EFFICIENT ORGANIZATION OF THE LIVESTOCK-MEAT MARKETING SYSTEM IN EASTERN MACEDONIA. GREECE Dissertation for the Degree of Ph. D. MICHIGAN STATE UNIVERSITY CHRISTOS THEGCHARIS KAMENIDIS 1974 _ ._AA—————_- "Hn‘ oil-.1 ”J LIBRARY Michigan State University f - This is to certify that the thesis entitled EFFICIENT ORGANIZATION OF THE LIVESTOCK-MEAT MARKETING SYSTEM IN EASTERN MACEDONIA, GREECE presented by Christos Theocharis Kamenidis has been accepted towards fulfillment of the requirements for ph - IL degree in Jgnicnltural Economics Date /////$///974 0-7639 ‘7 . ":fi‘ HUAG & SDNS' 800K ENDERYIMC.w ABSTRACT EFFICIENT ORGANIZATION OF THE LIVESTOCK-MEAT MARKETING SYSTEM IN EASTERN MACEDONIA, GREECE By Christos Theocharis Kamenidis The significant rise in per capita income of the Greek peo- ple coupled with remarkable growth of foreign tourism in Greece has led to a substantial increase of total meat consumption in the country. In order to reduce meat imports, and therefore the foreign exchange outflow, the Government has taken a series of measures, such as higher output prices and input subsidies, more credit to producers with very low interest rates, etc. As a result of this policy, some larger pro- ducers have entered the livestock industry while most of the existing livestock producers have expanded their operations. Thus, livestock production is expected to increase appreciably by l980. 0n the other hand, existing slaughterhouses are relatively many, small and technologically out of date. Their buildings are gen- erally old and poorly equipped. They still employ crude methods of livestock slaughtering. They do not process livestock by-products be- cause their small volumes make it unprofitable. The aforementioned factors may necessitate the establishment of new slaughter plants and systems. If new investment occurs, then Christos Theocharis Kamenidis the main questions which might be raised include: What should be the optimum number, size, and location of new slaughter plants in E. Mace- donia, so that the aggregate cost of livestock assembly, processing and meat distribution be minimized and thus the efficiency of the livestock-meat marketing system be improved? To undertake the empirical analysis, a linear programming transhipment model was employed. The computer program used was the APEX-I. The basic data needed for this computer analysis were: (1) Regional livestock supplies; (2) Regional meat consumption; (3) Live— stock assembly cost per unit of product between all the supply regions and all the plant locations; (4) Livestock slaughtering unit cost by plant sizes and by levels of capacity utilization; and (5) Meat dis- tribution cost per unit of product between all the plant locations and all the consumption centers. Six alternative solution models were constructed and tested in order to find out what might be the impact of changing the corres- ponding variable--characterizing each model--upon the optimal solution of the basic model. The characteristics of the basic model are: (l) 1972 livestock supplies; (2) 50 percent capacity utilization of trucks engaged in livestock assembly; (3) full capacity utilization of slaugh- tering plants; (4) use of modern technology in livestock slaughtering; and (5) 20 supply regions, 21 consumption centers and 10 potential plant sites. Model II differs from the basic one in assuming full ca- pacity utilization of the trucks engaged in livestock assembly. Model III assumes l4 supply regions, 15 consumption regions and 8 potential Christos Theocharis Kamenidis plant sites. Model IV assumes 1980 livestock supplies; Model V as- sumes 90 percent plant capacity utilization; and Model VI assumes con- tinuation of the currently existing livestock slaughtering system. The empirical analysis has shown that whenever a modern live- stock slaughtering system was assumed--as is the case in all models ex- cept model VI--the optimum solution ended up with either two plants (models: Basic, II, III and optimal solution of model IV) or three plants (second optimal solution of model IV and optimal solution of model V). When the optimum number of plants is two, then the optimum plant locations are either Serres and Kavala (when 1972 livestock sup- plies are assumed) or Serres and Drama (when 1980 supplies are assumed). When the optimum number of plants is three, then the optimum plant 10- cations are Serres, Kavala and Drama. The major questions which arise next are: (1) Should new slaughtering plants using modern technologies by established in E. Macedonia, Greece, or should the current system continue? (2) If mod- ern slaughtering technology is to be introduced, should two or three plants be built? The trade-offs (advantages and disadvantages) of the alternative solutions will determine which course of action should be adopted. If two or three new slaughtering plants using a modern tech- nology were established, then some probable advantages over the old system of 21 slaughterhouses would be: (1) concentration of larger amounts of livestock by-products at the plant locations, which in turn may make their processing profitable; (2) increased efficiency of the livestock-meat marketing system; (3) improvement in meat quality; Christos Theocharis Kamenidis (4) economies of size in the veterinary inspection of slaughtered ani- mals. Some probable disadvantages of the proposed new slaughtering system over the existing one would be: (1) reduction in the employ- ment of slaughterers as a result of substitution of capital for labor; (2) loss of revenues for the communities whose slaughterhouses will be closed; (3) problems of disposing larger amounts of waste. If three plants (i.e., one in each province of E. Macedonia) were established rather than two, a more equitable pattern of regional economic development would result. However, a system of three plants would have a higher total cost than one of two plants, given the same total output and input price structure. Given these benefits and costs for all the alternative solu- tions, it is the task of policy makers to make the final decision. EFFICIENT ORGANIZATION OF THE LIVESTOCK-MEAT MARKETING SYSTEM IN EASTERN MACEDONIA, GREECE By Christos Theocharis Kamenidis A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics 1974 This doctoral dissertation is dedicated to my parents, Theocharis and Irene and to my wife, Katherine. ii ACKNOWLEDGMENTS The author wishes to express his sincere appreciation and heartfelt thanks to Dr. Vernon L. Sorenson, chairman of his guidance and thesis committees as well as to Dr. Lester V. Mandersheid, co- chairman of the thesis committee and Dr. James D. Shaffer, co-chairman of the guidance committee for their invaluable counseling, useful sug- gestions, constructive criticisms and warm friendship throughout his doctoral course program and thesis preparation. Appreciation is extended to Dr. Stephen Harsh, member of the thesis committee, for his contribution to the matrix formulation, and to Doctors Byron W. Brown and Donald S. Henley, members of the guidance committee, for their helpful and friendly advice. Acknowledgments are also due to Dr. Harold M. Riley, chair- man of the Agricultural Economics Department, and through him all the other faculty members for providing me with a research assistantship which greatly facilitated the completion of my course program and the- sis research, and for enriching my knowledge in the Economics of Agri- culture. A special appreciation is expressed to professors Harold F. Breimyer, V. James Rhodes, Charles L. Cramer, Joseph C. Headley, Robert M. Finley and Curtis H. Braschler, for their valuable counsel— ing, encouragement, and friendship during my M.S. studies at the University of Missouri-Columbia. The same can be said for Mr. K. E. Hunt, Director and Godfrey Tyler, my tutor, during my Diploma graduate studies in the Institute of Agricultural Economics, University of Oxford, England. I am especially grateful to my professors at home, Univer- sity of Thessaloniki, Greece, Doctors Euthymios Papageorgiou, George Kitsopanidis and Anthony Adamopoulos for their useful advices, friendly cooperation, and great support throughout my graduate studies. Many thanks are due to the National Research Institute of Greece for partial financing this research, to L. Ananikas for the con- structive cooperation in this common project, to Dr. C. Papageorgiou, Dr. S. Mariadis, C. Miliotis, V. Kalaitzis, H. Koutoglidis, L. Kazakopoulos, J. Karpazis, C. Nousias, C. Salpistis, P. Spanidis, K. Theocharidis, C. Hourmouziadis, P. Patsis and other friends and col- leagues of mine for continuously providing me with useful information during the development of my thesis. Appreciation is expressed to Dr. Lawrence E. Ziewacz and Preston Pattie for refining the English in my thesis; also to Katherine Ely for her assistance in the computer analysis. Finally, a debt of gratitude is due my wife, Katherine, for her incredible patience, absolute understanding, strong encouragement and for assuming the financial burden of our family's care throughout the entirety of my graduate studies. My daughter Irene, and my son Theocharis deserve mention here for their loving devotion. Not to be forgotten are my parents, Theocharis and Irene, my brother Theodoros, and my sisters, Martha and Meropi. Their sacrifices, encouragement and support undoubtedly have played a significant role in the successful completion of my undergraduate and graduate education. iv TABLE OF CONTENTS CHAPTER Page I. RESEARCH OBJECTIVES AND BACKGROUND ECONOMIC INFORMATION ...................... 1 Introduction ..................... 1 The Research Problem ................. 6 The Analysis Objectives ............... . 7 The Area of Study ................... 9 The Sources of Data .................. 11 Summary ........................ 13 II. THE PRESENT LIVESTOCK AND MEAT MARKETING SYSTEM IN E. MACEDONIA, GREECE ................ 15 Introduction ..................... 15 Marketing Channels for Livestock and Meat ....... 16 Livestock Production ................. 22 Livestock Assembly .................. 26 Livestock Slaughtering ................ 29 Meat Transportation and Distribution ......... 36 Meat Wholesaling ................... 37 Meat Retailing .................... 40 Livestock and Meat Price and Trade Policies ...... 44 Summary ........................ 46 III. THEORETICAL CONSIDERATIONS AND METHODOLOGICAL PROCEDURES ...................... 50 Introduction ..................... 50 The Economic Model .................. ‘ 50 The Mathematical Model ................ ”'53 The Computer Model .................. " 54 The Analytical Procedure ............... 60 Alternative Solution Models ............. , 68 Feasibility Assumptions ................ 69 Summary ....................... a 71 IV. ESTIMATION OF REGIONAL LIVESTOCK SUPPLY AND MEAT CONSUMPTION, LIVESTOCK ASSEMBLY AND PROCESSING. AND MEAT DISTRIBUTION COSTS ............. i . 73 Introduction . . . . . . . . . . . . . . . . . . . . . 73 Estimation and Projections of Regional Livestock Supplies ................. 74 Estimation Of Regional Meet Consumption ....... 76 Estimation of Livestock Assembly Cost ........ 79 Estimation of.the.Livestock Processing Cost ..... 83 Estimation of Meat Distribution Cost ........ 90 Summary ....................... 92 V. NUMBER, SIZE AND LOCATION OF LIVESTOCK SLAUGHTERING PLANTS ................. 95 Introduction .................... 95 The Basic Solution Model .............. 96 The Alternative Solution Model II ......... 103 The Alternative Solution Model III ......... 105 The Alternative Solution Model IV .......... 112 The Alternative Solution Model V .......... 120 The Alternative Solution Model VI .......... 126 Summary ....................... 133 v1. SUMMARY. IMPLICATIONS. LIMITATIONS AND NEEDED RESEARCH ................... 136 Summary ....................... 136 Implications .................... 144 Limitations ..................... 154 Needed Research ................... 157 BIBLIOGRAPHY ......................... 151 APPENDICES .......................... 