. Forestry Division. Report--Apparent annual timber surpluses for Northern two-thirds of Michigan. Sawtimber is defined as the portion of growing stock trees which contain at least one 12 foot sauilog or two eight foot sawlogs. Softwood must be at least 9" d.b.h. while hardwoods must be at least ll'1. #« Poletimber is defined as growing stock trees of com­ mercial species at least 5* d.b.h. but smaller than sawtimber. 88 on a county basis. Again the major sources of supply of timber are Northern Louier Peninsula and five counties in the Southern Lower Peninsula. Counties in Table 27 meet two minimum criteria in order to be counted as source of wood supply. These are a) Each county should have a growing stock of at least 10D+ million cubic feet and/or b) All the counties average 5056 of commercial forest as a percentage of land area. The rationale for these criteria is that these figures represent current stumpage capacity (about 571fABF) which is more than adequate to supply a pallet plant with logs (consumes about 1.5 MBF annually) and other wood products firms for a long period of time. Even taking into account constraints imposed upon the timber resource base by multipleuse and sustained yield acts, there would be still a surplus of available timber. As can be seen in Table 27, all counties in the Northern Lower Peninsula with the exceptions of Bay, Arenac, and Isabella counties can serve as sources of wood supply. In the Southern Lower Peninsula only five counties - Allegan, Berry, Kent, Montcalm and Muskegon have substantial timber resources. All these counties can serve current exist­ ing pallet firms as well as future expanded operations. The regional forest inventory data were aggregated on the basis of counties in Table 2B in order to form a wood supply region for the pallet industry. All counties within a supply region are assumed to have substantial excess amount of timber not only to sustain exist­ ing pallet plants but also any additional demand required B9 TABLE 27 AREA OF LAND AND NET VOLUME ON COMMERCIAL FORESTS BY COUNTY, MICHIGAN: I960 Commercial forest as a % of land area (percentages) County Alcona Alpena Antrim Benzie Charlevoix Cheboygan Clare Crawf ord Emmet Gladuin Grand Traverse Iosco Kalkasa Lake Leelanau Manistee Mason Mecosta Midland Missaukee Montmorency Newaygo Oceana Ogemaw Osceola Oscoda Otsego Presque Isle Roscommon Wexf ord 71 BO 51 60 52 79 59 7B 6B 60 51 65 70 02 34 64 47 34 44 57 05 57 43 SO 42 05 77 64 74 70 Net volume of groining stock (1000's cu. ft 313,422 229,430 204,406 143,591 223,201 366,166 196,700 229,246 256,980 161,384 164,917 225,740 190,364 303,726 112,604 244,926 152,245 104,234 135,365 105,71B 229,593 326,936 147,041 226,851 196,236 248,446 286,118 253,992 305,609 267,049 SOUTHERN LOWER MICHIGAN Allegan Barry Kent Montcalm Muskegon SOURCE: 26 31 22 32 51 160,301 147,582 117,307 147,371 160,911 Spencer Jr. and J. T. Hahn. 1904. Michigan's Fourth Forest Inventory: Timber volumes and projections of timber supply. 90 TABLE 2B AGGREGATION OF COUNTIES INTD UDOD SUPPLY REGIONS Counties Allegan, Supply Region 1 Barry, Kent Otsego, Montgomery, Alpena 2 Crawford, Alcona 3 Iosco h Oscoda, Roscommon, Clare, Ogemaw, Gladwin, Midland Osceola, Mecosta, Grand Traverse, Manistee, Mason, Charlevoix, 5 Montcalm 6 Leelanau, Benzie 7 Oceana, Muskegon 6 Antrim, Kalkaska, Missaukee by new plant(s). 9 The counties are shown in Figure B, Timber is assumed to be accessible and forest ownerships ready to fulfil any slack of demand in the pallet in du st ry . PLANT LOCATIONS IN LOWER MICHIGAN According to the Michigan directory of forest products manufactures, firms. the whole State of Michigan has 198 pallet In the study area the total number of pallet firms is 183 (Heinen and Ramm, 1900). Hence virtually all the Michigan plants are located in this area (about 9256 of the firms in the state). As shown in Figure 7 most firms are 91 ClfCW^ [Mm *,*: * *• "_ * .* *p • SLM »i : tLAtm,* w VOLUME CLASSES (Million cubic feet) flffpffl 4l*tM 500 + U tM ta * IggSL 250-499 100-249 Less than 100 Jd(*MA itjtitfim FIGURE 6, GROWING-STOCK VOLUME IN MICHIGAN COUNTIES BY VOLUME (1980). tttl* * * 92 \ / r i i \ »r«*»orii4rc; iwreju Tt\* **»* rvjfw VS— 2 ** y. KEy //// Production Zones ..,. Cities-Markets xvyx Plant Locations u**rc* fMTOm frf|W* ii^KinbiKz <"niuU FIGURE 7. ttr'**it* anein Detroit PLANT LOCATIONS, PRODUCTION ZONES, A ND CITIES (HARKETS). 93 clustered spatially throughout lower Michigan but the heaviest concentrations are in the southeastern area of the state. On a county basis, three counties account for the largest concentrations of pallet firms because of heavy industrializa­ tion and urbanization of the counties (agglomeration effect): LJayne, Oakland and Macomb. One major pallet consumer-auto- mobile industry and related firms are located in these coun­ ties. Since pallet plants are concentrated in certain por­ tions of state, of the model, for the sake of simplicity and functioning the production zones are aggregated on the basis of counties in those areas (Table 29). Basis of in­ clusion uithin a production zone is that each of the adjoining counties have at least three or more plants and any uiood supply region could use the same highway or railway network to transport logs or timber simultaneously to various pallet plants uithin a production zone. Also for reasons of econo­ mies of scale and access to end-use markets, these production zones are not only suitable for location of current plants but also for new pallet plant(s), should the need arise, UDOD PALLET MARKETS The use of wooden pallets for shipping and warehousing manufactured goods has increased rapidly not only in Michigan but throughout the U.S. Because of transportation costs pallet producers are oriented to their market centers. Michigan they ship to their local markets and industrial In 94 TABLE 29 AGGREGATION OF COUNTIES INTO PRODUCTION ZONES Counties Berrien, Production Zone Identifier Van Buren, Muskegon, 1 Cass 2 Ottawa, Kent □ceana, Newaygo, Oscoda, Alpena, 3 Montcalm, Mason Iosco, Arena, Ogemaw 4 Clare, Midland 5 Jackson, 6 Ingham St. Clair, Macomb, Oakland, Wayne 7 Monroe, Lenawee 6 Cheboygan, Otsego 9 centers such as Flint, Lansing, Kalamazoo, Detroit and Toledo, Ohio (border town) are local centers of commerce, Grand Rapids, (see figure 7). These industry and government. These metropolitan areas, with the exception of Jackson, have 100,000 or more inhabitants (County Business Patterns, 19B2). These metropolitan areas also provide three market segments that consume the largest amounts of pallets (Nies, 1985): (a) food, (b) automotive* and (c) government. such as Flint, Lansing, Detroit, mobile industry. Cities Toledo are centers of auto­ Hence they furnish markets for pallets. The Kalamazoo-Battle Creek area is a well-known food industry 95 center. Pallets for shipping and warehousing purposes are in demand there. The smallest city - Jackson is included because it serves as center for an important state government function. It has one of the largest prison systems in the country and utilizes pallets for food transfer. Aggregation of urban areas into market areas for pallet consumption is undertaken in order to facilitate location analysis. Neighboring cities are grouped into the following market loci as shown in Table 30. TABLE 30 AGGREGATION OF CITIES INTO MARKET AREAS Urban Cities Number of Market Areas Jackson A Lansing-East Lansing B Flint C Muskegon, Grand Rapids Ann Arbor, Detroit, Toledo 0 E Kalamazoo-Battle Creek F Midland, Bay City, G Saginaw 96 The formation of market loci assumes that production zones are geared towards serving any of these market areas. These are the ultimate consumption areas for pallets in the state. DEMAND PRICES OF PALLETS There are numerous small pallet manufacturing firms in the state. Approximately B2% of the firms are small-to- medium size, employing less than 20 persons (Bureau of Census, 1982). Price based on intended use and competitive bidding are the criterion for most pallet purchases. There are a multiplicity of factors that affect the worth of a wood pallet in the market place. Some of these are (a) buyer dominance of wood pallet market, ible or reusable, (b) nature of the product being expend­ (c) extent of access to end-use markets, (d) easy availability of raw materials, (e) existence of sub­ stitutes, and (f) numerous pallet types and sizes. Because a pallet is a product that comes in numerous different sizes and designs, it is difficult to stipulate uniform universal pricing mechanism. (1985) One form often used is specified by Nies in Table 31. However, this analysis follows another form of pricing procedure that is appropriate for comparative analysis. Product price is based on amount of lumber footage (board feet) per pallet unit. Price of $0.50 per board foot in a pallet is taken as the average measure of its exchange 97 TABLE 31 AVERAGE PRICES IN TERMS QF PALLET SIZE Price Ranges Dominant Size $ 2.DO - $ 5.0D 36" X 40" $ 5.DO - $ 7.0D 40" X 48" $ 7.00 - $11.00 48 » X 72" $11.0D & up 88" X 108” SOURCE: Nies, Joseph. 19B5, Bureau of In­ dustrial Development, Michigan Techno­ logical University. value to the consumer (Diaze, 1985). This is assumed to cover the costs of lumber including all operating expenses that go into manufacturing and delivering a pallet unit to the ultimate user. According to Mario Diaze, owner of a small average size pallet firm in Lansing, per board foot (bd. ft.). lumber costs $0.20 In terms of percentages of sale price per board foot ($0.50/bd, ft.)i 75# is taken up by operating costs ($0.23/bd. (waste or residue) ft.), ($0.Q5/bd. 18# covers product loss ft.), and the remaining 7% is the profit margin left for the businessman ($0.02/bd. ft.). Since there is no uniform pallet size or lumber content per pallet, ’'standard pallet" in this study would assume the findings of a study by Spelter and Phelps (1984) which used a national study to measure actual volumes of lumber input used per unit of pallet output between 1948 and I960. 98 The findings were termed input-output coefficients or r,use factors approach". Lumber consumption factor (lumber content per product) by end use was divided into softwoods and hard­ woods. The study found that softwood pallet consumption (BF/pallet) had remained constant at 24 bd. ft./pallet between periods 194B-19B1. On the other hand hardwood consumption per pallet decreased from 28 bd. ft,/pallet in 1949 to 17 bd. ft. in 1981. In summary, when both species groups con­ sumption patterns are added, it is realized that the weighted average lumber content of a pallet had fallen from 27 bd. ft. in 1949 to 19 bd. ft. in 1981. Table 32 indicates how pallet prices can be assessed on the basis of lumber content from the above study. TABLE 32 STANDARDIZED PALLET PRICE ASSESSMENT Board feet (total) Total Price ($) ($0.50/bd. ft.) Number of pallets (19 bd. ft./pallet) 19 9.50 1.00 10D 50.00 5.26 500 250.00 25.32 1000 500.00 52.53 1500 750.00 70.95 2000 1000.00 105.26 99 ESTIMATING PALLET COST Since pallets are law-value products, pallet manufac­ turers tend to locate within 10D to 150 miles of the indus­ trialized market centers {Forest Products Laboratory, 1971)* Pallets are also bulky and therefore expensive to ship. Thus they are generally sold to delivery points within a radius of 150 miles from a plant. The decision to locate a pallet plant on a particular site or area depends mostly on economic factors such as the cost of raw materials, labor and transporting the finished product to the market. When dealing with pallet industry in Michigan, that it is basically a small establishment industry. one finds About 62 percent of pallet plants in the state employ less than 9 people (U.S. Department of Commerce, 1982). A typical pallet firm considered in this study can be characterized as a small pallet firm and includes all activity from supply of either cut-to-size lumber or cut cants/squared logs or round log through production process to the selling of finish­ ed pallets to customer. The major manufacturing process considered is that of cutting wood into pieces and nailing them into pallets. This is where the major labor costs arise. Certainly transportation cost is one of the major constraints in the production costs -- all along from stumpage sale to product delivery at the market. 100 RftUI MATERIALS Pallet parts generally come from the lower grades of either hardwood or softuood lumber. Purchased lumber is usually number 2 or 3 common grade except in low-value species, where all grades are used. Hence practically all commercial species may be used for pallets. In Michigan such abundant species such as maple, aspen, oak, etc. could all be used for pallet manufacture. Because lumber and nails comprise about 50 percent of the cost of a pallet, they form the major determinants in pallet price formulation (Figure 8). The price of pallets at mills is usually quoted by the board foot. Though this is based on a valid principle (vary­ ing lumber requirements), this cost-figuring method does not properly compensate the mill for differences to the number of fastenings, handlings, labor, sizes and types of pallets. etc., involved in various The interview with the owner of the small pallet firm revealed that lumber cost is about $0.20 per board foot (BF). the lumber. at the plant. Processing wastes about 25% of This then totals $D.2S per board foot delivered Reliable cost estimates dictate accurate deter­ mination of nail or staple requirements. The same interview revealed that a typical small firm would spend about SD.D75 per nail. 101 Cost of raw material {42,150 Net Income (6.1$) Other Operating Expenses (14.7$) FIGURE B - BREAK-DOWN OF A TYPICAL PALLET COST IN MICHIGAN SOURCE: Huber, Henry. 19B2. 102 LABOR COST IN ft PALLET FIRM Apart from lumber cost or ran material cost, labor cost is the major operating expense in a pallet firm (Figure B). In a study of 17 Michigan pallet manufactures* it was found that labor cost averaged 32.2$ of every sales dollar (Huber* 1982). In 1904 a wage and labor survey was undertaken by National Wooden Pallet and Container Association. Results in Table 33 feature nine common job categories of a pallet firm. The survey report indicated a 3.656 increase in average wages paid to production employees from $5.58 in 1903 to $5.78 in 1984. averages The assumption made here is that these are straight (no weighting). cash compensations The averages are based on total (including incentive pay). A figure of 2000 hours was used when computing the hourly wages of salaried e mployees. If the labor/sales ratio (portion of a sales dollar attributed to labor cost) is high* to management. it should be of concern It may be the result of a high hourly rate or low production or lack of mechanization or any combina­ tion of the three (Huber* 1982). The Huber study further found that the range of labor cost to sales ratio for pallet manufacturers varies from 10. 056 to 53 .356. This difference can be attributed to considerable variation in the amount of manufacturing labor performed by the 40 firms surveyed. Some used cut-to-size and length lumber and only nailed it 103 TABLE 33 SUMMARY OF THE 1904 SURVEY OF WAGES National Central Region (includes Ml) Averages (hourly rate) President/CED Headsaw Operator Cut-off Saw Operator Re-saw Operator Planer Operator Pneumatic Nail gun Operator Nailing Machine Operator Lift Truck Driver Laborers (helpers* etc.) SOURCE: 22,32 7.22 5.10 5. 30 6.49 10.75 7.01 5.66 5.76 5.60 6.50 5.58 6.65 6.32 5.26 5.56 5. 88 4.92 National Wooden Pallet and Container Association. 1984. Mini Wage Survey. into a pallet. Whereas others cut cants or squared logs into lumber and the rest cut thin pallet materials from round log. Certainly more labor is expended cutting round logs and this difference in type of pallet operation accounts at least in part for the wide variation in the range of ratios. In the current input/output study of forest products industry in Michigan* the pallet sector was found to spend $0.2224 per dollar of expenditure on labor (Chappelle* et a l ., 1985). At a more practical level {at a firm level) the Lansing pallet firm interviewed indicated that labor cost accounted for about 3056 of pallet sale price. 1Q4 Hence labor costs are a significant factor in total production cost of pallet industry. Direct labor costs are those resulting from salaries, wages, and piece-rate payments. The labor/sales ratio also reflects labor productivity. Productivity of labor when ’’mixed" with capital inputs (tech­ nology) must be considered before comparison of labor costs are made between regions. Nature of labor force and conse­ quently labor cost vary depending on technology used in the production process. Comparison between regions or plant locations is made difficult by the fact that labor costs per unit of output (pallet) is influenced by factors such as variation in the use of factors of production, types of products manufactured and institutional constraints (Blyth, 1 96 4) . CALCULATION OF TRANSPORT COSTS Because pallets are relatively low in cost, and relative­ ly heavy, costs of transporting pallets controls to a signi­ ficant degree the economic availability of existing raw materials for pallets. Location of forest resources as well as their characteristics are the primary determinants for assessing the future technology and market strategy for pro­ ducing and supplying pallets (liJallin, 1977). Transport costs are costs of overcoming the barrier of distance between loca­ tion of production and location of consumer. In a typical pallet manufacturing process timber or lumber is shipped IDS from the supply region to the ultimate industrial consumer via the production plant site. This analysis assumes that most lumber or logs are hauled to the pallet firm by trucks since distances covered are mostly short. Even then most shipments of primary forest products in Michigan currently move by truck (DenUyl, et al., 19B2). Rail which was exten­ sively used in the past* has declined in importance because of abandonments, decreased reliability of service and rate increases relative to trucks. Nevertheless the forest pro­ ducts rail transport rate is less than that for trucks for long hauls when rail service is available. MEASURING ROAD HAUL DISTANCES Routes of travel are chosen to link supply regions to market locations via production sites. in the pallet industry, As it so happens the product has a low product price and at times the production point is in the same zone as the market place (market oriented good). Hence this accounts for overlapping of the production point and market areas in some places. In this analysis there are seven markets areas to be served with pallets 9 (Table 34). These are represented by the metropolitan areas of Jackson, Lansing, Grand Rapids, Detroit, Flint, Kalamazoo, Saginaw, and others mentioned. Most timber supply regions are in Northern Lower Michi­ gan. Pallet firms are located all throughout the state - TABLE 34 AGGREGATION OF RAW MATERIALS, PRODUCTION AND MARKET LOCATIONS INCLUDED COUNTIES IN A SUPPLY REGION 1 2 3 4 5 6 7 B 9 Allegan-Barry-Kent Otsego-Montgomery-Alpena Crawford-Oscoda-Alcona R d s c ommon-Ogemaw-Iosco Clare-Gladwin-Midiand Osceola-Mecosta-Montcalm Grand Traverse-Leelanau-Benzie Manistee-Mason-Osceana-Muskegon Charleuoix-Antrim-Kalkaska-Missaukee INCLUDED COUNTIES IN A PRODUCTION ZONE 1 2 3 4 5 6 7 8 9 Berrie-Van Buren-Cass Muskegon-Ottawa-Kent Oceana-Newygo-Montcalm-Mason Oscoda-Alpena-Iosco-Arena-Ogemaw Clare-Midland Jackson-Ingham St. Clair-Macomb-Oakland-liJayne Monroe-Lenawee Cheboygan-Otsego INCLUDED CITIES IN A MARKET AREA 1 2 3 4 5 6 7 Jackson Lansing-East Lansing Flint Muskegon-Grand Rapids Ann Arbor-Detroit-Toledo Kalamazoo-Battle Creek Midland-Bay City-Saginaw 107 clustered around the nine cited production zones. Each route extends from the market area to the edge of the supply area via a production point. The route then extends into the transportation network of the supply area (Osteen, 1976). Roads chosen are routes between points. Immediately after measuring the shortest straight-line distance (sd) for forest stand or supply region, transport distances by road quality class to a specific delivery/production point are then mea­ sured. These road haul distances can be stepped off on a map with a divider or for more exact results one can use a planimeter. Regardless of where the production zones and market loci are located, there. there are usually different ways of getting In this analysis there would be 9x9x7 or 567 alterna­ tive routes (or shipment paths) market areas. between supply regions and The problem becomes that of locating routes that have minimum transportation costs (Davis, et a l . t 1972). Hence the criteria established on this study measures the best route in terms of minimum distance. However, this would probably not be realistic because transport cost is a function of distance and road quality. TRANSFER COSTS Since only about 4.5$ of every sales dollar ($0,045) for a pallet is used to pay transportation charges, it does not seem to be a major factor in determining pallet cost. 108 But considering the fact that pallets are low priced products* transportation costs account for the biggest share after rau material cost and labor cost (Huber* 1982). The princi­ pal factors determining these costs are (a) road quality* (b) truck capacity* and (c) hauling distance. Most haul cost information on lumber or timber assume that the species transported are the best commercial species for the product. Pallet firms tend to use lower grade lumber or lumber of low value species. Also many firms use cants or cut-to-size lumber as inputs. Transfer or shipping costs following two equations (Dellyl* (i) were determined using the 1982): shipping timber $/CDRD (ii) = 9.34 + 0.0579 (MILES) = 10.25 + 0.14 (MILES) shipping lumber S/MBF (million board feet) The study assumed that the truck commonly used for haul­ ing forest products (wood chips, timber and lumber) is a AD,000 pound tractor-trailer which carries about 20 cords of timber or 25 tons of wood chips or 7.5 mbf (thousand board feet) of lumber. PLANT CAPACITIES Since there are diverse sizes and technologies of pallet plants* pallet capacity values for all firms are difficult to estimate. The analytical model here requires that plant 109 capacities be calculated and correlated to production costs. One would also have to know whether current plants are utilized at full potential at any given time in the year. are difficult to obtain. These data An assumption is made that plant capacities are never reached and hence would not impose cost constraints on the efficiency of a pallet firm. In other words, one may assume the variable away if it does not affect results* Maximum capacity of the model plant (small pallet establishment) would not exceed 500 units per G-hour day, which requires 10,000 to 15,000 board feet of lumber supply. The plant of this size may employ about 16-18 people. TOTAL OUTPUT A strong industrial base and large forest inventory has resulted in Michigan as a state having the second largest number of pallet manufacturers in the country, Michigan had 198 pallet firms in the state in 198D (Heinen and Ramm, 1983). The only other state with more was Ohio. Hence the state is a major area for industrial consumption of pallets. Annual production of pallets in 1981 in Michigan was found to be around 15 million units (Gray, et al., 1905). Econo­ metric analysis of the forest products industry showed value of pallet output in Michigan to be about 12D million dollars in 1984 (Data Resources Inc., 1905). 35. This is shown in Table Forecasts for the future year 20DD estimate that there would be output of about 186 million dollars in the industry. 110 TABLE 35 FORECASTS FOR SECONDARY WOOD PROCESSING SECTORS (MICHIGAN): 19B4-2000 (thousands of 1972 dollars) Category 1984 Wood pallets & skids (2448) Real Output Interindustry demand Inventories Governments Exports Imports Net Exports SOURCE: Data Resources Inc'. 2000 46448.67 57SD5.81 70991.80 86078.74 0.40 17.67 8001.72 19176.94 -11175.22 0.19 25.94 14370.51 29483.5B -15113.07 1985. The growth rate of the industry between the years 1984-2000 is expected to be about 8 percent (Table 36). TABLE 36 PALLET INDUSTRY'S GROWTH RATE PERCENTAGES: 1984-199D Category Real Output Interindustry Demand 2.7 2.5 Inventories Governments Exports Imports Net Exports SOURCE: 1984-1990 Data Resources Inc. -4.4 2.4 3.7 2.7 -1.9 19S5. Ill DATA SUMMARY As formulated, the firm-location model consists of a single commodity - pallet (from different lumber species groups). The source of logs or lumber originate from nine specified supply regions which pass through nine production zones to ultimately seven specified market locations. This means that there are 9x9x7 or 567 possible routes or shipment paths by which a unit of output could pass through the net­ work. The problem is to find the "paths11 for locating new pallet firms through this spatial network in such a way as to maximize profit. Therefore the issue is that of locating routes that have minimum transportation costs and manufactur­ ing costs. And if the pallet industry expansion took place these would be the best "spaces" to locate new plants. CHAPTER VI ANALYSIS AND RESULTS This is the key chapter that portrays results of the firm-location model as it relates to the pallet industry in Lower Michigan. The chapter should resolve three issues at the core of this research; the indentification of surplus timber areas* potential locations for expansion of the pallet industry, expected economic impacts of the action on the s ta te . RESULTS AND INTERPRETATION DF COMPUTER RUNS The firm-location model was written in Fortran language and is a modified version of one of Hoover's original Industry location programs tested with dummy data on an IBM 7090 at the University of Pittsburg in 1967. The firm-location pro­ gram in this study was run on IBM XT microcomputer. The flexibility of the location program allowed one to study the effects of variations in model parameters. Insights into stability and sensitivity of solutions to changes in parameters. Insights into stability and sensitivity of solu­ tions tD changes in parameters was achieved by analyzing 112 113 alternative runs of the location model. For instance* the importance of two key variables were tested to determine how they affected program results; (i) variations in plant capacities affecting program results* and (II) increases in demand (consumption) targest indicating magnitude of ex­ pansion of production locations. Initially, numerous runs were made to pretest and validate the location program. Once that was accomplished, to arrive at the solutions. (i) RUN-I. four computer runs were made They were as follows: This can be referred to as "benchmark” run (Table 37). It has the following characteristics; (a) at any given time* it represents the average typical plant in the pallet industry in the study area consuming about 1.3 million of logs a year or about 6000 board feet of logs daily* (b) the plant's maximum capacity is about 250 units per 8 hour day or about 3.21 tons of products daily* (c) estimates of demand are derived from national eco­ nometric models reflecting the regional consumption patterns of the product. The rest of the parameters reflect produc­ tion and distribution variables similar in all four computer runs. (II) RUN-II. Same as RUN-I* except demand is doubled. See Table 38. TABLE 37 RESULTS OF BENCHMARK SOLUTION (RUN-l) 565 404 350 2B6 166 110 40 Profit (DMAX) 55.04 47.19 45.86 35.30 25.75 23,07 10.77 Definations: N DMAX BEST ROUTES Supply Regions CG CG OM on RO RO AB Shipment Flaws (tons/hour) 1 1 1 1 2 2 1 Production Locations OA CM MO OA JI JI ML Shipment Flows (tons/hour) 1 1 1 1 1 1 1 Market (K) DET SAG MUS FLI KAL LAN JAC 7TT Routes (N) = number of eligible paths remaing. = positive margin of pallet price over delivered cost in dollars ($). = represented by the respective paths following the profit margins. Symbol descriptors for supply regions, production locations, and mar­ kets defined in Table F-l. TABLE 38 RESULTS OF INCREASED DEMAND SOLUTION (RUN-Il) Routes (N) Profit (DMAX) Supply Regions 567 567 504 504 392 392 294 294 294 210 140 112 112 63 26 22 4 3 255.04 247.10 245.06 235.30 225.75 223.07 210.77 150.39 142.88 142.83 120.15 120.67 116.85 105.65 43.79 37.72 33.29 23.55 CG CG 0M 0M R0 R0 AB 00 CO AB 0M CA CO CA GL GL MM MM Shipment Flows (tons/hour) Defination: N = number of eligible DMAX = positive margin of BEST ROUTES = represented by the Symbol descriptors markets defined in 1 1 1 1 2 2 1 1 1 1 1 2 1 2 2 2 2 2 Production Locations 0A CM M0 OA JI JI ML SM CM MO SM BU ML BU ON ON CO CO Shipment Flows (tons/hour) Markets 