169 vi Table I-l. I-2. III-l. IV-1. IV-2. IV-3. IV-4. IV-5. IV-6. LIST OF TABLES Population, GNP, CPI, per capita income, foreign tourism, imports-exports and balance Of trade, Greece, 1958-1972 ................. Per capita meat consumption in kilograms, and total meat consumption, production and imports in thousand metric tons, Greece, 1958-1972 ...... Livestock farm sizes, Greece, 1971 ......... Slaughterhouses currently existing in E. Macedonia, Greece with the corresponding volume of slaughter- ings by livestock species, 1972 .......... Monthly livestock slaughterings in metric tons of carcass meat in all the slaughterhouses Of the province of Serres, E. Macedonia, Greece, 1972 Matrix format of the transhipment model under the linear programming formulation .......... Livestock slaughterings in number of head and in metric tons of carcass meat, E. Macedonia, Greece, 1972 ................... Regional meat consumption in metric tons, E. Macedonia, Greece, 1972 .............. Livestock assembly cost rates in terms of number Of head, E. Macedonia, Greece, June, 1974 ..... Livestock assembly cost rates in terms of meat equivalents, E. Macedonia, Greece, June, 1974 . . . . Cost of livestock slaughtering by sizes of plants and at full capacity utilization, E. Macedonia, Greece ...................... Meat transportation cost rates in Drachmae per metric ton of carcass meat, E. Macedonia, Greece, June, 1974 .................... vii Page 24 34 35 57 75 78 81 82 Table V-1. V-2. V—3. V-4. V-5. V-6. V-8. V-9. Flow of live animals from the production regions to the slaughterhouses, E. Macedonia, Greece, 1972 (Volume measured in metric tons Of carcass meat): Basic model ................. Flow of carcass meat in metric tons from the slaugh- terhouses to the consumption centers, E. Macedonia, Greece, 1972: Basic model ............. Flow of live animals from the production regions to the slaughterhouses, E. Macedonia, Greece, 1972 (Volume measured in metric tons of carcass meat): Model II . . .................... Flow of carcass meat in metric tons from the slaugh- terhouses to the consumption centers, E. Macedonia, Greece, 1972: Model II ............... Flow Of live animals from the production regions to the slaughterhouses, E. Macedonia, Greece, 1972 (Volume measured in metric tons of carcass meat): Model III ...................... Flow of carcass meat in metric tons from the slaugh- terhouses to the consumption centers, E. Macedonia, Greece, 1972: Model III .............. Flow of live animals from the production regions to the slaughterhouses, E. Macedonia, Greece, 1980 (Volume measured in metric tons Of carcass meat): Model IV--0ptimum solution ............. Flow of carcass meat in metric tons from the slaugh— terhouses to the consumption centers, E. Macedonia, Greece, 1980: Model IV--0ptimum solution ...... Flow of live animals from the production regions to the slaughterhouses, E. Macedonia, Greece, 1980 (Volume measured in metric tons of carcass meat): Model IV--Second best solution ........... Flow of carcass meat in metric tons from the slaugh- terhouses to the consumption centers, E. Macedonia, Greece, 1980: Model IV--Second best solution . . . . Flow of live animals from the production regions to the slaughterhouses, E. Macedonia, Greece, 1980 (Volume measured in metric tons of carcass meat): Model V ....................... viii Page 107 116 121 124 Table Page V-12. Flow of carcass meat in metric tons from the slaugh- terhouses to the consumption centers, E. Macedonia, Greece, 1980: Model V ............... 125 V-13. Flow of live animals from the production regions to the slaughterhouses, E. Macedonia, Greece, 1972 (Volume measured in metric tons of carcass meat): Model VI ...................... 128 V-14. Flow of carcass meat in metric tons from the slaugh- terhouses to the consumption centers, E. Macedonia, Greece, 1972: Model VI ............... 129 V-15. Flow of live animals from the production regions to the slaughterhouses, E. Macedonia, Greece, 1972 (Volume measured in metric tons Of carcass meat), based upon only assembly and distribution costs . . . 130 V-16. Flow of carcass meat in metric tons from the Slaugh- terhouses tO the consumption centers, E. Macedonia, Greece, 1972, based upon only assembly and distri- bution costs .................... 131 VI-l. The characteristics of the various alternative solu- tion models ..................... 141 VI-2. Main research findings for each alternative solu- tion model ..................... 142 A-3. Distance matrix in kilometers, East Macedonia, Greece ....................... 177 A-4 Estimation of per capita meat consumption by the non-urban population Of each province in E. Macedonia, Greece, 1972 ..... . ......... 178 A-5 Slaughtering cost by plant sizes and by different levels of plant capacity utilization, E. Macedonia, Greece .................. 179 ix Figure I-1. II-1. III-1. III-2. IV-1. V-l. V-2. V-3. LIST OF FIGURES Map of Greece ..................... Marketing channels for livestock and meat, E. Macedonia, Greece, 1974 ............... Determination of the optimum number Of plants on the basis of the total (processing and transpor- tation) costs .................... Map of E. Macedonia, Greece .............. Short-run cost curves of seven different sizes Of livestock Slaughtering plants, E. Macedonia, Greece ....................... Flow of live animals from the supply regions to the slaughterhouses and flow Of carcass meat from the slaughterhouses to the consumption centers: Model I ....................... Flow of live animals from the supply regions to the slaughterhouses and flow Of carcass meat from the slaughterhouses to the consumption centers: Model IV--Optimum solution ............. Flow of live animals from the supply regions to the slaughterhouses and flow of carcass meat from the slaughterhouses to the consumption centers: Model IV--Second best solution ........... Page 10 18 52 62 104 CHAPTER I RESEARCH OBJECTIVES AND BACKGROUND ECONOMIC INFORMATION Introduction Greece has accomplished a notably rapid economic growth and development over the period of 1958 to 1972. Gross National Product (GNP) at constant (1958) prices--used here as a measure of economic I in 1958 to 262.1 growth--has increased from 94.8 billion drachmae billion drachmae in 1972 (Table I-l). This GNP rise reflects an an- nual growth rate Of real output by about eight percent on the average. The remarkable expansion in GNP coupled with a very low population growth--less than 0.5 percent annually on the average (Table I-1)--has contributed to a substantial increase in per capita income of Greeks. From 329 dollars in 1958 (current prices), per capita income of Greeks-~measured here in terms of Net National In- come (NNI)-4has increased to 1,129 dollars in 1972 (Table I-l). This income does not differ much from that in constant prices, since in- flation--measured in terms Of Consumer Price Index (CPI)--was insig- nificant. 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L H _ _ - n.0um .mdmuon I u .mudmuswummu .u.mv defiumazm AI u 1600 ummz mo A L h mdoauauwude I I . I I A L - Amuusouamv mumuuomaH _ mHOHHMUMM mHUHMmOHO§ mHOHMmQHog A Mooum0>HA J ummz wuommwwoum Iwwwm new: # van uwmz A z z - r muoabmaou _ mumamma Hmava>fivaH . v v “ xuoumm>HA mumosvoum v v d F Hmuoq xuoumm>aq b , V I . A In . )l/l/lf . . am: — mumopvoum c t)! .MIIv scammwaaoo —xuoumm>HA - ._ a J , mum um: um a fill MW ummmmuoum mo u z mwuoum Hana uwanam Tom Hmauumm .r 19 After the live animals have been slaughtered, carcass meat moves to its final destination (individual consumers or institutions of meat consumption) through one of the following ways: a. butchers--consumers; b. semi-wholesalers--consumers; c. semi-wholesalers--butchers--consumers; d. meat wholesalers--butchers--consumers; e. meat wholesalers--public meat markets--consumers; f. meat wholesalers--meat semi-wholesalers--butchers--consumers. Again neither in this case is there any research information with regard to the volume Of carcass meat moving through these chan- nels. However, from the unsystematic information available, it seems that route (a) prevails in villages and small towns while routes (d), (e), and (f) prevail in cities and large towns. The nature and the role of marketing channels for both live- stock and meat are described in brief in the immediately following sections. Commission men or "animal traders," as they are called in Greece, are usually successful farmers (leaders) in each community. They are working for someone's account, e.g. butchers', wholesalers', etc. They usually collect the necessary information regarding the availability of animals for sale, etc. and sometimes negotiate the price with farmers. They are authorized to offer either the final price or a minimum price. In the latter case, whoever does the as- sembly Of live animals Offers the final price after he has visited the place Of transaction and inspected the animals. Commission men 20 receive commission fees for the job which they perform. These vary for the different livestock species. In the provinces of E. Macedonia, the currently held commission fees are: for cattle, lOO drachmae per head; for hogs, 50 drachmae per head; and for lambs, 5 drachmae per head.12 Local livestock dealers are specialized marketing firms who both buy and sell live animals. They buy either directly from farmers, or usually through their commission men. They generally work for meat wholesalers of big cities and sometimes for semi-wholesalers, butchers, and meat processors. They supply them with either carcass meat (fre- quently) or with live animals (rarely). In the first case they take care of slaughtering while in the second case the latter (i.e. meat wholesalers, etc.) do. Meat wholesalers buy large amounts of meat, store it in their warehouses and then sell it to the various marketing firms, such as butchers, semi-wholesalers, public meat markets and meat processors. They also provide meat to some large meat consumption institutions, such as hospitals, taverns and restaurants. They usually buy from either local dealers (meat or live animals) and commission men or from meat importers (imported meat). When they buy live animals, either themselves or usually their personnel take care of slaughtering. Meat semi-wholesalers are between butchers and wholesalers in the marketing system. They buy either from meat wholesalers (big 12Data provided by three interviewed commission men in Serres and Drama. 21 cities) or farmers, commission men and local dealers (towns or vil- lages) and sell simultaneously to both, butchers and meat consumption institutions (hospitals, restaurants, hotels, etc.) as well as to individual consumers. Butchers, are specialized meat retailers. They sell directly to individual consumers and rarely to institutions. The latter applies to towns and villages where wholesalers do not exist. They buy from either meat wholesalers or semi-wholesalers (as usually happens in cities), or from commission men and local dealers or directly from farmers (as usually happens in towns and villages). Public meat markets are city or large town areas in which many meat retailers are concentrated. Meat displays outside of their store, non-permanent customers and comparatively lower prices than butchers are their common characteristics. They base their profits on volume Of sales rather than on sale price. The competition among them is very keen. They buy meat from meat wholesalers and sell to both individual consumers and institutions (e.g. restaurants, etc.) Meat processors buy meat and process it to the various meat products, such as sausage, salami, etc. They buy either from meat wholesalers or from commission men and livestock local dealers. They sell directly to the grocery stores or to sausage stores, spe- cialized small retail stores, which sell only sausages, salami and other ready-tO-eat meat products. Importers are specialized in either live animal imports or fresh and/or frozen meat imports. They sell directly to meat whole- salers. When they import live animals for meat, they take them to the nearest Greek slaughterhouse, slaughter them and sell the carcass meat to meat wholesalers. 22 Livestock Production UBecause the marketing process starts with the product as it is offered at the farm, the conditions surrounding this product and its production are important in understanding many of the problems and costs of agricultural marketing."13 This implies that the produc- tion and marketing Of livestock products must be synchronized; other- wise inefficiencies shall be generated in the entire livestock production and meat marketing system. For example, when the livestock production is scattered over a relatively large area and the volume of production is small, the livestock assembly function becomes very difficult and highly costly. Aspects of livestock production which are of great importance to the livestock and meat marketing system seem to be the number, size, mix and location of livestock production units. The reason is that they significantly affect the efficiency Of the livestock assembly function, which in turn affects the efficiency of livestock slaughter- ing and so one. Thus the efficiency of the total marketing system is affected. For example, when livestock production is undertaken by many small, mixed and widely scattered production units, no economies of size can be realized in livestock assembly, slaughtering, and meat distribution. In this way the total marketing cost per unit of pro- duct will be comparatively high. This means that the marketing system will essentially perform inefficiently in the sense of producing a certain output with a relatively high cost. 13R. L. Kohls, "Marketing of Agricultural Products," third edition, the MacMillan Company, New York, 1967, p. 76. 