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 paths remaing. pallet price over deliver cost ($), respective paths following the profitmargins. for supply regions, production locations, and Table F-l. DET SAG MUS FLI KAL LAN JAC DET SAG MUS FLI KAL LAN JAC DET MUS SAG FLI 116 (III) RUN-III* Same as RUN-I, except figures are increased capacity value. (IV) RUN-IV. production capacity to reflect full See Table 35, Same as RUN-I, except the production capa­ city figures are low or minimal less). (25$ and See Table 40. Benchmark Solution (RUN-l) According to the program results, there are four supply regions comprised of twelve counties that should be harvesting locations for pallet plants in Lower Michigan (Table 37). Thes^ are areas of surplus or excess timber inventory that could be used to satisfy raw material requirements of new pal­ let plants in case of expansion of the industry in the region. The solution indicates that logs in supply regions mentioned below can be transported economically (optimally) to pallet plants located in any of the following production zones: Supply regions (counties) TD Production zone counties Clare-Gladwin-Midland Oscoda-Alpena-Iosco-Arean□gemaw Clare-Gladwin-Midland Clare-Midland Osceola-Mecosta-Montcalm Muskegon-Ottawa-Kent Osceola-Mecosta-Montcalm TD Oscoda-Alpena-Iosco-Arena□gemaw Roscommon-Dgemaw-Iosco Jackson-Ingham Roscommon-Ogemaw-Iosco Jackson-Ingham Allegan-Barry-Kent Monroe-Lenauee TABLE 39 RESULTS OF FULL CAPACITY SOLUTION (RUN-IIl) Routes (N) 565 ABA A03 322 2A1 160 79 Profit (DMAX) 55.OA A7.19 A6.03 37.A1 27.86 25.IB 1A.76 Supply Regions Shipment Flows (tons/hour) CG CG CG CG CG CG CG 2 1 1 2 2 2 1 Production Locations OA CM ON OA JI JI ML Shipment Flows (tons/hour) 1 1 1 1 1 1 1 Markets DET SAG MUS FLI KAL LAN JAC 117 Definations: N = number of eligible DMAX = positive margin of BEST ROUTES = represented by the Symbol descriptors markets defined in paths remaing. pallet price over delivered cost in dollars ($). respective paths following the profit margins. for supply regions, production locations, and Table F-l. TABLE 40 RESULTS OF LOU-MINIMUM CAPACITY SOLUTION (RUN-IV) Routes (N) 5B5 3B2 243 142 73 30 7 Profit (DMAX) 55,04 45.B6 44.62 31.83 23.01 16.85 5.32 Supply Regions Shipment Flows (tons/hour) CG DM RO CO AB 0M CA 1 1 1 1 1 1 1 Production Locations AO MO CM JI BU ML QN Shipment Flows (tons/hour) 1 1 1 1 1 1 1 Markets DET MUS SAG FLI KAL LAN JAC 118 Definations: N = number of eligible DMAX = positive margin of BEST ROUTES = represented by the Symbol descriptors markets defined in paths remaing, pallet price over deliverd cost in dollars ($). respective paths following the profit margins. for supply regions, production locations, and Table F-l* 119 In addition, efficiently the following markets could be supplied (optimally) of counties* from the plant in the stated group Pallet prices (per ton of sales) at the re ­ spective markets are also included: Markets Price per ton Production zone counties Jackson 106 Monroe-Lenawee Lansing 116 Jackson-Ingham Flint 128 Oscoda-Alpena-Iosco Flint 128 Arenac-Ogemaw Muskegon-Grand Rapids 138 Muskegon-Ottawa-Kent Ann Arbor-DetroitToledo 147 Oscoda-Alpena-Iosco Ann Arbor-DetroitToledo 147 Arenac-Ogemaw Kalamazoo-Battle Creek 120 Jackson-Ingham Midland-Bay CitySaginaw 137 Clare-Midland The map (Figure 9) illustrates the locations of surplus timber, optimal plant locations, form, and markets. In a summary it portrays the locational patterns and flows of the pallet firm-location model. Thereby imparting to the obser­ ver the spatial relationships of the model results. 120 4**1* wttr* 4 4 1 I jI U J Wf ** OSUC'* ////> iH9U***VWm Mfcatr* Uil/tli //// 4*1044*0 . Midi ////* fJFtfP ** ♦ Saginaw— U 3-- W/4 KEY |4'4 IW ,\jrJAmjt ///// **** Surplus Timber 'Optimal Locations' Markets 4Hf(4* .1iu111n ia t*r$* . Fliht u r ix s t* VJQitm FIGURE 9. LOCATIONS OF SURPLUS TIMBER, OPTIMAL PLANT LOCATIONS, A N D MARKETS, \\s 121 Interpretation With the pallet industry operating at the current capa­ city lev/el (50 percent), these twelve counties were found to be most suitable as supply regions for pallet firms in the Lower Michigan area. These stumpage supply areas are prime sites that could enable the pallet industry to deliver pallets to the markets at minimum delivered cost* It is important to note that most of the counties supplying logs are located in the Northern Lower Peninsula. counties are situated here; In fact eight these are Roscommon, Iosco, Mecosta, Midland, Osceola, Ogemaw, Clare, and Gladwin. Only four counties are located in the Southern Lower Peninsula; these are Allegan, Kent, Montcalm, and Barry. Most of the stumpage supply counties form a ring around the Southern Lower Peninsula area. Results also indicate that if pallet industrial expan­ sion is to take place, half of the best locations for new firms are in the Northern Lower Peninsula. they also happen to be heavily forested. Coincidentally, These areas are basically rural and are in close proximity to manufacturing cities of Bay City, Saginaw, Flint, and Midland. The rest of the best areas for plant locations are near the urban centers of the Southern Lower Peninsula; Jackson, Hillsdale, Muskegon, these are Ingham, Ottawa, Monroe and Kent. puter results heavily favor two counties, Com­ Ingham and Jackson, 122 as central places conveniently located to serve industrial cities of Detroit* Lansing* Battle Creek, and Toledo. As far as the results are concerned, the pallet industry would fetch the highest prices in the Detroit market. lowest prices are offered in Jackson. The Certainly, it can be inferred that heavy industrialization of Detroit and Toledo result in higher pallet demand, thereby contributing to the higher market prices for pallets in these urban areas. Increased Demand Solution Run (RUN-II) A higher demand function means that more pallet plants are brought into production as a result of increased output consumed in the market place. Given the same capacity level (about 50$) in the production process of the typical plants, increased production zones and distribution networks now come into action to satisfy increasing demand (Table 36). Interpretation If the pallet price is increased or even doubled (as in this program run) more forested counties would come under consideration as log supply counties. In fact, all 30 stump­ age supply counties are at one point or the other efficient supply points for the pallet industry. There would be in­ creasing utilization by pallet plants to satisfy the rising demand in the pallet markets. Based on the industry's current capacity rate, it appears that all nine production zones 123 in 26 counties would be operational for pallet manufacturing purposes. However, should demand continue to rise faster than supply, few cities are deemed to compete effectively for production needs, these are: Detroit, Flint, Grand Rapids, Bay City, Saginaw, dentally, Toledo, Muskegon, and Midland. Inci­ these are also the major pallet markets in Michigan since they are the centers for steel, manufacturing and auto­ mobile industries that consume most pallets. Full Capacity Solution Run (RUN-III) Again, based on the current capacity and demand levels, if pallet plants operated at maximum capacity and beyond it appears that only one resource supply region could satisfy all existing raw material needs (Table 39), a still fewer pallet plants could satisfy current product demand at the given seven markets. Interpretation If pallet plants were operating at full capacity, only three counties, instead of twelve counties at the current capacity (50$), would be sufficient enough to supply logs to sustain the current industry demand. are Clare, Gladwin, and Midland. These three counties These counties are located at the southeastern end of the Northern Lower Peninsula bor­ dering manufacturing cities of Saginaw, Midland, and Bay 124 City. These forested areas are also located to efficiently supply the big pallet markets of Flint, Detroit and Toledo. Lour Capacity Solution Run (RUN-IU) This solution is in reverse to the above statement. UJith low or minimum capacity it would take far more supply regions and production zones to even satisfy current demand, let alone maximum demand (Table 40). This result does not offer any practical solution for policy since there is a production limit beneath which a plant or industry cannot operate if it does not cover its overhead and production costs. In other words, to stay in business. a firm has to break even if it is At such a low capacity, costs might outweigh investment return margins, ANALYSIS OF PALLET-FIRM LOCATION RESULTS Results from the computer program can best be understood in the context of the study objective. This facilitates interpretations of the results in a more meaningful and realis­ tic manner. To further add reality to the program solutions, output and capacity results in Table G-l (calculated manually) can be used to compare and gauge the practicality of the computer results as far as the pallet industry is concerned in Lower Michigan. It is important to note that the 50% capacity is dependent on the assumptions made in modeling 125 and does not represent a survey result of the pallet plants in the Lotuer Michigan region. With respect to the computer model* the major solution it provides is that of defining the 'optimal' paths (I,J,K) out of a possible 567 paths (9 ,f 9 " 7) and designating the respective 'margins' (DMAX), i.e.* sales profitability. For more information regarding the solution's significance, refer to the earlier formulation of the location model in Chapter IV. However as it relates to the research focus of the pallet industry, these 'optimum' paths represent the identification of ideal timber supply regions and production locations for the seven given major pallet markets in the region. Specifically two study goals are obtained simul­ taneously in these results; (1) results indicate locational relationships and transporta­ tion flows between the surplus wood areas and current lcoations of pallet plants in the region. words, In other compare current plant locations in Figure 7 which show total number of plants in a county and potential plant locations in Figure 9 showing the optimal county locations. (2) results convey the potential for expansion of pallet establishments in certain locations by defining the current correlation between timber supply, production capacity, and market demand in a given area; i.e., where should new pallet plants be located in the region. In the context of model results, this is shown as which 126 "paths" offer the largest profit margins and can with­ stand further introduction of new inputs or outputs into the system so as to arrive at an equilibrium in a given time. Two criteria are used to identify these best paths; (a) positive margin of pallet demand price over its delivered cost. (b) available capacity or capacity expansion cost. The benchmark solution and sensitivity analyses conducted highlight three factors crucial in understanding the results of the location model; I-Capacity Values Capacity value is one of two key factors that imparts reality to the firm-location results. of the benchmark solution (RUN-I), From the appearance one would infer that more pallet plants need to be built in certain locations in the region (Table 37). In other words, there is an assumption of plants manufacturing pallets at full capacity in the Lower Michigan area. However, this is misleading, when it is realized that the optimal solution of the model is attained at a 50$ capacity ratio. This is derived from practical data on the physical capacity of pallet plants in the region (see Table G-l). Therefore, it is important to note that there is underutilization of pallet plants in the region. Hence accurate measurement of capacity values appears to be crucial in the determination of model results. 127 II-Demand Function Demand is the other key variable mentioned above. demand is increased, Uhen as measured by increases in real price, more plants and routes are brought into production and opera­ tion respectively (Table 38). Higher consumption targets offer higher prices and hence more output sold at the seven markets in the Southern Lower Michigan area. This can be explained by the fact that the location model is demand driven. III-Resource Supply Regions Since the pallet industry in Lower Michigan appears to operate at about 50$ capacity volume, it appears that with a scenario depicting a full or maximum capacity volume of the same number of plants, only one supply region would be able to fulfill the current output demand (Table 39). This portrays the magnitude of underutilization of timber resources in the area. However it would take a more detailed analysis to pinpoint exactly how much surplus timber is avail­ able specifically for the pallet industry. the pallet industry, For apart from other forest products industries compete for the same resource as raw materials. In this regard, it should be stated that the pallet industry is most direct­ ly competitive with the pulp and paper industry. Most often pallet plants process the least valuable quality of logs that other wood product firms might not consider as suitable raw materials. 12B To more clearly place the location model into perspec­ tive, one can also state that results are in the form of optimal points in space for specific resource management and production decisions. These spatial points represent the maximum range of distances between respective resource supplies, production locations and the markets for a given pallet establishment to operate and maintain a profitable economic activity. When sensitivity analysis was conducted upon the results, it was found that most optimal routes (paths) did not exceed 15D miles from the point of origin to the destination. Indeed this is the normal economic distance radius at which a pallet industry operates profitably. In view of the foregoing statement about distance, it is also worth noting that the northern most pallet plants did not appear in the solution for the southern markets. However if the same distance continuum is assumed to apply to the northern industries, it can be reasoned that pallets produced there are shipped economically to the Upper Penin­ sula. Or even depending upon cost differential of the indus­ try and therefore its comparative advantage with respect to the Wisconsin pallet industry, these Michigan pallet plants could also export pallets to the other states. 