23 Table II-l, referring to the country as a whole but seeming to apply to the specific area Of E. Macedonia as well, indicates that livestock production of any specie (cattle, sheep, goats and hogs) is undertaken by a relatively large number of small producers. As this table shows, this is especially true for the cattle subsector. About 80 percent Of cattle producers raise only one to four animals each and the total number of cattle they raise represents almost half Of the area's total cattle production. More than 92 percent of total cattle production is undertaken by 99 percent of all producers, each of whom raise 1 to 19 cattle. In the hog subsector, 85 percent of all hog producers feed only one to four hogs and the total number of hogs they feed represents 30 percent Of the area's total hog production. Half of the hog pro- duction is undertaken by only one percent of the total number of hog producers, each Of whom feeds more than 50 hogs. In the sheep-lamb subsector, 53 percent Of all sheep-lamb producers raise one to nine animals each and the total number of animals they raise accounts for about six percent of the area's total sheep-lamb production. More than 73 percent of total sheep-lamb production is undertaken by 20 percent of producers who raise 50 animals or more each. In the goat subsector, almost 90 percent Of goat producers raise one to nine animals each and the total number of animals they raise represents 20 percent of the area's total goat production. Approximately 65 percent of the total goat production in the area is undertaken by only 3.5 percent of all goat producers who raise more than 50 animals each. 24 Table II-l. Livestock farm sizes, Greece, 1971.(]) 1. Size Distribution Of Cattle Holdings Cattle farm size (number Of cattle per holding) Holdings and animals 1 - 4 5 - 9 lO-19 20-29 30-49 50 & Over No. of holdings 192.720 39.400 9.100 1.220 620 240 NO. of cattle(2) 413.500 244.920 113.240 22.000 28.120 14.500 2. Size Distribution of Sheep Holdings Sheep farm size (number of sheep per holding) Holdings and animals 1 - 9 10-19 20-49 50-99 100-199 200 & Over N0. of sheep holdings 139,360 29,060 43,340 31,900 17,940 3,900 No. of sheep(2) 435,800 371,180 1333.920 Z062,460 2244,180 1035.120 3. Size Distribution of Goat Holdings Goat farm size (number of goats per holding) Holdings and animals 1 - 9 lO-l9 20-49 50-99 100-199 200 & Over N0. of goat holdings 367,580 17,960 13,160 9,200 7,500 3,760 No. Of goatsIZ) 893,100 221,200 379,780 602,180 968,120 .l79,400 4. Size Distribution of hog holdings Hog farm size (number of hogs per holding) Holdings and animals 1 - 4 5 - 9 10-19 20-29 30-49 50 & Over NO. of hog holdings 120,520 8,300 7,120 1,840 1,620 1,520 No. of hogs(2) 170,840 53,600 90,400 40,240 57,660 164,380 (1) Sample of five percent of total farms. (2) Number Of animals represents inventories in the end of 1971. SOURCE: National Statistical Service of Greece, Statistical Yearbook of Greece, 1972, Athens, Greece, 1973. pp. 173-174. 25 The generally small size of livestock operations can be ex- plained by the fact that livestock production usually is not undertaken as a principal business activity by farmers, but rather as a supple- mentary activity aimed at improving their incomes. Almost one-fourth of all farm families in Greece, in addition to cultivating various crops, raise one to ten livestock to make fuller utilization of their labor force, or to more profitably utilize crop products (e.g., corn, alfalfa, etc.) produced on their farms. Another characteristic of livestock production in E. Mace- donia, which is disadvantageous to livestock and meat marketing system, is the fact that livestock producers of any species are not concentrated in one or few areas, but rather are scattered all over the villages and towns of each province. Without exception, all the 286 communities of E. Macedonia feed livestock. However, the volume of production differs from community to community depending on the area of its arable land, pasture land, etc. An idea of the regional livestock production in the area of E. Macedonia is given in Table IV-l of Chapter IV. As that table shows, the most dense livestock producing regions in E. Macedonia are in the following order: Serres, Iraklia, Chryssoupolis, Drama, Nigrita, Nea Zichni and Doxaton. Both characteristics of livestock production in E. Macedonia (i.e., small size and the scattering of livestock operations) along with the hilly and mountainous land pose problems to efficiently (i.e., least cost) organizing the livestock and meat marketing system. 26 Livestock Assembly_ Livestock assembly is undertaken primarily by butchers and secondly by local dealers. The former are working on behalf of them- selves while the latter are working to supply meat wholesalers. The first type of assembly is common in small cities, towns and villages, while the second is in the big cities of Athens, Thessaloniki and some others. Livestock assembly seems to be undertaken on a rather un- coordinated basis. This is especially true in the case of butchers in villages or small towns. Whenever a butcher needs meat to supply his customers, he visits farmers of his village or surrounding villages, buys the required animals, slaughters them in the local slaughterhouse, and then sells the meat. It is very rare for cooperation to take place among the butchers in obtaining meat supplies. Thersituation is somewhat different in the case of local dealers. They try to satisfy a substantially greater meat demand of their meat wholesalers. For this reason, they usually undertake the livestock assembly as long as they find and are able to purchase the required, generally large number of live animals. The gathering of the necessary market informatione-regarding the quantity, quality, kind and price of the live animals-~for either category of livestock assemblers is done by commission men. Their nature and role has been described in section two of this chapter. Because of the individualistic type of livestock assembly organization and operation, a small volume of animals is usually assembled each time. For this reason, whoever undertakes the assembly 27 function seeks the employment of small trucks in order to avoid rela- tively higher transportation expenses. Out of 77 reported cases of livestock assembly undertaken by the interviewed 30 butchers in E. Macedonia, it was found that the following frequency of truck utiliza- tion occurred: (a) in 57.2 percent of the cases, trucks of 2 and 2.5 tons were used in livestock assembly; (b) in 14.2 percent of the cases, trucks of l and 1.5 tons were used; (c) in 16.9 percent of the cases, tricycles or trucks of 1/4 through 1/2 tons were used; (d) in 7.8 per- cent of the cases, trucks of 4 tons were used; and (e) in 3.9 percent of the cases, trucks of over 6 tons were used. The general trend in the size of trucks used in livestock assembly was that, the greater the distance between Slaughterhouses and production points, the greater the size of the employed trucks was. This is something which was expected, since in longer distances, the chances of acquiring larger volumes of live animals are better. In- deed, the interviews indicated that the large size trucks of over four tons were utilized in distances greater than 100 kilometers on the average, and their average capacity utilization was 94 percent. In general, the degree of capacity utilization of all kinds of trucks which were used in the livestock assembly was relatively low. In 23.4 percent of the 77 reported cases of assembling live animals, it was found that the degree of truck capacity utilization ranged between 15 and 25 percent. In 24.7 percent of all cases, the degree Of truck capacity utilization ranged between 26 and 50 percent. In 16.8 percent of the cases, trucks were used between 51 and 75 percent. 28 of their capacity. In 31.2 percent of all cases, the capacity utili- zation of the employed trucks ranged between 76 and 100 percent. In 3.9 percent of all cases, trucks were used in over capacity. Out of 30 interviewed butchers, 29 of them had used rented trucks, for assembling the required livestock. Only one of them has had his own trucks. Exactly one-half of the interviewed butchers preferred to have their own trucks. The main reason cited for this was convenience, i.e., to do the job when they liked. The remaining one-half of them do not like to have their own trucks, because they expect the operat- ing and maintenance costs of trucks to be comparatively very high for their generally small volume of business handled. Out of the total number of interviewed butchers, 53.3 per- cent of them answered that they go to buy livestock for slaughtering four times a month, 30 percent of them make six to eight trips a month, and the remaining 16.7 percent go more than 12 times a month. The cost rates of livestock assembly varies with distance and size and type of truck. Livestock assembly cost rates in E. Macedonia are shown in Table IV-3 of Chapter IV. These rates refer to a full capacity utilization of the corresponding trucks.. The in- terviews revealed that the load does not play much role in the deter- mination of the transportation rates. The major factor underlying the livestock assembly cost rates is distance. Another is the size of the truck. The larger the truck, the higher the rate for the same distance. The type of road construction (asphalt, gravel, etc.),topography (hilly 29 or mountainous areas), kind of animals transported, etc. also play some, but not an important, role in the determination Of transporta- tion rates by truckers. Livestock Slaughtering The FAO report14 describes the existing situation of live- stock slaughterhouses in Greece as follows: All slaughterhouses are rather poorly equipped. Buildings are generally old and frequently without outside walls so that dust and vermin cannot be kept off. Usually stables and slaughter rooms are not separated. Floors are of concrete and the waste water drains into an open channel in the middle of the slaughter- house from where it runs, untreated, into brooks and rivers. Mechanical equipment is generally inadequate, e.g., there are no overhead rail systems for the internal movement of carcasses and no machines for dehairing pigs. Only in few cases are there tanks for scalding pigs. There are also no working tables; the dehairing of pigs and the cleansing of the intestines is done on the floor. Scales are mostly obsolete and cold storage rooms are generally lacking. In most slaughterhouses, there are not even separate rooms for storing meat so carcasses remain in the killing room until transported. While seven years have passed since this report was first published, the situation in the slaughterhouses is still essentially the same. Of course, some new slaughterhouses have been built between 1967 and today, but they are very few and outside of the study area. In general, the improvement programs have been implemented very slowly. Currently in the three provinces of E. Macedonia there are 21 slaughterhouses which are distributed as follows: Serres, 10; Kavala, 5; and Drama, 6. However, these are only the main slaughter- houses; in Greece they are called "slaughterhouses of wide meat con- sumption", in the sense that they can provide carcass meat all over the nation.' The smaller slaughterhouses, the "slaughterhouses of ”Ibid. 30 local meat consumption," as they are called, serve the meat require- ments of the local communities. Besides a small building, they are not equipped at all. These slaughterhouses are usually located in towns or large villages. There are 18 such slaughterhouses in E. Macedonia, the majority of them in the province of Kavala.15 The ownership of slaughterhouses belongs to the correspond- ing communities where they are located and for which they are a good source of income. None of them are private or cooperative. The ex- penses for their construction and equipment are undertaken by both the national government and the community authorities contributing about equally. The operation of the slaughterhouses is undertaken by and large by the owning municipalities. Very rarely are they leased to private companies or individuals. Out of the 21 slaughterhouses of E. Macedonia, only two are currently leased to individuals. These are the slaughterhouses of Neos Skopos and Nigrita, both in the province of Serres. The capacity of slaughterhouses cannot be defined precisely and therefore it cannot be measured accurately under the existing system of slaughtering. It depends almost entirely on the number of slaughterers working in a specified slaughterhouse. It also depends on their skills; the more skillful they are the larger number of animals they can slaughter and skin. Thus, the size of a slaughterhouse 15Veterinary offices of the Ministry of Agriculture in Serres, Kavala and Drama. 31 in Greece cannot be described objectively unless the number of slaughterers working in it is incorporated. Essentially, there is no management in the regular meaning of the term in the slaughterhouses of E. Macedonia. There is usually only one person working in the municipal building and he is transferred to the slaughterhouse to take care of it when it operates. He may well be called acting manager. His responsibilities include opening the slaughterhouse on operating days, cleaning it after the operation, and collecting the slaughtering fees. His educational level‘Is low, usually not beyond elementary school. Slaughterhouses do not operate every day. They are usually open three days a week, i.e., Monday, Wednesday and Friday and then for only a few hours a day, typically from 8:00 to 10:00 a.m. These two factors (days and hours of operation) indicate that the currently existing slaughterhouses in E. Macedonia are not fully utilized. Whoever owns slaughtered animals pays slaughtering fees, "rights of slaughtering" as they are called in Greece. These are charges imposed by the municipalities to the users of their slaugh- terhouses. Slaughtering fees in the province of Serres are: 30 drachmae per head of cattle, 25 drachmae per head of hogs, lO drachmae per head of sheep-goats and 5 drachmae per head of lambs or goat-kids. The corresponding figures for Drama and Kavala are 70, 50, 7.5 and 16 5 drachmae per head respectively. The usual total values of these animals are currently averaged at the levels of about 12,000 drachmae A 16Data provided by the acting managers of the main slaugh- terhouses in each province of Serres, Kavala and Drama. 32 for cattle, 3,000 drachmae for hogs, 800 drachmae for sheep-goats, and 500 drachmae for lambs and goat kids. Slaughtering (killing and skinning) of animals is done by specialized workers, the slaughterers. Methods used are generally crude. Cattle are killed by pistol using specially treated arrows. The other animals, hogs, sheep and goats are killed by knife. Skin- ning is usually done on the floor, unless the slaughterhouse is equipped with an internal rail system on the ceiling. In such a case, the killed animal is hung for skinning. Slaughterers typically work independently of the slaughter- house in the sense that they are not employees of the slaughterhouse. They have their own union through which they are notified to go for work. They are paid directly by the owners of the slaughtered animals such as butchers, meat wholesalers or local dealers. Their payment is scheduled according to the livestock species. For the area of E. Macedonia, they are:17 l. Cattle 160 drachmae 2. Hogs 90 drachmae 3. Sheep and goats 25 drachmae 4. Lambs 20 drachmae These prices reflect the cost of slaughterers' labor used in both the killing and skinning the slaughtered animals at the slaughterhouse. While, there is not complete uniformity in the payment of slaughterers among all the areas, the differences are small. 17Data provided by the presidents of slaughterers unions in each province of Serres, Kavala and Drama. 33 Much of the animal byproducts (such as blood, etc.) is thrown away during the slaughtering process. The relatively small volume of slaughtering makes it unprofitable to process these by- products in each slaughterhouse. Inspection of the slaughtered ani- mals by veterinary doctors takes place both before and after the slaughtering. The annual volume of slaughterings in the main slaughter- houses of E. Macedonia are shown in Table II-2. As the table shows only three out of the 21 slaughterhouses have processed more than two thousand tons of meat annually. Another three slaughterhouses pro- cessed between one and two thousand tons of meat. The remaining 16 slaughterhouses processed less than one thousand tons of meat. Of these, three slaughterhouses processed less than 100 tons of meat in 1972. There are typical seasonal fluctuations in livestock slaugh- tering, varying for the different species, as Table II-3 indicates. This table gives the monthly livestock slaughterings by species and in total for the entire province of Serres, i.e., for its ten slaugh- terhouses altogether. As the table shows, the peak of cattle slaughterings takes place in the months of June and October. For Sheep, lamb and goats it takes place in August and September, and for hogs in November and December. This seasonality is generally related to either demand for the corresponding kinds of meat or to the avail- ability of fodder during the months in question. The first case usually applies to lamb and pork subsectors while the second to the cattle subsector. 34 Table II—2. Slaughterhouses currently existing in E. Macedonia, Greece, with the corresponding volume of slaughterings by livestock species, 1972. Location of Number of Head of Carcass Meat Weight in Tons Slaughterhouses Slaughtered Animals of Slaughtered Animals Cattle Sheep- Hogs Beef Lamb Pork Total Goats 'Mutton Carcass Goat Meat Meat 1. Serres 7,636 30,062 5,718 1,380 351 457 2,188 2. Iraklia 4.783 46.808 5,135 1,079 613 411 2.103 3. Mavrothalassa 409 1,925 236 85 21 12 118 4. Nea Zichni 633 3,628 410 96 43 25 164 5. Neos Skopos 7,287 6,811 3,359 1,592 82 235 1,909 6. Nigrita 3,938 13,836 1,901 857 138 95 1.090 7. Proti 1,163 7,539 786 166 90 37 293 8. Rodopolis 565 2,533 1,148 102 33 64 199 9. Sidirokastron 2,194 220,058 1,500 450 3,081 89 3,620 10. Strymonikon 322 4,986 578 53 70 41 164 I. Province of Serres,28,930 338,186 20,771 5,860 4,522 1,466 11,848 11. Kavala 3,976 25,476 2,241 618 242 133 993 12. Chryssoupolis 3,085 16,986 3,581 1,238 186 215 1,639 13. Eleftheroupolis 1,226 4.950 1,057 171 46 85‘ 302 14. Podochorion 194 5,870 148 18 49 7 74 15. Moustheni 147 2,136 155 13 21 8 42 II. Province of Kavala 8,628 55,418 7,182 2,058 544 448 3,050 16. Drama 4,207 16,464 1,440 603 179 72 854 17. Prossotsani 1,189 10,589 1,173 177 118 82 377 18. Kato Nevrokopi 317 281 90 37 4 6 47 19. Nikiforos 495 4,784 119 61 43 6 110 20. Kalampaki 3,152 3.458 667 691 45 47 783 21. Doxaton 1,187 5,975 1,610 198 61 84 343 III. Province of Drama 10,547 41,551 5,099 1,767 450 297 2,514 IV. Eastern Macedonia 48,105 317,027 33,052 9,685 4,086 2,211 17,402 Sources: The Veterinary Offices of the Ministry of Agriculture in each of the three provinces of Serres, Kavala, and Drama. 35 .mmccmm mo mocw>oc¢ ms“ :4 mgappzuwcm< 4o chm4:_z mg» 40 mupmeo Acmcwgmum> mg» ”mumaom o.oop 44m.__ o.oop 444.~ I o.oop Num.4 o.oop oom.m Aoz ._F _.m_ mmm.. 0.4 NmF A.m_ m_4 “.mp mom Laneyuo .oL a.mF o_m._ 4.4 044 o.m_ m_m N._4 mmm cansapaam .4. _.m_ 4mm._ m.4 4m_ o.o~ mom 4.4 A_m 833434 .4 4.4 44_._ N.m mm_ 4.~_ 4mm N.“ 0N4 s_=4 .A 4.N_ ~84._ m.4 mm “.4 om4 0.4, 4mm 4:34 .4 m.~ mam “.4 mm N.N mm A.N 4NP so: .m m.4 ABA 4.4 we 4.m mam m.m F_m _4La< .4 4.4 m4m N.o 44 A.o mm N.A 0N4 £0242 .m A.4 4mm F.4 4m . N._ mm o.“ oA4 seasenmc .N 4.m ~44 m.o_ Fm, m._ 44 M.“ 044 4244244 .. ucmucma mcoh ucmucmm mcoh ucmocma meek acmucmm mcop mgucos 3442 to mucwx FP< xeoa 4am: A444 .coeesz .neas comm .Nump .mummcw .mwcocmumz .m .mmccmm mo mucw>oca as“ mo mmmaozcmugmzmpm mgp __m =4 puma mmmucmu 4o mcop uwcgma cw mmcwgmpgmzmpm xoopmm>4~ xpcgcoz .mTHH OFEuh 36 Meat Transportation and Distribution Meat transportation refers to shipments of carcass meat from the slaughterhouses to representative points (e.g. warehouses of meat wholesalers) of consuming centers. Meat distribution refers to ship- ments of carcass meat within the city, that is, from a central point (e.g., a warehouse of a meat wholesaler) to the individual meat re- tailing Shops. In this analysis meat distribution cost is ignored and the term is interchangeably used with that of "meat transportation cost." Meat transportation takes place with trucks equipped with refrigeration facilities when the distance is relatively long, or with common trucks or tricycles when the distance is relatively short. The most commonly used refrigerated trucks in the area of E. Macedonia are of sizes 2, 2.5, 5, 6, 10 and 12 tons. Meat transportation cost rates in E. Macedonia are shown in Table IV-6 of Chapter IV. As that Table shows they vary in direct proportion to distances travelled. The volume shipped or the size of truck does not seem to play any important role in fixing the transpor- tation cost rates. Truckers are generally small in number and size in both E. Macedonia and the country as a whole. The main reason for this is the relatively small annual volume of their business. In the city of 18 Serres there are 18 truckers; in Kavala, 12; and in Drama 11. Out of ten interviewed truckers, two of them had 5 trucks each, three had 18Data provided by the provincial offices of the Ministry of Commerce in Serres, Kavala and Drama. 37 four trucks, two had three trucks, and three had two trucks. These truckers were selected for interview because all of them were involved in either live animals or meat transportation or in both. Almost all truckers in a city or a province constitute a Union. Through this they establish uniform transportation rates for the entire area in which their activity is extended. Meat Wholesaling_ Meat wholesaling seems to be the most underdeveloped area of the meat marketing system in E. Macedonia and the country as a whole. Both individuals and governmental authorities, by a vast majority, consider it as an unproductive marketing function. They consider wholesalers along with local dealers or commission men as "parasites" on farmers. This unfavorable belief, the so-called "antimiddleman bias," created against meat wholesalers: and middlemen in general, led the governments to ignore them any time new public programs were formu- lated for the development of the livestock and meat industry in the country. Meat wholesalers operate under fixed marketing margins of six percent. This means to get the meat wholesaling price, on the farm price of meat (which is also determined by the government) Should be added an amount equivalent to six percent of farm price. This meat wholesaling margin policy does not uniformly apply all over the country. In many provinces in which wholesaling was considered by the government as abandoned by meat retailing, wholesaling was not author- ized at all. 38 The outcome of this governmental policy was that meat whole- salers from, say, Athens or Thessaloniki going to such provinces and buying its meat supplies were unwilling to sell meat (obviously at no profit, since no wholesaling margin was authorized there) to the re- tailers of that province. The consequence of this was that even in the most favorable lamb producing areas, customers could not find lamb to consume. After strong protests by both local meat retailers and consumers, the government made an effort to alleviate this situa- tion somewhat. A new rule was established so every wholesaler buying meat supplies from a province was obliged to sell to that local market at least 25 percent of the total volume of his meat purchases. The chain reaction of meat wholesalers to that new governmental rule was twofold: (1) either they were unwilling to go to such provinces to get meat supplies with the consequence that many animals in those areas could not be sold locally, or (2) if some of them still were continuing to go to those areas to get meat supplies, both the live- stock slaughtering and carcass meat transportation was undertaken secretly at night. The result of their behavior was that: (a) neither a good picture of livestock slaughterings during that period can be given, since these slaughterings were not recorded, (b) nor were the slaughtered animals examined sanitarily. Meat wholesalers do not handle large volumes of meat, simply because the market area which they serve is relatively small. Thus, in Serres there are four wholesalers, three in Kavala and none in 19 Drama. Each runs his business almost alone. They get their meat 19Data provided by the provincial offices of the Ministry of Commerce in Serres, Kavala and Drama. 