129 DISCUSSION OF PROBLEMS DURING TESTING Some problems were encountered when the first few runs of the model were undertaken. They dealt with the structure of the model as well as the nature of algorithm used in the p rograms. Problems in Computer Model Structure One of the major obstacles dealt with clarification of unit specification for input/output variables. The original unit specifications in the location model were in million board feet (MBF) of lumber or number of pallet units (19 board feet/unit). Yet the unit of analysis for the location computer program was on a per ton unit basis. Hence approx- mate conversions were necessary to reduce the unit of inputs from MBF to one-ton basis. related variables, variables, Once this was done* all other especially production and consumption had to be converted to the common denominator before program execution. The transport cost functions for inputs and outputs used in the study were different from the program transport cost computation techniques, i.e. dif­ ferent formula were used to compute transport cost rates. Some adjustments had to be made to approximate the two proce­ dures. A transportation study of timber and lumber shipment on the Great Lakes provided the formulae used to derive the transportation cost values that served as inputs for the program (OenUyl, et al., 1982). 130 One major deviation of the model from program that inevi­ tably affects results is capacity figures* The model had assumed away the impact of capacity constraints on results. Yet the program is structured in such a way that it cannot execute without capacity values at both source and produc­ tion location. Therefore, the problem became that of defining the specific capacity values and their role in the computa­ tion of results. The computer program requires that the capacity values at raw material sources be higher than the values at production locations. follows: This can be explained as About 20%-25% of timber that goes into making pallets is wasted in the production process as residue, hence it could be said that one ton of logs (raw material) results in about three-quarters of a ton of lumber suitable for pallet manufacture. Therefore, the maximum capacity at the sawmill receiving raw materials would be higher than at the pallet plant receiving lumber processed from the same inputs. instance, For 6 tons of logs processed at the sawmill would pro­ duce about A . 5 tons of lumber for the pallet plant. Also, the extra cost of processing logs or lumber beyond full capa­ city was made too high to influence program results. The reasoning behind this is that by setting high costs for any output beyond the initial capacity limit, it means that the plant's physical capacity is non-expansible. This not only requires less data but also eliminates more complications relating to interpretation of results. 131 Computation Problems With respect to the algorithm used in the program, it appears that it inas written in the Fortran IV programming language which was popular in 1960s. Hence the language had to be modified in order to be compatible with the current language in use on microcomputers-Fortran 77. This revised standard was completed and issued for commercial use in 1977. The program was keypunched from a source code into a floppy disk using an IBFI PC XT microcomputer. Conversion of the source code to machine code was accomplished through compila­ tion and adjustment of the program to fit the MS-DOS operating system. MS-DOS provides commands for memory management, file management, input/output control and program control. COSTS OF MODELING AMD PROGRAMMING Certainly the research costs entailed in attaining solu­ tions to the study problem cannot be ignored. of problem definition, From the time it took about one year and a quarter to realize results of this effort. allocations explained below, In terms of man-hour this means that a full one man- year was spent to implement this model once conceptualized. The research costs can be assessed from three perspectives: Data Collection The nature of this task involved mainly secondary data collection because primary data collection would be very 132 time consuming and ccstly. About seven months uere spent in collecting and processing data* Around twenty hours per week were spent on the activity throughout this period. Library sources provided most data needed for the study. Other major sources for data were: Michigan State University research findings and publications, federal agencies - parti­ cularly USDA, Michigan Departments of Natural Resources and Commerce, and National Wooden and Pallet and Container Associa­ tion {N W P CA ). Model Design Formulation and construction of the model required about seven months to accomplish. One had to ensure that the model definition fit the study problem. This involved adjusting and improving upon the model structure. The result was appro­ priate flowcharts reflecting all the variables and analytical nature of the problem. The statements were then refined and translated into FORTRAN language. During the 5 months time period, about 30 hours per week were spent on this por­ tion of research. Costs of Programming Considerable time and resources were used in computing the results. ter. This program was run on an IBM PC XT microcom- Initially the original unmodified program was run on a mainframe computer at the University of Pittsburgh in late 133 196Ds. The available source code was keypunched on the micro­ computer* Once this uias done several runs were completed to ensure model validity to get solutions. testing for sensitivity analysis. This also involved A programmer assisted in FORTRAN coding and programming operations (debugging). This task took about three months of about AO-hour weeks. ADJUSTMENT DF THE MODEL AFTER TESTING After several computer runs of the model, some adjustments were necessary to provide realistic solutions to the problem. The number of supply regions and production locations had to be either equal or less than ten. This is because the computer program structure necessitated up to ten spaces for each of the two inputs locations). (supply regions and production Hence though the original number of supply points in the model uias eleven, a way had to be found to reduce or aggregate them to nine supply points. Statistically, one had to ensure that minimum loss of information occurred. Data required in the model assumed constant production costs far all locations. Yet the computer program demanded constant manufacturing costs but varying processing costs, hence production costs differed at all the locations. Another bottleneck to the program was definition of product increments i.e., additional amount of input after each iteration. Since the unit of input for the computer 134 program uas per tan basisf I decided to put minimum unit of increment, i.e. one ton for each successive iteration. Since the location model is demand driven, demand func­ tion parameters do control the execution and results of the program. Hence an effort uas made to research relevant and appropriate demand parameters for the pallet product at vari­ ous markets. National data figures reflecting consumption patterns of the pallet products over the years were applied to the local market areas. The A(K) and B(K) parameters represent intercept and slope values respectively of demand curves of the pallet market. In a context of programming techniques and efficiency, certain recommendations for improving the location program seem appropriate at this juncture: (a) One of the weaknesses of this program is the output format. It should be improved upon in both its text and band structure. For instance, construction of output tables would be more demonstrative by showing shipments matrices from each origin to destination of each point. Unit measurements should be explicitly shown in real variables, not in inte- ger form as shown in the current program output. (b) The program should handle parameters interactively for all files each time a program is running. it now functions, three operations take place sequen­ tially during each program run; externally, As (a) access of files (b) modification of program, and (c) 135 stoppage and initialization of program. This laborious process involves stopping operations dur­ ing each step back and forth. There should be no need to consult the external editor, as now required. This modification warrants establishment of a data management subroutine that would change files auto­ matically and internally. This would be accomplish­ ed at the same time the operating system is running the program. Use of the external editor in the location program structure is inefficient in terms of time. INCOME AND EMPLOYMENT IMPACTS FROM THE INPUT-OUTPUT MODEL After gaining an understanding of location factors and patterns in the pallet industry, input-output analysis was used to evaluate economic impacts. The input-output model ascertains the significance of the pallet industry in increas­ ing economic growth of Michigan. This analytical method develops multipliers which are vital when assessing impacts associated with the introduction of a pallet firm(s) in the local economy. Results are generally conveyed in terms of output, income, and employment multipliers* In a way, this discussion takes the location analysis to its logical conclu­ sion - the regional impact of new pallet firms. Assuming that production output was maximized (full capacity) in the industry, location analysis of pallet industry 136 might suggest plant locations at major urban markets such as Detroit and Toledo, in the southeast and Muskegon and Grand Rapids in the southwest. The same scenario might de­ pict that rural based firms in the northeast could compete effectively for nearby urban markets of Saginaw, Flint and Midland. These markets and others would be assumed to be capable of absorbing extra output if industry is expanded. Once the rationale for expansion of the pallet industry in the state is established, it becomes necessary to take the analysis one step further and define the economic impact flowing from such an expansion. This is done before one appraises the potential impacts of pallet firms on cities or communities. On the basis of such factors as the projected industry's output growth rate of 7.7$ up to the year 1990, forecast of increased output sales from $12 million in 1984 to $19 million in 2000 (Data Resources Inc., 1985), surplus timber inventory in the state, comparative advantage of the industry in relation to the neighboring states, major pallet markets and other factors, one can assume that the demand for pallet products is increasing and is likely to remain so in the future. Hence let's take a hypothetical example: suppose ten new pallet plants were to be constructed in the Lower Michigan area, what would be their economic impacts on the region. This can be measured on the basis of income and employment accruing from the action. However, it is important to note that according to this study there is no need to build new plants in the state in the foreseeable 137 future because surplus capacity currently exists in the pallet industry. Income Impacts Scenario Impact is defined by a multiplier. In this instance, the income multiplier indicates value of income generated in the state for each additional dollar of final demand for the pallet sector's output. According to an input-output analysis of Michigan's forest products sectors, it was found that the wood pallets sectors had a Type I income multiplier of 1.917 and Type II income multiplier of 2.491 (Chappelle, et al.f 1986). In this analysis, the Type II income multi­ plier is appropriate since the households sector is removed from final demand and considered an endogenous sector in order to capture the full local income multiplier effect (i.e., the induced effect). constructed in the region, would be $9.37 million. If ten new pallet plants are total income accruing to the state It is calculated in the following manner; in respect to the input-output analysis, wood pallets sector had total annual sales of $73.32 million in the state in 1980. A typical pallet plant in the study produces at full capacity of 500 units per B-hour day. plants in the state, most small in size. There are 198 On an annual basis, this typical plant could manufacture 120,GOO pallets (IS board feet each) or process about 2.3 million board feet of lumber. On average, a single plant's annual output is 138 then $0.37 million ($73.32 million/190). Hence ten new pallet plants (assuming identical technology) would deliver annual sales of $3.7 million (10/190 w $73.32 million). II income multiplier of 2.491, Given Type total income generated for the state becomes $9.22 million ($3.7 million w 2.491). Estimated Mew Employment Employment multipliers indicate the number of jobs gene­ rated in the state for each additional dollar of final demand for the sector's output. Given the ten additional pallet plants built in the region there should be 150 to 190 workers employed. This is because a typical plant in our study assumed employment of 16 to 19 workers. 