39 supplies through local dealers. They buy in cash from farmers and sell in short term (weekly) credit to meat retailers. The relatively large wholesalers are generally specialized, i.e., they are engaged in either beef wholesaling, lamb wholesaling, or frozen meat whole- saling, etc. This obviously makes the meat marketing system more in- efficient since it forces meat retailers to deal with more than one wholesaler, and thus Spend more time in getting their meat supplies. Marketing functions offered by meat wholesalers to either butchers or farmers seem to be very poor, if they ever exist. Besides meat storage and short-term credit to butchers, it seems that meat wholesalers do not provide at all or sufficiently the following market- ing functions: 1. No grading function is offered to either livestock producers when they or their representatives buy animals from them, or to meat retailers when they sell meat to them, usually in whole, half or quarter carcasses. The absence of meat grad- ing makes it necessary for butchers to visit them for personal inspection of meat purchased. 2. No transportation is provided by wholesalers to meat retailers, leaving them responsible for the meat shipments to their shops. 3. No outlook information is provided to either farmers or butchers concerning both meat supplies and prices in the near future. Information cutbacks (if not misinformation) many times are considered critical for a profitable operation, not only in meat wholesaling, but in many other businesses in Greece. 40 Meat Retailing Meat retailing in Greece is almost entirely undertaken by specialized sellers, the butchers. Public meat markets do operate in the cities, but their volume of sales seems to be small compared to that of butcher shops. Butcher shops are many in number and small in size. In the city of Serres with a population of 41 thousand people there are 57 butcher shops. In Kavala with a population of 47 thousand people there are 65 butcher shops and in Drama with a population of 31 thousand people there are 28 butcher shops.20 Informal interviews with butchers in Serres and Drama indi- cated that the weekly volume of meat sales of a representative butcher shop averages about 100 kilograms of beef, 150 kilograms of lamb, sheep and goat meat, 30 kilograms of pork, and 80 kilograms of chicken. Entry into meat retailing industry is easy. Whoever wants to operate a butcher shop submits an application to the local police station and gets a license for it. From a competitive point of view, it does not seem to present any barriers, since neither big butcher exist nor heavy capital investments are required. Meat advertisement by the meat retail stores is absent. Buying habits of Greek consumers seem to be much different than those of Americans. They buy more often (1 to 3 times a week), and much less (1 to 2 kilograms) each time. This buying behavior of Greek consumers is probably the outcome of many factors, such as the greater amount of time available to Greek housewives (since a small 20Data provided by the provincial offices of the Ministry of Commerce in Serres, Kavala and Drama. 41 portion of them work), their desires to buy fresh meat, the proximity of butcher shops so that it is not a problem for them to go often for shopping, etc. Meat retailing is almost entirely a personal operation. The highest volume of its sales is based upon the personal relations of the butcher and his customers. Approximately 80 percent of his clientele is a permanent one. The trust which the butcher creates to his customers via his good service is the most important element for keeping such a high percentage of permanent clientele. Almost all meat retailers run their business in small stores. An average size of 15 square meters (i.e., 3 x 5 meters) is very com- mon. Despite the small size of the butcher shop, rent is relatively high. Depending upon its proximity to the center of the city, the rent ranges from 25 to 100 dollars a month. Total monthly variable cost (including rent) averages about 120 to 250 dollars a month. The butcher shops are generally poorly equipped. However, refrigerators and freezers along with a scale and meat grinder exist in all the shops. Usually the scale is not automatic in the small towns or villages, while the electronic scales--widely used in the U.S.A.--are not being used yet in Greece. Special butcher knives are used to cut the meat in a primitive way into smaller parts. An axe or saw are also in existence for cutting the bones, which almost al- ways accompany the meat selling. Boneless meat is seldom, if ever, sold by meat retailers. A large and round piece of wood upon which the meat is cut is another tool of the Greek butcher. 42 No display of meat cuts on a ready selling basis takes place in meat retailing in Greece as it does in other European countries and in the U.S.A. That is, there are no meat cuts packed, priced and displayed in an open refrigerator so that the customer can look them over and select the cut of his choice. Probably reasons for not hav- ing such a system in Greece may be the limited space in the butcher shops, the cost of an open refrigerator, the small volume of sales, etc. Butcher shops in more than 90 percent of the cases are operated by the butchers themselves. No other personnel helps with the operation simply because nobody else is needed. The butcher himself can very well manage all the transactions taking place during the day. Unusual peaks beyond his capacity are rare simply because the number of customers corresponding to each butcher is substantially limited. For most of the eight working hours a day the butcher is sitting in the store without any transactions. It is obvious that tremendous excess capacity in meat retailing in Greece takes place. The retail price of meat (as well as price at the wholesale and farm level) is set by the government and more specifically by the Ministry of Commerce through fixing the retail meat marketing margins. What actually happens is that the government sets the meat farm prices and then on the basis of fixed marketing margins determines the retail meat prices. That is, both wholesale and retail meat marketing margins (expressed in money terms) are added on the farm prices to obtain the retail meat prices. 43 The government sets retail prices for two kinds of meat cuts, legs and ribs. However, the price differential between the two cuts is not large. What is important in the Greek meat pricing system is not the grade as it is the age of the dressed animal, e.g. veal versus beef, etc. No meat grading system based on meat cuts (such as T-bone, sirloin, etc.) exists now in Greece, as it does in the U.S.A. and other European countries. This means that no price differentiation takes place in the meat market according to the quality of carcasses. This, in turn, essentially means "personal discrimination" because different customers pay almost the same prices for different grades of meat. This actually takes place currently in meat retailing in Greece. Butchers faced with such a situation (absence of meat grading and presence of governmental fixed retail prices) usually sell the good quality meat to their best customers (relatives, wealthy people who buy more often and in larger quantities). This, in essence, is at the expense of lower income customers, who even though pay the same price, actually acquire a much lower quality of meat. In other words, poor customers essentially subsidize the rich customers in the meat consumption in Greece. Retailers obtain their meat supplies either through whole- salers (as it commonly happens in the cities) or directly through farmers as happens in villages, towns and small cities. Retailers in getting their meat supplies from wholesalers spend considerable time in personal meat inspections in order to buy good quality meat and thus better satisfy their customers. Butchers 44 also spend time to find a transportation mode to ship the purchased meat to their stores. Given the fact that meat wholesalers are usually specialized in beef wholesalers, frozen meat wholesalers, etc., meat retailers in transacting with all of them separately spend considerable time. All these activities of meat retailers, which by and large could be eliminated in a well organized meat marketing system, seem working at the expense of successfully managing the meat retailing business. Livestock and Meat Price and Trade Policies The major objective of governmental policies regarding the livestock and meat subsector of the Greek economy is to stimulate livestock production in order to achieve the following three principal targets.21 a. to minimize meat imports in order to reduce the outflow of foreign exchange, badly needed for the industrialization process of the country. b. to provide sufficient incomes to livestock producers, and c. to supply sufficient amounts of relatively low cost meat to all the consumers throughout the country. The main policy instruments, which the government employed from time to time to accomplish its targets were:22 21OECD, "Agricultural Policy in Greece." Paris, France, 1973: PP- 36'40. 22Ibid. 45 a. price policies for livestock and meat. b. trade (especially import) policies for livestock and meat. Until 1964, the government relied on a tariff barrier of 15 to 28 percent on imported livestock and meat, to protect domestic production. Yet the rate was not sufficiently high to balance the difference in price levels between domestic and world markets. As a result prices for imported meat were considerably lower than domestic meat prices. Consumer demand for lower priced imported meat under- standably rose and thus demand for, and therefore, prices of domestic meat did not increase sufficiently to cover increased production costs.23 Since foreign trade protection policy had not satisfactorily worked, the government at the beginning of 1964 introduced the system of minimum farm prices, varying for the different kinds of meat. As soon as producer prices threaten to fall below the mini- mum price, issuance of import licenses is reduced or stopped in order to reduce total meat supplies and thus keep prices above the minimum levels. During the period of 1970-73, supply of meat was small and demand high, pushing the meat prices up. The government trying to control the rising cost of living, introduced maximum prices for meat at all levels, farm, wholesale and retail, which from time to time were raised to not discourage the domestic livestock production. 23E. Bockenhoff and N. E. Wernberg, "Marketing of Livestock and Meat in Greece," FAQ, No. TF-7, Rome, Italy, 1967, p. 39. 46 During this year 1974, the government also introduced mini- mum intervention prices for pork, in order to prevent prices from falling below a minimum level. This policy was mainly aimed at not allowing the discouragement of hog producers from the currently exist- ing demand crisis for pork. Such a possible discouragement may lead hog producers to reduce or to give up production with the probable consequences of another nationwide meat supply crisis. In addition to the product price policies, a program of direct or indirect subsidies is also in existence. Subsidies in the form of premium for cattle with a liveweight of more than 250 kilo— grams were the first introduced in 1963. In 1966, this minimum live- weight was increased to 300 kilograms. In 1970, this program was abolished. Since 1971, a generous investment program on livestock pro- duction was introduced in order to encourage the entry of larger producers into the livestock industry to develop it relatively faster. Heavy subsidies on inputs (buildings, equipment, etc.), large amounts of loans with a very low interest rate and increased meat prices were employed. Summary The basic characteristics of the present livestock slaugh- tering system and other marketing functions of the livestock--meat industry in E. Macedonia were presented in this chapter. The purpose was to give an idea of how the entire livestock production and meat 47 marketing system performs. This may help in better understanding the research problem and in facilitating the decision making process. Livestock production in E. Macedonia is undertaken by many small farmers. The density of production in each region is primarily affected by the acreage of both arable and pasture land and secondly by other factors, such as rainfall, farming traditions, etc. The production density affects the performance of livestock assembly, processing, and the meat distribution system. Their costs affect, in turn, the optimal number, size and location of slaughter plants. Livestock assembly is basically performed by butchers and local dealers. The former are found more often in villages and small towns while the latter in cities and large towns. Butchers assemble live animals always for themselves while local dealers by and large for meat wholesalers. Livestock assembly cost rates vary primarily with the distance that the animals are shipped and secondly with the size or the type of trucks used as transporters. Livestock slaughtering in E. Macedonia takes place in the existing 21 "slaughterhouses of wide meat consumption." Of them, 10 are located in the province of Serres, 5 in Kavala and 6 in Drama. The annual volume of slaughterings per plant is generally small. Sixteen out of 21 slaughterhouses slaughter live animals accounting for less than 1,000 tons of carcass meat equivalents. They usually operate three days a week, and only a few hours each day. All plants are owned and operated by the municipalities in which they are located. The owners of slaughtered animals pay both slaughtering fees to local administration for the right of using the facility and 48 wages to slaughterers for slaughtering (killing and skinning) the animals. The buildings, machinery and equipment of most existing slaughterhouses are out of date. The slaughtering system is a crude one. Cattle are killed by pistols using specially treated arrows while sheep, goats and hogs are killed by knife. The skinning is usually done on the floor. No processing of animal by-products takes place, because their small volume in each slaughterhouse makes it unprofitable. The transportation of carcass meat from slaughterhouses to consumption centers is accomplished with either common or refrigerated trucks. The former are used within short distances of less than 30 kilometers while the latter are used for longer distances. Meat transportation cost rates per ton are basically related to distance. Meat wholesaling is essentially underdeveloped. Only seven meat wholesalers exist currently in E. Macedonia, of whom four are in Serres, three in Kavala and none in Drama. Their primary function is to sell meat to butchers, semi-wholesalers and big consumption insti- tutions (hospitals, restaurants, etc.). No grading service is offered to either livestock producers or meat retailers. Also, neither trans- portation nor outlook information is provided to either participant of the livestock-meat industry. Meat retailing is undertaken by specialized retailers--the butchers. It is also performed by meat semi-wholesalers. Butchers are relatively numerous and their annual volume of sales is small. They generally operate on a personal basis, in the sense that they 49 have a large number of permanent customers. The meat they sell is neither graded nor pre-packaged, and they apply almost uniform prices to all meat cuts. Meat prices are fixed at the farm level and regulated at the wholesale and retail level through regulating the marketing mar- gins. Governmental trade policies are exercised by controlling the volumeof meat imports. The purpose of both price and trade policies is basically twofold: a) to provide sufficient income to livestock producers and b) to assure consumers of a regular flow of meat at a reasonable price. CHAPTER III THEORETICAL CONSIDERATIONS AND METHODOLOGICAL PROCEDURES Introduction The economic theory (model) underlying the problem under investigation along with the mathematical and computer models util- ized in the analysis are presented in this chapter. Also, the ana- lytical procedure which was followed is described in brief. Further- more, the simplifying assumptions which were made and the variations of the basic solution model which were considered during the analysis are presented. The Economic Model The cost minimization model underlies any plant location analysis. The reason for this is that such analyses aim toward the determination of an optimum location for a processing plant in a cer- tain area, so that the totality of specified costs incurred can be minimized. Such costs are principally considered the following: (a) the cost of assembling the raw material from its sources to the sites where the plants are located; (b) the cost of processing the material in the plants in question; and (c) the cost of distributing the finished product from the plant locations to its final destinations. 5O 51 The nature of the current problem is the determination of the optimum number, size and location of slaughtering plants in the area of E. Macedonia. In analyzing this problem, the focus was put almost entirely on minimizing the aggregate costs of assembling the live animals from the production regions to the slaughtering plants, processing them in the plants and distributing the carcass meat from the slaughtering plants to the consumption centers. Figure III-1, whose horizontal axis represents the number of plants and vertical axis the total costs, shows graphically how the optimum number of plants in the minimum cost (optimum) solution is achieved. In this graph, one curve gives the total transportation cost (TTC), i.e., the combined costs of livestock assembly and meat distribution; another curve gives the total processing costs (TPC). The transportation cost curve is downward sloping to the right, indi- cating that as the number of slaughtering plants increases, the total transportation costs decrease. This is so because, on the one hand, live animals are shipped relatively short distances in order to be slaughtered, and, on the other hand, carcass meat is also transported relatively short distances from slaughtering plants to consumption centers. To the contrary, the total processing cost curve is upward sloping to the right. This means that as the number of slaughtering plants decreases, total processing costs decline too, for the simple reason that economies of size are expected to be realized in process- ing. From the combination of the transportation and processing cost curves, the total cost (TC) curve is obtained. The importance of this curve is that its lowest point gives the optimum solution, i.e., IN MILLION DRACHMAE TOTAL COSTS 7O 40 30 20 10 52 TC TTC l I l I l I I l 1 J 012 3 4 5 6 7 8 9‘10 NUMBER OF PLANTS Figure III-l. Determination of the optimum number of plants on the basis of the total (processing and transportation) costs. (Hypothetical data) 53 the optimum number of slaughtering plants with which the minimum ag- gregate cost is achieved. The Mathematical Model The mathematical model used in this analysis was developed 24 by King and Logan. It has the following form: ' ° ' : = . . .. .. + . . . + . . . M1n1m12e Z Z1£JA1JL1J ZJCJEJ XJZkTJkMJk Subject to: (a) production balance: ZiLij E-Si (b) consumption balance: Z.M.k > Ok J J '— (c) processing balance: ZiLij = Ej = szjk (d) Lij’ Ej, Mjk 3_O Where: i = supply regions; i = 1,...20 j = potential slaughtering plants; j = 1,...lO k = consumption centers; k = 1,...21 Li' = live animals (expressed in meat equivalents in tons), J Shipped from the supply region i to the slaughtering plant j. E. = live animals (expressed in meat equivalents in tons), processed in the slaughtering plant j. M.k = carcass meat in tons, shipped from the slaughtering J plant j to the consumption center k. 24King, Gordon A., and S. H. Logan, "Optimum Location, Num- ber and Size of Processing Plants with Raw Product and Final Product Shipments," Journal of Farm Economics, Vol. 46, No. 1 (February, 1964), 94-108. 54 Ai' = livestock assembly cost in drachmae per ton of meat 3 equivalents, from the supply region 1 to the slaugh- terhouse j. C. = processing cost in drachmae per ton of meat equiva- lents of the livestock processed at the slaughter plant j. T.k = meat transportation cost in drachmae per ton of car- 3 cass meat from the slaughter plant j to the consump- tion center k. S. = total supply of livestock slaughterings (expressed in terms of meat equivalents) in tons from the supply region i. Dk = total meat demand in tons in the consumption center k. The COmputer Model The computer model used in this analysis is the "tranship- ment model." This is a special kind of transportation linear program- ming model. It is called so because this model studies simultaneously the shipment of a product from its origins to marketing facilities (e.g., processing plants, warehouses, etc.) and the transhipment of the product from these facilities to final destinations. For this reason, the matrix of the transhipment model is accordingly con- structed in order to take into consideration all activities involved. In this study of optimum number, size, and location of pro- cessing (slaughtering) plants, the matrix has been divided into three distinct parts, with regard to activities (columns). These are the following: 1. The part referring to the livestock assembly from the produc- tion points to the slaughterhouses. The number of activities 55 (columns) of this part is equal to the number of supply points times the number of processing plants. 2. The part referring to the livestock slaughtering at all the potential slaughterhouses. The number of activities in this part is exactly equal to the number of all potential slaugh- tering plants. 3. The part referring to the distribution of carcass meat from the slaughterhouses to the consumption points. The number of activities of this part is equal to the number of slaugh- terhouses times the number of consumption points. The matrix size for this problem is 81 rows by 420 columns. Of these 420 columns, the first 200 columns represent the potential shipment of live animals from each of the 20 supply points to each of the 10 potential slaughtering plants. The next columns, i.e., from column 201 to column 210, rep- resent the number of all potential slaughtering plants. These activ- ities reflect the total number of live animals slaughtered and pro- cessed in each of these 10 potential plants. The last 210 columns, i.e., from column 211 to column 420, represent the shipment of carcass meat from each of the 10 potential plants to each of the 21 existing consumption points. As far as the rows are concerned, the first 20 rows repre- sent the supply of live animals from each of the 20 supply points. The next 10 rows, 21-30, represent "livestock equilibrium" in the processing plants, i.e., what is received from the production points is equal to what is processed in the plants. The next 10 rows, 31-40, 56 represent the "meat equilibrium," i.e., what is shipped to consump- tion points is equal to what is processed in the plants. The follow- ing 21 rows, 41-61, represent the meat in-shipments to the existing 21 consumption points. The remained 20 rows, 62-81, represent the plant capacities of the potential 10 plants given in a range of maxi- mum and minimum volume which can be processed in each of these plants. Table III-1 gives an idea as to how the matrix used in this analysis looks. This matrix was basically constructed by Professor Stephen Harsh of Michigan State University and modified by the author to present more neatly the inflow and outflow of the product. As it is seen in this matrix format (based upon hypothetical data), there are 3 supply regions, A, B and C, 2 processing plants, F and H, and 3 conéumption regions, X, Y and Z. These made up a ma- trix size of 14 rows (3 + 4 + 3 + 4) by 14 columns (3 X 2 + 2 + 3 X 3). A brief explanation of this matrix format might be worth- while, since it could give some insights as to how this computer model works. The explanation will follow the matrix structure by rows. Row 1 shows that the supply region A can ship its total amount of less than or equal to 500 units (as shown in the column of constraints) to both potential plants F and H, as figures of l indi- cate in columns 1 and 2. However, as to what quantity will be shipped from the supply region A to the potential processing plants F and H will depend first on the livestock assembly cost from A to F and H (which in turn, will primarily depend on the corresponding distance) and secondly on the unitary processing cost in each of these two plants. The