2.069, However given Type II employment of total number of indirect and induced jobs for the state would be between 331 (2.069 w 160) and 393 (2.069 M 190) . CHAPTER VII EVALUATION OF THE MODEL CRITICISM OF THE IDEAL MDDEL The model used in this study is a revised and modified version of the basic Hoover's industry location model (Hoover, 1967). It is celled a pallet firm-location model. However, it is important to note that the Hoover model is general and data sets were developed that apply to the pallet indus­ try. The model gauges how the location pattern of the pallet industry can be affected by a variety of assumed changes in resource availability, input costs, transport costs, pro­ cessing costs, or market locations as a direct or indirect result of project (plant) establishments. As noted by Hoover (1967, p. l): The (model) solutions describe the impact in terms of location shifts (including shifts in the pat­ terns of material and product flows) and also pro­ vide information on the change in the overall "efficiency" of the i ndustry’s location pattern as measured by total output or average delivered cost of the product with a given pattern of d e­ mands at the various markets. However as with any economic model, certain assumptions in­ herent in the formulation Df the Industry Location model 139 14Q might not jibe with reality of given situations and cases in the real world. The model's major assumption that the solutions are "optimal" implies existence of perfect competi­ tion in an industry amongst other assumptions. That is, prevailing conditions guarantee a free impersonal market in which the market forces of demand and supply, or of revenue and cost determine the allocation of resources and the dis­ tribution of income. In general, a purely competitive industry possesses the following four characteristics: (I) Each firm in the industry is small relative to the mar­ ket, i.e., it can exert no perceptible influence on price or produces only a very small fraction of the total output of the industry. If any single firm were to double its rate of output or cut its production, the impact on the total output of the industry would not be noticed. (II) The second feature of perfect competition is that con­ sumers, producers, and resource owners must possess perfect knowledge or be fully informed. In its fullest sense, per­ fect knowledge requires knowledge of the future as well as the present. (III) It is assumed that there are no significant barriers to entry to a purely competitive industry. Capital costs required of prospective competitors are not so prohibitive that they are effectively barred from entering the industry. In short, free mobility of resources requires free and easy entry and exit of business firms into and out of an industry. 141 (IV) Finally, all firms in the industry produce a product that for all intents and purposes is homogeneous. The impor­ tant point is that the product produced by each of the firms must be indistinguishable in the minds of the consumers, whether real or imagined. Noting the assumptions and inherent characteristics above should automatically convince an observer that no market (for instance the pallet market dealt with in this study) has been or can be perfectly competitive. Though Hoover*s industry location model makes an allowance for homogeneous product assumption, the model cannot meet the requirement for perfect knowledge in an industry under economic scrutiny or analysis. Even though the pallet industry in Michigan tends to be dominated by relatively small firms, preconditions for pure competition, which is one of the one still finds mono­ polies abound here and there in certain areas of the state (especially in cases of vertically integrated firms). Finally the assumption of free mobility of resources is very difficult to realize particularly in a natural resource based industry such as the pallet industry where imperfect factor mobility constraints, imposed by such factors as land, biomass, capital, and technology plays an important role in location of the business firms. Nevertheless, as indicated before, no industry, including the pallet industry, can pass all the tests for perfect competi­ tion. Economic models tend to be overly "abstract". Yet 142 □ne should recognize that it is precisely this abstraction that makes the model a powerful analytical tool. Hence in this case* the location model provides a standard that mea­ sures how efficiently the pallet industry operates in pro­ viding the product to consumers at minimum delivered cost. This is especially important fact since much stumpage is in public ownership. CRITICISM DF THE APPLIED MODEL The applied model in this study has its origins in Hoover's Industry Location model which is a general model. It is important to reiterate that data sets used here only applies to the pallet industry. Hence it is labelled a firm- location model that deals with locational shifts within the pallet industry in Lower Michigan. This applied model has certain weaknesses in its characteristics and assumptions. It also differs in many respects from the original general model (Hoover, 1967). Temporal Dimension One of the major deviations is that this analysis deals with a natural resource based product physical, biological, (a pallet) which has and economic components to its utility. Timber which is the source of raw material for pallets, grows and matures over decades before it can be commercially utilized in the production process. Hence there is a temporal dimension 143 with respect to varying stumpage productivity and supply over time as a consequence of biological processes and must be considered. The temporal dimension can also be applied to the manufacturing process of pallets since such factors as depreciation and rate of technological turnover do in­ fluence output of a physical plant over its physical life span which can be anywhere from a range of 20 to 40 years. However the applied model like the general model is static. Hence the temporal aspect of the problem involving time ele­ ment is not captured in the model structure. Aggregation Error Another criticism of this study model has to do with delineation of regions, analysis. locales, or spaces for location Though this is a general problem in regional eco­ nomics field, aggregation poses a particular concern in this research with regards to drawing boundaries of supply regions and production locations. first, Hence two complications ensue; the basis of aggregation of counties to form supply regions and production centers - this concerns finding focal points in space as origins of measurements to other points in space. Secondly, definition of routes of travel that link supply regions to the markets via production locations. Though a factor such as economies of concentration (agglomerative factor) tends to affect delineation of space and activity, the magnitude of aggregation error still cannot be under 144 estimated. This particularly affects direction and outcome of project results. Demand Function Estimates of current (future) demand and price levels should be a major determinant of capital expansion in the pallet industry. In addition* estimates of demand can assist forest resource managers to evaluate current forest programs, establish timber growth objectives, forest policies and programs. and aid in structuring Most econometric models of pallet markets are national in scope, and hence the demand function data used in most regional studies including the location model applied in this study, have their origins from the same aggregate base. This makes for distorted values and results for a specific region such as Michigan or the Great Lakes. The current demand function parameters are not perhaps true with respect to the local consumption and price figures in Michigan. Therefore there is need to develop regional or state econometric models that would reflect de­ mand functions far specific related markets. Furthermore, the pallet market can be segmented according to end-product markets such as the automobile industry market, the food pallet market, and the general industrial market. The resul­ tant econometric models for each of these markets would pro­ vide significant information to pallet manufacturers in these m ar ke ts . 145 Transport Cost-Confiquration In the applied location model, an assumption is made that the best route between points in space is measured in terms of minimum distance. in the real world. Yet this is not necessarily true This is because a transport (transfer) cost assessment is a function of both distance and road qua­ lity amongst other variables. Nevertheless these two factors play a crucial role in determining routes used for transporta­ tion. At the same time the exact "least cost" criterion is unrealistic due to complex mathematics, computation time, and costs involved to determine transport costs for every possible route. The pallet location model assumes that four- lane highways are the ones used. This may not always be true since other transport mediums such as waterways, rail­ ways, country side roads or freeways can be used to transfer timber or lumber. In addition, this model assumes that a tractor-trailer travelling at 45 m.p.h. might further distort the true time-distance picture of the situations in that it is major determinant of mileages between places and the one used to compute transfer costs in the model. example, If for a truck travels at a higher speed (say, 53 m.p.h.) the transport rates of lumber to sawmills, then from there to pallet plants might over estimate actual value paid by pallet manufacturers for hauling. The opposite would occur when trucks travel at a lower speed. The shipping cost formu­ lae used in this analysis is not necessarily the optimal 146 or "the best*' one. Further, it does not specifically encom­ pass all transportation problems and logistics of pallet industry for it is a general shipping cost formula that applies to any wood products industry. Hence it might not reflect the true transfer rates of pallet rates in the region. fortunately, Un­ there is a lack of transportation data specific for the pallet industry in Michigan. Errors caused by all the above mentioned factors in transport cost configuration have an important bearing on results of the pallet firm-location model. This is because transport cost is a major vari­ able in the formulation and computation of industrial loca­ tion analysis. Pallet Unit Definition To provide comparable information on requirements for wood pallet firms in Michigan, be made. certain assumptions have to The major assumption deals with the units in which location requirements will be expressed. The usual account­ ing practice of wood-using firms is to express cost items in dollar per sales unit, e.g. per thousand board feet of lumber, per thousand square feet of particle board, or per ton of pulp. On the other hand, in considering differences between locations, the figures available to the firm are normally expressed in terms of the units by which the inputs are sold: labor in daily or hourly wages, wood in thousand board feet or cords, forest land in acres. For the pallet 147 Firm-location model requirements are expressed in the latter terms - the units by which these items are bought by pallet firms. Hence wood is measured in thousand board feet (bd, ft.) - this is necessary if one is to make evaluations of alternative locations. ft pallet unit (load) in the model is equal to a production output of a typical Michigan pallet plant or firm per year. Each plant processes 1.3 million board of lumber as inputs or manufactures 6B,OOD pallets (19 board feet per unit) outputs per year. Hence a quoted pallet plant capacity figures represents total amount of stumpage flow or output for a given path that sustains a typical pallet firm for a year in business. Production fig­ ures are totalled on a yearly basis depicting legitimate business expenditures such as transportation, labor, and operating costs. whole pallet unit harvest Market prices are calculated for the (load). These yearly figures are used to permit location factors to be evaluated on a common basis, hence assessing the magnitude of location factors and their relative impact on location analysis. Supply and Demand Implicit in the model is the assumption that supply equals demand: the market. whatever the firm produces is consumed in In the real world, this is rather difficult since there is erratic demand for a product at any given time. This is because the pallet market is strongly influenced by overall general economic activity (especially the prices 140 pallet consumers receive for their products), while it is only weakly affected by activity (most significant being pallet prices) within the forest products markets (Luppold, et al., 1986). The business cycle (as influenced by the national economy) and state of the regional economy impact the nature of supply and demand for pallets in the markets. Hence various factors result in peaks or dips of demand for pallets and consequently their supply. This is because a pallet good is an industrial commodity in that it is a good whose demand is derived from demands for final consumer goods. Hence its demand fluctuates more than a typical consumer good given the linkages involved. ASSESSMENT OF THE MODEL FOR POLICY ANALYSIS The purpose of this section is to ascertain the suita­ bility of this model for a policy abjective, i.e. to analyze if there is potential for expansion of the pallet industry in Michigan. In other words, how does the model formulation relate to policy variables sought after in the study. There are political and economic issues that are not fully c a p ­ tured in the model structure. These issues are beyond the economic environment in which the industrial location model presumably operates, in the long run. but nevertheless still play a key role Hence when the pallet firm-location model is examined in terms of its relevance to policy problems, some pitfalls do emerge. following headings: They can be observed under the 149 Timber Supply One limitation has to do with the nature of the product and its raui material supply patterns. The product (pallet) is a forest resource product, and hence the raw material supply pattern is a reflection of the regional timber supply pattern. Significance of location factors in the pallet industry cannot be appreciated without understanding the relations governing business decisions. These decisions not only involve business firms but also Government agencies. The forests as source of timber for pallets are owned by various institutions such as Federal, State, Local govern­ ments, and private industries/groups. Each ownership type pursues its own management objectives over the forest resources, the forest management programs executed by each group influ­ ences the availability and costs of timber for production processes of various wood products, including pallets* Most government units manage forest resources under the principle of multiple use. This means that a forest is managed for both resource conservation ment purposes. (non economic) and economic develop­ Since government agencies own most of timber lands in Lower Michigan, it naturally implies that most of the timber market is controlled by those governmental bodies. Hence they possess monoplistic powers in the market place, thereby allowing the government to dictate price of raw mate­ rials or output produced* The industrial location model assumes that input or output costs are determined in the 150 free market place. That ignores the reality uihen it comes to timber sales in the state. Government units influence raw material costs, which in turn affects product demand outcomes in the state at any given time. Infrastructure Raw material and product costs which play a major role in industrial location analysis are influenced indirectly by quality and efficiency of transport systems of the region under study. For example, when the State of Michigan under­ takes to improve the transportation system, e.g., completion of a system of highways, building more railroad terminal facilities, port development, creation of waterways for ves­ sels, etc. - these do have an impact on the relative advan­ tages of a given area for industrial (firm) location. Construc­ tion of most projects are funded from tax revenues. Hence in areas such as Upper and Northern Lower Peninsula, which are well endowed with forests, transport improvement could tip the scales of locational advantage in their favor. Such an improvement might give the area an advantage over locations using inferior or high-cost materials which had existed be ­ cause of nearness to markets. On the other hand in the south­ ern region (Lower Michigan) which has densely populated market areas, transport improvement would cut costs of assembling raw materials there. Impacts of transportation policy is hardly built into the industry location model but it is 151 sufficient to say that many questions of transportation policy can be analyzed using the model with different data sets. Production Functions In an empirical location study, results seem to be in­ fluenced by nature of production functions within an industry. The production function can be defined as the physical rela­ tionship between various inputs (including transportation inputs) and output. other industry, In the pallet industry, it is necessary to know (a) raw material inputs per unit of output, puts, just as in any (b) the utility and labor of in­ (c) manner in which these inputs vary with the size of plant, and (d) the manner in which the capital investments vary with the plant size. analysis, Using another facet of location an examination of an industry's production func­ tions and market areas could indicate a spatial framework of areas or regions which are suitable for plant location (flirov, 1959). The technology of pallet manufacture suggests that the most important types of variables include raw mate­ rials (lumber), nails, transportation, and labor. The pallet industry is continually using more high technology in their production processes. Also increasingly more firms (about 20%) are trying to upgrade the value and quality of pallets by binding wood and metal together to produce a pallet unit. This also allows entrepreneurs to compete with others in a larger market radius. Innovation has also resulted in 152 a new production technology called palletech process (Nies, 1905). This is a patented method of manufacturing industrial grade, molded wood, material handling pallets. This is a technological advancement over conventional lumber-mailed units. As opposed to labor intensive operations prevalent in the traditional technology, capital intensive, the palletech process is heavily and can be sold beyond the usual 1DD-150 miles radius typical of wooden pallets. Actually palletech pallets can be sold profitably up to 500 miles market radius. flgg1 omeration Economies Both location theory and the economics of pallet manufac­ ture suggest the importance of agglomeration economies in industrial plant location. In general the pallet industry is dominated by small scale production plants. Minimum capi­ tal is required to start a pallet firm, hence this results in lower capital costs per unit of capacity. Due to low market values, pallet firms tend to be market-oriented taking advantages of localization economies. They tend to be in close proximity to their industrial consumers such as Michigan automobile manufacturers. A typical pallet firm in this study is assumed to be composed of two production units. This makes for a vertically integrated industrial structure (integration economies). wood or logs to lumber. One is a sawmill that reduces roundThen a pallet plant takes over to process this lumber to pallet parts and is finally assembled for the consumers (for more details see Chapter III). 153 Assessment Though the industrial location model attempts to offer optimal results, it nevertheless has imperfections hampering its ability to answer fully policy questions posed by decision makers. The model is equipped to effectively handle economic variables in a static setting. Yet the non quantifiable variables (e.g. policy stipulations, rules or laws) that might affect the nature in which the industry operates are not fully captured by the model. Production functions of pallet firms are affected indirectly by these non-economic forces. The legislative enactments, matters as harvest criteria, freight charges, etc. law and rules on such tax rates, transportaion systems, regional/local economic development plans, influence the workings of the market. More specifically the pallet industry's production costs, such as stumpage costs, labor costs, and transfer costs are influenced indirect­ ly by nature of business climate in the state caused by vari­ ous laws enacted by the state. For instance Federal and State harvest rules affect the amount and quality of stumpage available for sale to sawmill owners. Also, state or local government efforts at road constructions and improvements could indirectly affect business owner's location decision. The nature of economic incentives and tax structures do enter into a fi rm ’s location decision calculation. An owner of a pallet firm interviewed for this study emphasized that M i c h ig an ’s w o r k e r ’s compensation act was the most important 154 single factor that in the future would affect his decision to operate or expand his business in the state. As can be observed, these non economic forces appear to have substan­ tial clout (in the long run) i"n influencing results of indus­ trial location analysis. If these exogenous dynamic vari­ ables (policy issues) are not taken into account in the loca­ tion model, validity of results would be questionable. Hence their role in business location decisions should be considered simultaneously with the usual economic variables in any eco­ nomic analysis. The magnitude of many of these intangible variables could be reflected in the input data sets. For instance, using different stumpage or freight rates for the pallet industry and testing their impacts on firm location decisions. CHAPTER VIII S U MM AR Y, CONCLUSIONS, To reiterate, AND RECOMMENDATIONS the purpose of this study uias to analyze the potential for expansion of pallet industry in Lower Michi­ gan: i.e., examining whether there is "space" for expansion and if positive, analyzing spatial distribution of pallet firms in relation to surplus wood areas so as to determine future sites (routes) for location of pallet firms. This is accomplished in the context of resource constraint analysis and its impact on the locational aspects of the industry in Lower Michigan area. Results of locational analysis are linked with input-output multipliers to ascertain potential economic impacts of pallet firms on the state, were to take place. ings in three parts: if expansion This chapter summarizes research find­ (a) summary of results, ing conclusions and policy recommendations, (b) highlight­ and (c) ideas for refining methodology and data. SUMMARY OF RESULTS The results can be better understood when viewed in relation to three major issues discussed and analyzed in the course of study. These are 155 156 (a) interpretation of final results when the capacity factor is considered; (b) surplus wood areas in relation to current pallet production locations; (c) potential economic impacts of pallet firms on the region. The main conclusion is that the pallet industry in Lower Michigan is operating at about 50$ capacity in the context of the assumptions and data of location model. Hence expan­ sion of the industry by investment in new plants in the region is not necessary at this point in time. Only when this ex­ cess capacity in the pallet industry is utilized* should an alternative of building new plants in the region be con­ sidered. However, if new pallet plants that are to be intro­ duced use new technologies, the decision to build new plants in the region may be worth reconsidering. If new plants are built they may compete away market share from existing plants. Ignoring possible new technologies, it now appears, however, that the low capacity factor appears to be a reflec­ tion of the vitality and productivity of the pallet industry in the Lower Michigan area. The capacity could be increased in one of two ways or both concurrently; first, increased demand for pallet products and/or secondly, increased product market share relative to substitute products. With regards to surplus wood areas, except for four counties (Allegan, Barry, Kent, and Montcalm) in Southern Lower Michigan, all counties endowed with surplus timber 157 stock are in the Northern Lower Peninsula, In total twelve counties serve as reservoirs for timber for production pur­ poses, including the pallet industry in Lower Michigan. These counties form a rim around the industrial Southern Michigan region. They have enough excess timber inventory and productive timber growth to sustain new forest products firms, pallet plants included. It is important to note this fact since the same wood could be used for manufacture of other products such as paper, etc. Nevertheless, plywood, construction lumber, these areas are within economic distance of the industrial consumer markets of southern Michigan. Going back to the beginning, this study is an offshoot of a project that analyzed economic impacts of forest pro­ ducts sectors in the State using an input/output model (Chappelle, et a l ., 1986). The analysis projected that the wood pallets sector had the highest income multiplier of all the major forest products sectors. Hence increased use of pallet plants (as a result of increased product demand) in the suitable production zones would also generate consider­ able income for the counties and cities involved. Most of the major pallet markets are also locations for major indus­ tries such as chemicals, steel, machinery, and transportation equipment supplying both local and national markets. However summarizing again, the pallet industry in Lower Michigan has excess capacity and increased demand can be met from the current plants without necessarily building new pallet plants in the region. Hence there is no need 158 for expansion of the industry at this particular time unless demand doubles or increases considerably. Gr unless more efficient pallet plants come in and compete away current market shares. CQNCLUSIGM5 Lower Michigan (composed of Northern Lower Peninsula and Southern Lower Peninsula) was the focus of study region. Inventory of natural resources, particularly forest biomass was reviewed. Other socio-economic resources of the region were also considered. All this information was essential to achieve the analytical and policy objectives of the study. Manufacturing industries of the region were also examined to assess significance of their production output and sales. Forest products industries, particularly the pallet sector were further scrutinized for their impacts on the regional economy. Also the general nature, structure, and technology of pallet firms in the industry were studied. Surplus wood areas were located mostly in the Northern Lower Peninsula. These areas were then made timber supply areas suitable for forest products processing, particularly pallet manufacture. Resource and economic constraints that influence pallet supply and demand were analyzed. timber species, competition, Such factors as type of demand, and pallet price were found to influence pallet production. Pallet market structure was also studied to realize its impact on demand. Certainly 159 the study of constraints cannot be complete without under­ standing the role of Michigan's comparative advantage for pallet manufacturing. The crux of this research was spatial analysis of pallet plants vis-a-viz surplus wood areas and markets in the region; i.e. to determine whether expansion of pallet industry is likely to be profitable in the region and if affirmative, selection of potential locations for future pallet plants in the region. This was accomplished through the use of an analytical tool, the pallet firm-location model. However, the main finding from the analytical results is that the production capacity (as defined by the model) pallet plants is not fully utilized. of the current Hence there is still room for increased output without construction of new plants. However should potential new pallet plants use new technologies, the possible introduction of new plants into the region should be reconsidered. Nevertheless existing plants can handle .any foreseen increased demand for product. Nevertheless should there be a decision to expand pallet industry in Lower Michigan, half of the best locations for new pallet plants are in the Northern Lower Peninsula and the rest in the Southern Lower Peninsula. The best locations in Northern Lower Peninsula for new plants would be eight counties: Clare, Roscommon, and Midland. Ogemaw, Iosco, Mecosta, Osceola, Gladwin, These counties are located at the south­ eastern end of the Northern Lower Peninsula bordering manufac­ turing metropolitan areas of Bay City, Saginaw, and Midland. 