quantities of raw material shipped out from region A to 57 Amou pmou cowpmpcoamcmc» new: mcwmmmuoca amou apnemmm< xooumm>w4 m4- o4- N4- m 1 cm. m 1 mm- on- om- om- mN- mp- m4- o4- pmoo 44:: com m 4 .4P mmwuwomamu com A P : .mp pcmpm 4con w P .NF mwcmpm ,ooe m 4 u .FF mcwmmmuocm omN m 4 F N .op mWWWflMfiwmo cow m 4 P > .m mcmpcmu . . omm N F F x .m covuaszmcou Ezwcn4Fw=cm o n F 4 P p- I .N :44. o u _ _ _ 7 .._ e Eavcnwpvzcm o n P- F 4 4 z .m mucmpa xuopmw>44 o u 4. P 4 P u .4 mcwmmmuocm com w P 4 u .m wwwwwamwwo com w _ A m .N mcocmam .p.» com w A _ 4 ._ Npaasm mucwmcpmcou N > x N > x z m I m z m I m mcowumuoN 3 2 N4 Z 2 m m N o m 4 m N F .cowum_:scow mewssmcmocg cmmcwp we» cans: 4muoe pcmsqwgmcmcu asp mo pmscom chpmz .FTHHN mFQMH 58 plants F and H simultaneously appear in rows 4 and 5 under the same columns 1 and 2. Similar explanation can be given for the supply re- gions B and C. The intersection of rows 4 and 5 with the columns 7 and 8, respectively, give the total amount of raw material processed in each of these plants. These amounts should be equal to the sum of the corresponding quantities shipped to these plants from each of the existing supply regions and thus a zero balance livestock equilibrium appears in the column of constraints with regard to the processing plants. Columns 9, 10 and 11 under the row 6 show the amounts of finished product (carcass meat) which can be shipped from the slaugh- tering plant F to each of all existing consumption points X, Y and Z. As to what quantity of meat will be shipped from F to X, Y and Z will depend on the meat transportation cost between them and that in turn will primarily depend on the corresponding distance. Rows 8, 9 and 10 under the same columns 9, 10 and 11 show the carcass meat outshipment from plant F to consumption points 5, Y and Z as simultaneously being in-shipments to these consumption points. Similar explanation can be given for columns 12, 13 and 14 for rows 7 (as out-shipments) and 8, 9 and 10 (as in-shipments). The quantities of carcass meat which should be shipped to each of these consumption regions should be greater than or equal to the quantities appearing in the column of constraints for the corresponding rows 8, 9 and 10. The intersection of columns 7 and 8 with the rows 6 and 7, respectively, give the total amount of carcass meat shipped out from 59 each of the plants F and H. These amounts should be equal to the sum of the corresponding quantities shipped to each of the consumption regions and thus a zero balance meat equilibrium appears in the col- umn of constraints with regard to the processing plants. Rows 11 and 12 give the plant capacity of plant F and rows 13 and 14 give the plant capacity of plant H in a range of greater than or equal to and less than or equal to a given plant capacity as shown in the column of constraints. Columns 7 and 8 give the amounts of raw materials processed in each plant, respectively. The last row, which is not numbered, gives the unitary costs of assembly, processing, and distribution. The assembly and distribution cost is given as the cost of transporting one unit of the product for the distance involved. For example, the assembly cost 10 appearing in column 1 means that to assemble one unit of live animals (here 1 ton of carcass meat equivalents) from the supply re- gion A to the processing plant F will cost 10 monetary units. The processing costs under the columns 7 and 8 are given as the costs of processing one unit of live animals (here 1 ton of carcass meat equiv- alent). The unitary cost figures bear a negative sign in front of them. This is so, because this cost minimization problem is solved in the computer as maximization problem. It is obvious that to maxi- mize the negative cost function is the same thing as to minimize the positive cost function. The most significant information given by the computer out- put is the following: 6O 1. The quantities shipped from supply points to processing plants; 2. The quantities processed in each plant; 3. The quantities shipped from processing plants to consumption centers; 4. The aggregate cost of assembly, processing and distribution of the optimal solution; 5. The marginal cost of livestock slaughtering in each plant. That is, how much the total cost of slaughtering in a certain plant will change when the volume of livestock slaughtering in that plant will increase by one unit. With regard to computer analysis, it has been done in the computer center of Michigan State University. Because of the rel- atively large size of the matrix (81 rows by 420 columns), the APEX-I25 copyright computer program has been utilized in this analy- sis. The Analytical Procedure To generate the appropriate form of data which were re- quired in the determination of optimum number, size and location of livestock slaughtering plants in E. Macedonia, Greece, the following stepwise procedure was employed. 25Control Data Corporation, "APEX-I Reference Manual," Con- trol Data Corporation, Minneapolis, Minnesota, 1974. 61 a. Location and Volume of Livestock Slaughterings. The first step is the designation of livestock supply areas and the es- timation of livestock slaughterings in each area. The latter is described in Chapter IV. The designation of supply areas in E. Macedonia has been done for each province separately. The basis for the demarka- tion was the existence of natural barriers, such as rivers, mountains, concentration of villages, etc. The province of Serres was divided into 8 areas, that of Kavala into 5 areas, and the province of Drama into 6 areas. Thus, the entire area of E. Macedonia was subdivided into 19 smaller regions, as they are shown in the following map (Figure III-2). The supply of slaughterings in each of these regions was represented by one point, since the transhipment model which is used in this analysis is a point-trading model. Generally, each region has been represented by its central locality. However, for regions in which cities or large towns were in- cluded, they were selected as representative points, whether or not they were centers of the regions. The rationale for this is that these cities or towns are usually centers of sizable livestock production in addition to being major cen- ters of meat consumption. Besides these 19 supply regions of livestock slaughter- ings in E. Macedonia, another supply point was added to rep- resent the livestock and meat imports into the area. The village of Promachon, which lies in the borders of Greece and 62 .mommcw .owcoumumz .m mo am: .N-HHH seamen @ waa.msommxuno doanoaoovom mammamnuou>.. o aufiuwfiz @ .maoum wo>aaovom w wmam @ Aanuflu aaz © mmuuom doumxoo moamuoqw. «AHxaAH © O «namuommoum aouum flow 0 owummamumm no UNfl—OH . S m . “moxou>mz oumM doumnouwvwm O maaomouom 63 Bulgaria was selected. This point was selected because all the imports of both live animals and meat into Greece from Yugoslavia, Rumania and Bulgaria pass through this village. b. Location and Volume of Meat Consumption. The second step of this analysis is the designation of the meat consumption re- gions and the estimation of the meat consumption volume in each of these regions. The latter is described in Chapter IV. This designation is exactly the same as that of the livestock slaughtering supply regions, as far as the mainland of E. Macedonia is concerned. In the whole area of E. Mace- donia, l9 meat consumption regions were selected, each of which coincides with the 19 livestock supply regions. The representative production points of these regions were also used as the representative consumption points of the same re- gions. Beside these 19 meat consumption centers of E. Macedonia, two additional consumption centers were used to represent the regions to which the surplus meat shall be exported. The two largest cities of Greece, Athens and Thessaloniki, were se- lected as such consumption centers. These cities were se- lected to be the meat exporting points of E. Macedonia, simply 26 because according to 1972 data, more than 95 percent of the total meat exports from E. Macedonia go to those two cities. 26Provincial Veterinary Offices of the Ministry of Agricul- ture in Serres, Kavala and Drama. 54 c. Designation of the Potential Slaughtering_Plant Sites. The third procedural step of this analysis is the designation of the potential plant Sites. The significance of this step is to estimate the distances between them and all the production and consumption regions. Then, on the basis of these dis- tances, both livestock assembly cost and meat distribution cost per unit of product can be estimated. The major factor taken into consideration in selecting the potential plant sites was the concentration of livestock production. In regions in which a high density of livestock production exists, the representative points of these regions were selected as candidate plant sites. Another factor which was also important in the selection of the potential plant sites is the proximity of these plants to the existing big consumptiori' centers. Other factors, such as adequate labor supply, abundance of water supply, availability of electric- ity, access to highways, are also important elements in any plant location analysis. However, in this case these factors were not critical ones because all the regions of the study area seem to meet almost equally well these requirements. On the basis of the above considerations, the following 10 locations were selected as potential plant sites: Sidirokastron, Iraklia, Serres, Nigrita, Nea Zichni, Elefthe- roupolis, Kavala, Chrysoupolis, Doxaton, and Drama. These plant sites are shown in the map (Figure III-2) with a symbol of a circle around a dot. Of these plants, the first five 65 belong to the province of Serres, the next three in the prov- ince of Kavala and the last two in the province of Drama. After these plant sites have been selected, the next task is to estimate the road distances between them and all the production and consumption points. The distance estima- tion has been done on the basis of road distance data pro- vided by the Technical Offices of each province. On the ba- sis of these distance data, the distance matrix has been con- structed (Appendix Table A-3). . Livestock Assembly Cost. The fourth step in this analysis is the estimation of the livestock assembly cost, that is, the cost of shipping live animals from the production points to slaughterhouses. This cost along with the meat distribution cost has been estimated on the basis of data received through questionnaires from trucking companies. Out of 41 truckers in E. Macedonia, 12 have been selected for interview. The criterion of their selection was their heavy involvement in either livestock assembly and/or meat transportation. Their names and the nature of their business was provided by the offices of their unions in the corresponding provinces. The number distribution originally was five for Serres, four for Kavala and three for Drama. Of them, two were not met be- cause they were out of town the day of interview. So, fin- ally ten truckers were interviewed, of whom four are located in Serres and three in both Kavala and Drama. The estimation of livestock assembly cost is presented in the next Chapter IV. 66 e. Livestock Slaughtering Cost. The fifth procedural step in this analysis is the estimation of processing costs, that is, the in-plant unitary cost of livestock slaughtering. This has been done for different sizes of plants and for different levels of capacity utilization, as it is described in Chapter IV. f. Meat Distribution Cost. The sixth step in this analysis is the estimation of meat distribution cost, that is, the cost of shipping the carcass meat of slaughtered animals from the slaughterhouses to major representative points (e.g., ware- houses of meat wholesalers) of the consumption centers. As to how these cost data have been obtained, it has already been described above, in section (d). The estimation of meat distribution cost is presented in the following Chapter IV. 9. Number, Size, and Location of Slaughtering Plants. The final procedural step in this analysis is the determination of the optimum number, size, and location of slaughter plants. To find this the following procedure was employed: 1. The total number of livestock to be slaughtered (expressed in meat equivalents) in E. Macedonia was divided by the total number (ten) of the potential slaughter plants. Thus, the volume of slaughterings which correspOnds to each plant was determined. 