160 These forested counties are basically rural and optimally located to efficiently supply the big pallet markets of Flint* Detroit, and Toledo, The rest of the best areas (counties) for plant locations are near the urban centers of the Southern Lower Peninsula: these are Ingham* Jackson, Hillsdale, Muskegon, Ottawa, Monroe, and Kent. two counties, The results indicate Ingham and Jackson* as central places to locate pallet plants - because there are conveniently located to serve industrial cities of Detroit, Flint, Lansing* Battle Creek, and Toledo. However, apart from the plant site analysis conducted in this study, another key factor that influences plantlocation is the tax climate in an area or region. The tax climate, as one element in the general business reputation of a State, influences plant location decisions. some location decision It influences making by causing firms to exclude certain states or urban areas from consideration, Apart from financial inducements in the form of tax concessions, others take the form of low interest loans at State and local levels. These inducement packages could be manipulated to attract wood products firms to the State. This would apply especially to large capital intensive industries. Intensive forest management can also be fostered by legislating proper forest taxation laws and incentives to keep private landowners reinvesting in their lands for increased timber yield. With the apparent surplus of timber in the region, the pallet consumption pattern rate becomes a constraint in 161 determining use of timber as raw material for the industry. Hence resource supply (timber) becomes a commodity for the pallet industry only when its market demand exists. The recently published forest products industry input/ output model of Michigan provided coefficients necessary to assess impact of pallet industry on the state (Chappelle, et a l . • 1986). The multiplier analysis .indicated that if income maximization is the policy objective pursued, wood pallets should be given priority. Therefore, then should the product demand increase, higher production output in the pallet industry should result in good economic results for the region. The next topic below explores policy avenues that would facilitate utilization of timber resources for commercial purposes as well as for general economic develop­ ment efforts. RECOMMENDATIONS Strategies for Economic Development The motive behind the selection of this research topic was desire by professionals (policy makers, source managers, businessmen, and planners) economists, re­ to identify re ­ source sectors that could foster economic development in the state of Michigan. The basis of this action was an a t­ tempt to diversify and revitalize the sluggish economy of the state in the early 198Ds. 1B2 □ne resource base not fully Utilized Idas the forest resources. Hence the u/ood pallet sector was chosen for in­ vestigation as a result of possessing the highest input/output income multipliers amongst all the uiood products sectors in Michigan. It is in view of the foregoing statements that strategies for forest resource development are defined* parti­ cularly as it relates to the wood pallets sector: (I) In the study region, the largest single owner of commercial forest land is the state of Michigan. This is administered by the State Department of Natural resources. One realizes that the state's forest management policy is not to maximize timber production as the sole goal, but rather to manage under the multiple use concept. This means that other than for timber, public forests are managed for non-commercial purposes such as recreation, hunting, wildlife, and so forth while protecting the environment. Though evidence indicates there is surplus timber in state forests, there is insuf­ ficient demand to justify further processing of timber for pallet manufacturing, (II) One effective way to increase timber use by industry is to stimulate derived demand. the pallet industry, In the case of evidence indicates insufficient demand to utilize the current industrial capacity. Since demand level is set in the market place, only increased demand will result in increased use of 163 plants* if not expansion of the industry. However, the state can assist in this process by mounting a campaign to promote use of pallets* such as en ­ couraging palletization of handling systems. Pro­ motion through either incentives or advertising could boost use of this industrial good. Overall the effort by the state to attract and keep major automobile and chemical industrial complexes in the state will go a long way towards fostering demand for pallet products, i (ill) Accessibility to forested areas can be facilitated by investment in transportation systems. The state has regulatory or revenue power to help accomplish this goal. Most forest products are transported by truck on public roads. Hence regulations pertain­ ing to truck weights and load size influence volume of transferred wood products and therefore cost of transporting materials and products between places. Maintenance of the rail transport system in major forested areas should be encouraged by state regula­ tions. port, Other transport systems, such as water trans­ should be explored by the state as a cost- cutting device for wood products firms. 164 SUGGESTIONS FDR FURTHER RESEARCH Methods developed in this study appear to be effective for spatial timber supply analysis, which can help guide regional economic development. However, these methods do not resolve fully all the technical and policy questions posed in determining optimal locations of establishments in the pallet industry (or for that matter, any wood products industry)* Much needs to be done to improve research methods. Some areas where improvements can be made are suggested be­ low. The first two relate to alternative model designs that could also solve the industrial plant location problem. The remaining two areas for improvement pertain to improved data required for pallet industry analysis. (a) The location analysis can also be approached from a different angle. technique. This involves use of a spatial equilibrium It could be applied to study the pallet industry in Lower Michigan. The specific objective would be to deter­ mine the probable spatial organization of the pallet industry under future economic conditions with special emphasis on identifying conditions under which production plants could be moved closer to market areas, away from raw material loca­ tions (supply regions). For a given industrial structure, a spatial equilibrium model can be formulated to determine locations of manufacturing that will minimize total costs of all manufacturing and transportation for the entire indus­ try. Since cost minimization is the rational behavior of 165 entrepreneurs in a perfectly competitive industry, the solu­ tions are reasonable forecasts of probable future spatial organization of an industry. However one should realize that results would be for the sector as a whole not for the individual firm as in the location model. (b) Further in-depth analysis could be undertaken using econometric techniques that would serve one or more of the following objectives; (a) ascertain forces associated with location of individual pallet firms, (b) project spatial distribution of pallet firms, and (c) offer policy guide­ lines to local development planners in evaluating employment prospects for their counties or cities. Regression analysis is a statistical technique to estimate from empirical data the relationships between two or more economic variables. These methods would permit analysis of changes in the location of firms or plants and determine the variables associated with these changes (for more details see work done by Spiegelman, I960). With regards to location analysis, as in this study, the regression model would highlight the magni­ tude of three key impacts of external forces on plant loca­ tion: (l) transportation costs, (2) production costs, and (3) agglomeration factors. (c) Host econometric studies of pallet markets are national in scope, hence there is need for research that would segment the markets according to appropriate geographic markets or consumer centers. In this case Michigan econo­ metric models could be constructed so as to obtain a more 166 accurate information on demand and price projections for pallet products in the state. (d) Further research is also needed on forest resources as rau materials for the pallet industry in the state. This would attempt to correlate specific timber species to a parti­ cular economic activity or production location at a given time. In the case of the pallet industry, amount of specific species (e.g., maple) this implies the consumed by a given manufacturing technology at a location at a given time. Essentially this would be a spatial-temporal model of re­ source analysis for production or consumption purposes. Also related to this issue would be an effort to structure a model to determine opt imal allocation of timber to product lines given timber resource constraint analysis. APPENDICES APPENDIX A SYMBOLS FOR FLOWCHART APPENDIX A - SYMBOLS FOR FLOWCHART CUD Beginning or ending of a program Start of Do Loop I index j, Limits n, m, Process or Computation Input or Output Decision Print T ’Information' or 'Results' Operations sequence and Oataflow direction o An entry form, or exit to another part of the program flowchart 167 APPENDIX B GROSS LOGICAL FLOWCHART C 5tart D Total Stumpage Supply Wood Surplus Region Harvest Cost Transportation Matrix [Access] to Raw Material Sources Input Transport Costs Three Alternative Manufacturing Technologies \/ Cut-To-Size Lumber Cut cuts/ Squared logs Saiumill Plant Locations 16B 169 2 Wood Processing and Nailing Plant Capacity V a l u e s : 7 [0.25; 0.50; 0.75; 1.0D] / Production Cost Finished Pallets (Output) Transportation Matrix (Access) To Markets Output Transport Costs For Each Supply Region Potion Calculate Raw Material Costs And Transport Costs to Plant Location Per Pallet Unit End-Use Markets For Each Plant Location Option Calculate Production Cost and Transport Cost to Deliver A Pallet Unit tD Market Q Determine Delivered Cost Totals for Each Pallet Unit 1* Raw material costs plus transport costs from each supply region to plant locations. 2. Production costs plus transport costs for each pallet unit from plant locations to markets. 3. Sum values; "2 + 3,f. 4. Shock the System: with alternacapacity constraint values: [0.25, 0.50, 0.75, 1.00] 5* Calculate a delivered cost to markets. Total value: u2 + 3 + capacityvalues”. Print Table With Pallet Unit Prices at Markets Unit Demand Price Print Table With Pallet Unit Prices fit Markets f 15 \ There Positive Demand Over Capacity, Values / No r I ' ' v ■ Total Output 1 ------------- & tPrint and Number: "Underutilized11 "Fully Utilized" "Over utilized" "Paths" [Routes or Regions or Spaces] New Plant and/or Expansion APPENDIX C PROGRAM CODE NOTATION APPENDIX C - PROGRAM CODE NOTATION NK = number of supply regions (K) NL = number of production locations (l_) NM = number of markets (M) YT = timber yield in MBF (Y) EZ = stumpage supply in cords at each supply region K TCI (K,L) = transport cost Df timber from source K to production point L MFJ (L) = manufacturing cost of pallet at a production point L TCJ (L ,M ) = transport cost of timber from production point L to market M E - total stumpage at each supply region K F = total output at each production point M Y = inventory of pallet units at plant location L lii = capacity at plant location L CliS = capacity constraint cost at plant location MR = marginal revenue of a pallet unit MC = marginal revenue of a pallet unit FD = final demand for pallets at markets M a (M)i b (M) = parameters of demand function set by forces M market QJ (L) = quantity of product shipped at production point L QJ (L,M) = quantity of product shipped from L to mar­ ket M y (M); = parameters of demand function set at N u (M) - assignment of a valid path (N) 172 L markets 173 Ii = input/output ration (1) A = raw material cost including harvest cost at supply region K MF = manufacturing cost of pallets at plant loca­ tion L SC (K) = transaction costs including stumpage and transfer costs at K sc Cl) = transaction costs including processing and transfer costs at L pj (n) = pallet unit price at market M CJ (L) » pallet unit cost at plant location L UMAX = maximum capacity at plant location L CU (L) = cost of capacity constraint at plant loca­ tion L NMAX = maximum number of valid paths L = a route or path with the widest margin for extra input/output U (L) = capacity potential of path L CU = initial capacity constraint cost at plant location L C'U = maximum capacity constraint cost at plant location L M = invalid paths KMAX = exhaustion of supply regions K LMAX = exhaustion of plant locations L MG = set number 17, 20) of markets--uhere DD = set volume of paths--uihere (DO = 5G7, 2352, 6137, 8000) KO = set number of supply regions--uhere 9* 14, IS, 20) KO = set number of production locations--tuhere (K0 = 9, 14, 19, 20) eliminated after an iteration (MD= 7, 12* (KO = 174 n - no s deviation of given number markets M from exogenously set number of markets M; M = (ID D - DO = deviation of given volumes of paths D from exogenously set volume of paths DD K - KD - deviation of given supply regions K from exogenously set number of supply regions KD; K = KO L - LO = deviation of given production locations from exogenously set number of production locations LO; L - LO APPENDIX D DETAILED FLOWCHART C START J Dimension Variables Input Formats Output Formats Read: NK, NL, NPI, YT, EZ Read: TCI (K, L) Read: NFJ (L)i W (L) CUi (L); C'liJ (L) Read: TCJ (L, PI) © 176 "Set outputs, shipments, sales to zero PJ (L) = 0 CU (L) = CU (L) QJ (M) = 0 "Calculate demand parameters" y (m), u (m) "assign identifiers to all paths "Fore each supply paint "For each Plant location" SC (L) = MFC + TCJ 1, ML "Price - Cost Equation 178 no p (n ) = pa KN (N) = K LN (N) = L MN (N) = N 10 Yes no KN(N) = K LN(rn) = L mn (m ) = n; n o = n Write 118 of paths, best paths 0 179 uAdd a Unit Output to Best Path" "Eliminate Invalid Paths" K = KN (L) L = JW (L) N = NN (L) Go To 112 1B0 112 Yea No yes yes (KO-K) (0D-0) no no (UD-L) no LO=L yes no I M = FH-L KN(M) = K LN(N) = L, MN ( o WRITE: "Sources, Plant Loca­ tion^, Markets, Pallet Unit Casts." Sales Delivered Prices PRINT: System Results "Identify Paths Suited for More Production." APPENDIX E SOURCE CODE LISTING APPENDIX E - SOURCE CODE LISTING nnononnnonnnnnnnnnn PROGRAM LOCATION FIRM - LOCATION PROGRAM PALLET INDUSTRY STUDY AUGUST, 1906 □RIGINAL LOCATION PROGRAM NO. 6 BY EDGAR HOOVER, 1966 REVISED VERSION BY ALEX OBIYA AND NILSON AMARALI 1906 T= NK NUMBER OF MARKETS B NI,NJ NUMBER OF SOURCES OR PROD. LOGS. B SUBSCRIPTS INDENT1FYING SOURCES, PROD. LOCS., MARKETS I,J,K B TM TRANSPORTATION COST ON MATERIALS FROM SOURCE, PER TON B TRANSPORTATION COST OF PRODUCT TO MARKET, PER TON TP S TONS OF MATERIAL PER TON OF PRODUCT AMI B CP COST OF PROCESSING, PER TON {CONSTANT) CAPM, CAPP = CAPACITY AT SOURCE OR PROD. LOC. U >= SI2E OF PRODUCT INCREMENT, IN TONS A,B,W,Y = PARAMETERS OF DEMAND FUNCTION D “ A “ BP DR P - W - YG EXM, EXP = EXTRA COST OF PRODUCING BEYOND INITIAL CAPACITY non DIMENSION 1 2 3 CMflO),CAPM(10),GM(10),T M (10,10),GMt10,10),C P (10), C A P P (10),QP<10),TP<10,10>,SP<10,101,0(10),W(10), YtlO),D <1000) ,IN(IOOO),JN<1000) ,KN(1000),A(10>,B<10), PtlOJ,EXM(10),EX P (10), CAPMEOO),CAPPE<10) READ DATA SET OUTPUTS, SHIPMENTS, AND SALES TO ZERO SO sv SDM CMXT CAPMT CPXT CAPPT 1A 15 = m = E tx E IB o 0 o 0 o 0 o 0 1Q2 nnn nonvi u u h w □PEN <2,FILE b ‘DATALOC') R EAD(2,*)N1,NJ,NK,U,AMI DO 2 I « 1,NI READ(2,*)CM (I),EXM{1),CAPM(I> CONTINUE DO 20001 I » 1 ,NI R E A D (2,*),J=1,NJ> 0001 CONTINUE DO 5 J = 1 ,NJ READ(2,* >GP CONTINUE DO 50001 J ■= 1 ,NJ READ(2,*> ” l,/B(K> W = A(K)/B(K> 112 CONTINUE WRITE(0,67) 67 FORMAT C ASSIGNMENT OF OUTPUT INCREMENTS',//) WRITE<0,6B) 68 FORMAT(23X,‘N ’,4X,*DMAX*,9X,*I J K ’,/> C------------------------------------------------------------------------C COMPUTE INITIAL MARGINS FOR ALL VALID PATHS, ASSIGN NUMBERS C------------------------------------------------------------------------N “ O DO 15 I ** 1,NI DO 15 J » 1,NJ DD 15 K = 1,NK DT=W -Y ) IF(DT)15,12,12 12 N “ N+l D(N) « DT IN(N) = I JN CM) KN(N> = K 15 CONTINUE C------------------------------------------------------------------------C IDENTIFY BEST PATH C------------------------------------------------------------------------10 DMAX = D(l) NMAX = 1 DO 16 L » 2 ,N IF(DCL)-DMAX)16,16,18 18 DMAX « D1L) NMAX « L 16 CONTINUE M b NMAX 17 I b IN (M) J b JN+U*AMI-CAPME(I))22,22,21 IA = I QP(J) = O P (J }+U IF IF(K-KA>76,77,76 77 DT D(LI-Y 401 IF (J - JA) 124,403,124 403 DT » DT - EXP(J) 124 IF (DT) 20,51,51 C----C COUNT VALID PATHS, RENUMBER C C C C c-----------------------------------------------------------------c 51 20 M “ M + 1 D(M> = DT IN(M> = I JN((1) = J KN(M) « K CONTINUE N = M _ _ —_________________ C RELEASE CAPACITY CONSTRAINTS C C-------------------------------------------------------------------------- C IF (IA) 500,500,501 501 CAPME(IA) = 100.«CAPME(1A) IA * 0 300 IF (JA) 502,502,503 503 CAPFE(JA) = 100.«CAPPE(JA) JA - O c-----------------------------------------------------------------c C WHEN ONE PATH REMAINS, ASSIGN FINAL OUTPUT INCREMENT C 165 nnn nnn C-------------------------------------------------------------------------- C 502 IF CAPMT - CAPMT + C APM(I) CMX = CAPM(I) - QM (I) IF )30,30,331 331 ’WRITE(0,B6) 86 FORMAT tlHO) DO 3030 J = 1,NJ IF(SM(I,J))3030,3030,26 28 WRITE(0,29)J,5MCI,J) 29 FORMAT(5X,' SHIPMENTS TO P.L. = ',I2,F9.0> 3030 CONTINUE 30 CONTINUE DO 31 J « 1,NJ C C PRINT RESULTS FOR PRDD.LOCS. AND PRODUCT SHIPMENT) C WRITE(0,32 32 FORMAT C ',//,* PRODUCTION LOCATION *= I2,/> WRITE(0,25)CAPP(J) WRITE(O ,26)O P (J ) SQ b SQ + QP(J) CAPPT “ CAPPT + CAPP(J ) CPX *> CAPP(J) - QP(J) IF (CPX) 1B1,182,1B0 180 CPXT - CPXT + CPX WRITE(0,83)CPX BO TO 230 1B2 WRITE(0,84) )0 186 GO TO 230 WRITE(0,84) GROW - -CPX WRITE(0,05)GROW 230 IF (QP(J)> 31,31,332 332 WRITE(0,86) DO 3131 K = 1,NK IF *Q(K) SV = SV + P (K) *□ 1B1 c C C 37 38 36 39 C C C 150 151 158 159 160 152 153 154 155 156 PRINT RESULTS FOR SALES AND DELIVERED PRICES WRITE(0,37)K FORMAT (' *,4(/),15X, *MARKET = ‘,12,/) WRITE(0,38)0(K) FORMAT (20X,* SALES,TONS «*, f b .O) WRITE(00,39)P(K) CONTINUE FORMAT(20X,' PRICE PER TON «',FB.2,//) Z * SWSQ PRINT SYSTEM RESULTS WRITE(O,150) FORMAT (' ’,10X,' TOTALS FOR SYSTEM',///) WRITE(O,151> FORMAT <15X, ’ SDURCE CAPACITY'> WRITE(0,158)CAPMT FORMAT tlOX,' INITIAL = ',FB,0) WRITE(O,159)EXCM FORMAT (I6X,* EXPANDED “ *,FB.O) WRITE(O,160)POO FORMAT (23X,' (ENOUGH F0R',F8.0, * TONS OF PRODUCT)',/) WRITE(0,152)SQM FORMAT(15X, ’ SOURCE OUTPUT =*,FB.O,/) WRITE(O,153)CMXT FORMAT (15X,' CAPACITY UNUSED “ ',F6.0,5(/)> WRITEtO,154) FORMAT (15X,' PROCESSING CAPACITY*) WRITE(0,15B)CAPPT WRITEtO,159)EXCP WRITE(O,155)SO FORMAT (/15X,* PROCESSING CAPACITY «',F6.0,/) WRITE(0,1S3)CPXT WRITE(0,156)SQ FORMAT (15X,' TOTAL SALES »*,F6.0) 1B7 nnn )Z WRITE(0,157 157 FORMAT <15X,' AVE. DEL. PRICE =*,FS.2) C---------------------------------------------------------C CALCULATION OF TOTAL C05T6 AND RENTS C---------------------------------------------------------COST b O DO 170 1 = ltNI COST •= COST + C M (1) * Q M U ) IF(QM(IJ —CAPM11))172,172,171 171 COST b COST+EXM<11*(QM 11)-CAPM(I)) 172 DO 170 J “ 1,NJ COST » COST + TMtI,J>*SM(I,J) 170 CONTINUE DO 173 J » 1,NJ COST - COST + CP(J)*QP(J) IF(OPtJ> —CAPP(J ))175,175,174 174 COST b COST+EXP(J)*(QP(J) - CAPPtJ)) 175 DO 173 1,NK COST b COST + TP,' VALUE OF OUTPUT «*,FB.2> 47 FORMAT t* TOTAL COST b '(f b .2> 4B FORMAT f RENT -',FB.2> 49 FORMAT (3X,'t *, F3.2,* PERCENT OF OUTPUT VALUE)') END APPENDIX F COMPILED DATA FOR THE MODEL APPENDIX F - COMPILED DATA FOR THE MODEL The supply and production locations are represented by identifiers (two-letter variables)* spelt out by name. Whereas markets are There are 9 supply regions, 9 production centers, and 7 major market centers (see Table F-l). In a matrix format these add to 567 possible routes that one pallet capacity unit (load) could travel through in a given year from rau material source to a given market to satisfy equivalent capacity unit demand. To reiterate, the basic purpose of the firm-location model is to determine which routes (spaces) need extra produc­ tion plant or whether the production output could be expanded to meet capacity potential of each firm (plant). For each path (space), the total costs of manufacturing and delivering a pallet unit are compared with market price when sales in that unit are just one unit: V « A + TC + MF = D V = raw material/product A = cost of raw material MF = manufacturing/production cost including labor cost and normal profit margin D = demand price of pallet unit at market TC = transport cost--access to raw material plus movement to markets (harvest cost) A pallet capacity unit (load) in this model is equivalent to production output of a typical Michigan pallet plant (196 166 firms) which manufactures 68,000 pallets (19 board feet each) or processes about 1.3 million board feet of lumber as raw material in a given year. Hence a cited pallet capacity unit represents the minimum amount of output or raw material flow in a given route that can sustain a typical pallet firm in business for a year (capacity unit/route/year). Production figures are summarized on a yearly basis detailing business expenditures for every essential item such as transportation, harvest, labor costs, (for convenience) etc. Market prices too are calculated for the whole capacity unit. to serve as inputs for the computer program, are further broken down to a per ton basis, input or output per ton per route. In order these figures e.g. price of As a result of this, the time frame is also comparatively reduced from a yearly basis to a working hourly basis e.g. price of input/output per route per hour (ton/route/hour). D-PRODUCTION COSTS AT PLANT (FIRM) LOCATIONS The equation below shows how production cost is arrived at by adding raw material cost (A) and manufacturing cost (MF)« One should notice that these costs are assumed constant throughout the study area. On per capacity unit basis: A $260,000 + + B $325,000 PROD. COST = $505,ODD/capacity unit/route/year 190 □n per ton basis: $ A + 37.00 + E-TRANSFER B $ (SHIPMENT) PROD. 4(6.25 = $ COST B3.25/ton/route/hour COSTS - Matrix computation formula Transport costs are estimated for the entire network, i.e. from supply regions through the production zones ulti­ mately to the markets. Final figures are shown in Tables F-2, F-3, F-4, and F-5. (i) supply region to production location $/one capacity unit (c o r d )/route/year: 9.34 For example, + 0.0579 (miles) shipping timber or logs from Montcalm County to a production plant in Ingham County: 9.34 + 0.0579 (60 miles * 1371 cords = $17571.90 On per ton basis: This is equivalent to $ 2.50/ton/route/hour (ii) production location to market pallet transfer--alloii)ing for 205t waste in trans­ formation process. $/one capacity unit (m b f )/route/year: 10.25 e.g., + 0.14 (miles) transporting pallets from St. Clair County to Detroit: 10.25 + 0.14 (56 miles) * 1.3MBF = $ IB,013.6 On per ton basis; This is equivalent to $3.34/ton/route/hour 191 F-DEMflND PRICE AT MARKETS - constant figure Pallet price is assessed at □.50/board foot based on earlier stipulations in Chapter V. Based on the basis of one capacity unit that a typical plant would sell in a given market, 1.3 MBF market price becomes: 0.50/board foot = $650,000/capacity unit On per ton basis: This is equivalent to $92.50/ton G-CflPflCITY ALTERNATIVES Plant capacity is one key variable that affects program results. terms; The capacity value can be expressed in different either in physical units (weight or volume) of output processed at a given time period (hour, day, week, or year). month The maximum capacity of the small pallet plant applicable to this model does not exceed 500 units per 8hour day in a 5 day-work week. As a reminder, 1 pallet unit contains 19 board feet of uiood and 185 board feet is approxi­ mately equal to 1 ton of lumber. However the results are tested under varying capacity estimates i.e. shocking the computer program exogenously with different capacity values: A - 25% B - 50* C - 75* D - 1D0* 192 Capacity value estimates (units or weights) for different time periods are shown in Table G-l. H-5ENSITIUITY ANALYSIS Further testing and verification of the model takes the form of testing sensitivity to spatial and temporal dimen­ sions. (i) varying timber supply sources and timber producti­ vity in periods between 19GG and 2010. (periods 19B0, 1905, 1995 and 2D10 as inputs) (ii) rate of capital and technological turnover (□ (iii) - 40 years) general sensitivity analysis (response to changes in parameters) Table G-l also represents mathematically calculated solutions which can be compared for consistency and practical­ ity to the computer results of the firm-location model. 193 TABLE F-l SYMBOL DESCRIPTORS FOR SUPPLY, PRODUCTION AND MARKET LOCATION INCLUDED COUNTIES IN A SUPPLY REGION Allegan-barry-Kent Otsego-Montgomery-Alpena Crawford-Oscoda-Alcona Roscommon-Ogemaw-Iosco Clare-Gladwin-Midland Osceola-Macosta-Montcalm Grand Traverse-Leelanau-Benzie Manistee-Mason-Osceana-Muskagon Charleuoix-Antrim-Kalkaska-Missaukee INCLUDED COUNTIES IN A PRODUCTION ZONE Berrien-Van Buren-Cass Muskegon-Ottawa-Kent Dceana-Newygo-Montcalm-Mason Oscoda-Alpena-Iosco-Arena-Ogemaw Clare-Midland Jackson-Ingham St. Clair-Macomb-Oakland-Uayne Monroe-Lenawee Cheboygan-Otsego INCLUDED CITIES IN A MARKET LOCUS Jackson Lansing-East Lansing Flint Muskegon-Grand Rapids Ann Arbor-Detroit-Toledo Kalamazoo-Battie Creek Midland-Bay City-Saginaw SYMBOLS (model-program) AB DM CO RO CG OM GL MM CA 1 2 3 4 5 G 7 a 9 SYMBOLS (model-program) B \l MO ON OA CM JI SM ML CO 1 2 3 4 5 6 7 8 9 SYMBOLS (model-program) JAC LAN FLI MUS DET KAL SAG 1 2 3 4 5 6 7 194 TABLE F-2 DISTANCES IN MILES BETWEEN SUPPLY SOURCES AND PRODUCTION LOCATIONS PRODUCTION LOCATIONS 1 BU 2 MO 3 ON 4 OA 5 CM 6 JI 7 SM 8 ML 9 CO SUPPLY SOURCES 1 AB 55 29 83 148 165 89 186 83 271 2 QM 2B7 227 158 146 89 181 228 214 75 3 CD 252 212 143 94 71 162 176 192 59 4 RO 185 145 78 57 36 97 137 134 115 5 CG 217 178 108 58 32 125 140 155 98 6 OZ 123 80 68 119 141 134 224 168 222 7 GL 146 103 77 135 92 158 218 191 194 8 MM 235 195 125 149 92 183 231 216 113 9 CA 100 56 59 141 140 112 208 145 245 SOURCE: Michigan Department of State Highways and Transporta­ tion. 1980. TABLE F-3 DISTANCES IN MILES BETWEEN PRODUCTION LOCATIONS AND MARKETS MARKETS 1 3 4 LANSING FLINT MUSKEGON/ GD. RAPIDS 2 JACKSON 5 ANN ARBOR/ DETROIT/ TOLEDO 6 KALAMAZOO/ BT. CREEK 7 MIDLAND/ BAY CITY/ SAGINAW PRODUCTION LOCATIONS 1 81/ 97 104 148 78 160 58 177 2 MO 106 80 123 32 153 63 104 3 ON 132 99 130 59 172 100 141 4 OA 99 68 33 124 86 122 91 5 CM 138 105 107 142 161 157 17 6 JI 37 5 47 74 77 56 78 7 SM 116 100 56 170 56 154 106 0 ML 5 37 77 108 71 50 117 9 CO 221 190 152 247 206 244 124 r SOURCE: Michigan Department of State Highways and Transportation. 19B0. 196 TABLE F-4 TRANSPORTATION COSTS ($) PER TON OF PALLETS FROM SUPPLY SOURCES TO PRODUCTION LOCATIONS PRODUCTION LOCATIONS 1 BU 2 MO 3 ON 4 OA 5 CM 6 JI 7 SM B ML 9 CO SUPPLY SOURCES 1 AB 2.44 2.15 2.76 3.49 3.69 2.83 3.92 2.76 4.88 2 OM 4.68 4.39 3.61 3.47 2.B7 3,87 4.40 4.24 2.67 3 CO 4.67 4.22 3.44 2.88 2.62 3. 65 3.81 3.99 2.49 4 RO 3.91 3.46 2.68 2.45 2.23 2.92 3.37 3.34 3.12 5 CG 4.27 3.83 3.04 2.47 2.10 3.23 3.40 3.57 2.92 6 02 3.21 2.73 2.59 3.16 3.41 3.34 4.35 3.72 4.33 7 GL 3.47 2.99 2.69 3.35 2.86 3.61 4.20 3.90 4.01 B MM 4.48 4.03 3.23 3.51 2.86 3.89 4.43 4.26 3.10 9 CA 2.95 2.45 2.49 3,41 3.40 3.09 4.17 3.46 4.69 SOURCE: DenUyl* R. Q. and Associates. 1902. TABLE F-5 TRANSPORTATION COSTS ($) PER TON OF PALLETS FROM PRODUCTION LOCATIONS TO MARKETS MARKETS 1 JACKSON 3 4 LANSING FLINT MUSKEGON/ GO. RAPIDS 2 5 ANN ARBOR/ DETROIT/ TOLEDO 6 KALAMAZOO/ BT. CREEK 7 MIDLAND/ BAY CITY/ SAGINAW PRODUCTION LOCATIONS 1 BV 4.41 4.59 5.73 3.80 6.04 3.39 6.48 2 MO 4.64 3.97 5.08 2.73 5.86 3.53 6.65 3 ON 5.32 4.46 5.26 3.42 6.35 4.47 5.56 4 OA 4.41 3.66 2 .75 5.10 4.12 5.06 4,24 5 CM 5.47 4.62 4.67 5.56 6.07 5.97 2.34 S JI 2.85 2.03 3.11 3.81 3.89 3.35 3.93 7 SM 4.90 4.49 3.35 6.30 3.35 5.89 4.63 8 ML 2.03 2.B5 3.89 4.68 3.74 3.19 4.93 9 CO 7.62 6.B2 5.83 8.23 7.23 8.22 5.08 SOURCE: DenUyl, R. B. and Associates. 1982. TABLE G-l PRODUCTION OUTPUT ESTIMATES UNDER DIFFERENT PHYSICAL CAPACITY VALUES FOR A TYPICAL PALLET PLANT AT A GIVEN TIME PERIOD 25 CAPACITY VALUE (PERCENTAGES) 50 75 100 HOUR units (pallets) weights (tons) board feet 16 1.61 297.85 31 3.21 593.05 47 4.82 091.70 63 6,42 1107.70 DAY units (pallets) Weights (tons) board feet 125 12.84 2375.4 250 25.68 4750.BO 375 3B.52 7126.20 500 51.36 9501.60 WEEK units (pallets) weights (tons) board feet 625 64.20 11,877.00 1250 120.40 23,754.00 1B75 192.60 35,631.00 2588 256.BO 49.173.00 MONTH units (pallets) weights (tons) board feet 2500 256.BO 48,50B.Q0 5000 513.60 95,016.00 7501 770.40 142,524.00 10,001 1027.20 190,032.00 YEAR units (pallets) weights (tons) board feet 30,005 3001.60 570,096.00 60,010 6163.20 1,140,192.00 90,015 9244.80 1,710,288.00 120,020 12,326.40 2,280,304.00 LITERATURE CITED LITERATURE CITED Airov, Joseph. 1959. The Location of the Synthetic-Fiber Industry: A case study in regional analysis. 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