2. Plant capacities were determined within a range of zero and 15,500 tons of meat equivalent. The latter figure represents the volume which the largest plant can process annually when it‘operates at 100 percent of its capacity. 3. Then, the unit cost of processing which corresponds to this volume of slaughterings was calculated. This was the same for all the potential plants in the first run, since it was assumed that in the first run each plant processes one-tenth of the total volume of slaughterings. 67 4. On the basis of these data, the first run was undertaken in the computer. The output of this run gave the differ- ent flows (volumes) of livestock slaughterings which are going to be processed in each plant. These volumes depend on the aggregate cost of livestock assembly and meat dis- tribution. 5. The appropriate unit processing costs were calculated for the corresponding new volumes of slaughterings for each plant. 6. The program with the new processing cost data was run again in the computer and the second output was obtained. This iterative procedure was continued until the total cost (assembly, processing and distribution) did not de- cline any more. 7. If no further reduction of total costs was achieved in more than two plants, then the plant with the smallest volume was eliminated and the program was run again with the remained number of plants. This was done for differ- ent combinations of plant locations of the above number of plants in order to find the optimum (minimum cost) solu- tion. This trial and error optimization process does not absolutely guarantee global optimum solution, because of economies of scale problem associated with linear programming. The optimum number of slaughtering plants is given by that number of plants when the minimum cost solution was achieved. The optimum size of slaughtering plants is determined by the corresponding volume of livestock slaughterings pro- cessed in each plant of the optimal solution. Finally, the optimum location of slaughtering plants is given by the corresponding location of the plants under which the optimal solution was obtained. 68 Alternative Solution Models Six alternative solution models--differing among themselves by some variable or variables--were examined in this analysis in order to evaluate the potential impact which they might have upon the opti- mum number, size and location of the slaughtering plants. These models are the following: 1. Basic Model. In this model were considered: (a) 1972 sup- plies of livestock (cattle, sheep-goats, and hogs) slaughter- ings; (b) 50 percent capacity utilization of trucks engaged in livestock assembly; (c) 100 percent capacity utilization of trucks engaged in meat distribution; (d) 100 percent ca- pacity utilization of slaughtering plants; (e) use of modern technology in livestock slaughtering; and (f) 20 supply re- , gions, 21 consumption centers and 10 potential slaughter plants. 2. Model II. This model differs from the basic one by only the livestock assembly cost. That is, in this model it is as- sumed that trucks engaged in livestock assembly are utilized at full capacity instead of 50 percent of their capacity as it was assumed in the basic model. 3. Model III. This model differs from the basic one by the num- ber of production and consumption regions and slaughtering plants. Specifically, in this model the study area of E. Macedonia was divided into only 14 production regions and 15 consumption centers. In addition, only 8 potential plants were considered. 69 4. Model IV. This model differs from the basic one only by the volumes of the regional livestock supplies. Projected live- stock slaughterings instead of those actually taken place in 1972 are considered in this model. These projections basic- ally were made for the year 1977. However, because of their very optimistic view, they are considered as applying to the year 1980. 5. Model V. This model differs from the basic one only by the degree of plant capacity utilization. That is, in this model plants are assumed to operate at 90 percent of capacity in- stead of 100 percent (full capacity) as assumed in the basic and all the other models. It also refers to 1980 supplies. 6. Model VI. This model differs from the basic one only by the technology used in livestock slaughtering. In particular, in this model is assumed the continuation of the current slaugh- tering system under which a standard unit processing cost ap- plies to all plants, regardless of their size and degree of capacity utilization. In other words, in this model, no economies of size are assumed in livestock slaughtering. Feasibility Assumptions In a dynamic economic system in which the free enterprise doctrine applies as that of Greece, the exogenous variables which might affect a certain endogenous variable are usually numerous. Therefore, it is practically impossible--from both operational and financial standpoints--to include all the potential causal variables 70 in a mathematical model. Thus, certain assumptions must be made upon some of the exogenous variables, so that the analysis becomes feasible. For this reason, the assumptions made in this study are called feasi- bility assumptions. Some of these assumptions are the following: 1. 2. Slaughtering cost function is assumed to be the same in all plants. This implies that all slaughter plants apply the same level of technological improvement and the prices of in- puts they use are also the same. This, in effect, means that neither technologies nor inputs affect the optimum solution pattern. Only different plant sizes and capacity utiliza- tions with their different unit processing costs affect the optimal solution. Transportation (both livestock assembly and meat distribution) cost functions are also assumed to be the same in all regions, since the same trucks and under the same conditions (truck capacity utilization, etc.) are assumed to be used in all re- gions for the relative distances and for the corresponding operations. Only distance is assumed to affect the unit transportation cost, ceteris paribus. . Livestock production and meat consumption are considered to be concentrated at one central point of each production and consumption region, respectively. Of course, this may tend to overestimate or underestimate the distances for each re- gion. However, with the large number of origins of supply, and destinations of demand these over- and underestimates may offset each other. 71 4. All animals supplied for slaughtering are assumed to go through the slaughterhouses. That is, slaughtering of any livestock on farm is considered as not taking place. Other- wise, their total annual volume of slaughterings will be lower and therefore the optimum solution much different. 5. The conversion of raw product (live animals) into the final product (carcass meat) is assumed to be given and constant for each livestock specie. In other words, the average weight of slaughtered animals is assumed to be uniform for each specie. 6. No price changes of the product within regions are assumed to be taking place for the period under consideration. 7. The total demand for the final product (meat) is equal to the total supply of raw product (live animals, as they are ex- pressed in meat equivalents) in the study area. Summar The transhipment model--a special kind of transportation lin- ear programming model--has been utilized in this analysis to deter- mine the optimum number, size and location of slaughtering plants in E. Macedonia, Greece. The basic characteristic of this model is that it takes simultaneously into consideration all the costs involved (assembly, processing and distribution) to give the solution output. The matrix format has been originally constructed by professor Stephen Harsh of Michigan State University and modified by the author. The size of the matrix used in this analysis is of 81 rows by 420 72 columns. Because of its large size, the APEX-I computer program has been utilized in the analysis. The economics underlying the research problem is the least- cost model, i.e., that of minimizing the total costs incurred in pro- ducing (processing) a certain amount of output. To study the problem, the area under consideration has been divided into 19 supply regions which were also consumption regions. One point in each region--generally a central one--was used to repre- sent its livestock production and meat consumption as well. A village of Serres on the border between Greece and Bulgaria was selected as the twentieth supply point to represent all livestock and meat imports in the area. The two largest cities of Greece, Athens and Thessaloniki, were selected as additional consumption centers to represent the ex- ported surplus meat of the area to these cities. Ten potential slaughter plants were considered to begin with in this study. Their locations coincide with the representative points of ten most densely populated livestock regions. CHAPTER IV ESTIMATION OF REGIONAL LIVESTOCK SUPPLY AND MEAT CONSUMPTION, LIVESTOCK ASSEMBLY AND PROCESSING, AND MEAT DISTRIBUTION COSTS Introduction This chapter is devoted to making the required estimation of: Q! . 1972 regional supplies of livestock slaughterings by species. Also to make their projections to year 1980; b. the regional meat consumption by kinds of meat; c. the livestock assembly cost per unit of product by different sizes of trucks and at various distances; d. the livestock slaughtering cost by sizes of plants and by different levels of capacity utilization; e. the meat distribution cost per unit of product and by dis- tances that meat is shipped. All these estimations are used as the basic information data for the computer analysis, whose results Shall be presented in the next chapter. 73 74 Estimation and Projections of Regional Livestock SuppTTes The annual regional volume of livestock slaughterings was estimated by adding those of all communities included in a specified region. The statistical data on the livestock slaughterings were given both in number of head and in metric tons of carcass meat equiva- lents. Of these two kinds of figures the latter were used in this analysis. This was done to make possible the summation of the slaugh- terings of all livestock Species undertaken in a region. This study refers to a multi-specie (cattle, sheep, goats and hogs) optimum slaughtering plant location, and the only common denominator which could be used to add the volumes of slaughterings of each specie is to express them in terms of meat equivalents. Table IV-l presents the annual livestock supplies by regions. It is understandable that this transformation may not give a perfectly accurate picture regard- ing the estimation of total costs of all livestock assembly and slaughtering, and meat distribution. However, the overall picture of total costs does not seem to deviate much from the reality mainly because the highest portion (65 percent) of all livestock slaughter- ings in E. Macedonia are cattle, on the basis of which all the cost data were estimated. The projections of livestock supplies were made by the extension agronomists of the Ministry of Agriculture in each province. They were primarily based upon the trends of livestock production. These projections were made by species and provinces in both number of head and tons of carcass meat. They originally referred to year 75 .Loguaw as» An wcoc :mmn mm: name omega :0 mcwmmmoocn cmcpczm meow 45¢ 40 mmuwwmo meucm>ocq mzp hp cmuw>oca :mmn m>mg Amaze» Lo mumm—44> an mmcwcmpsmszmv mums uwmmn och .wcsu42owcm< Co Acpmwcwz ”mmoLzom . .omm.om NNM.mN 4om.m mmw.4m 4mm.m NNo.NFm N4w.NF 4Nw.mo araasm Fmpoh .> -1 .- mmm.m u- 1- mmm.m 4mN.mmN 4o mNm mucoqEH .>H .. i- mmm.m 1- 1- m~m.m 4mN.mmN 4m mum cozumEoLm .om omm.m o.oop m—m.m Nm4 4wF.N me Now.4N omm.m Npo.m4 mango Co mu=w>oca .HNH mom N.4 pr 4 mm Fm mmm.n om mom cowpmmcmgmm .m— m4p N.N mm m mofi om o~m.~ No 4mm cogwcocwuwm .m_ mNm m.m mom mm 4mm Nm mFN.m NNN mmm.4 waoxoc>wz opmx .N_ New _.m4 mm4 44 men oFN upm.mm 44m mom.4 4cmmuommoca .mp mmm.~ m.o4 mmm._ 44m mam.m 4w4 omo.om 4m4.4 wwm.m mango .m— 4mm._ N.mN mm4.4 4N4 omm.4 4N4 mmN.NF mmm ~oo.4 coumxoo .4— oom.m o.oo_ mwm.m mmo nmo.o4 mFm mFm.oo_ Nmm.N www.mp mFm>mx mo muc4>ocm .HH mom m.m 4FN 44 o4N mm —o_.m4 mFF Nmm.4 moEmuoawo .m_ mm4.N N.m4 mmn.4 mmm mwm.m FNm mmm.~m Nmo.4 wmm.m m__oasommxccu .NF mxm.4 a.mN 4Nm N44 NmN.F map wmo.m_ New omm.m ~44>m¥ .4: mmm o.NF com No4 NMN.N mmF Nmm.om Nmm mwm.m mwponzocmgqu—m .op NO4 m.N mwm mm . N44 Nmp Fm4.m4 oofi mmw cowcocuouom .m oom.m4 o.oo4 FNO.N_ 4om.~ 44o.Nm NFm.4 Nmm.Nm4 mmN.N m4m.mm mmccmm mo mucw>ocm .H mmN.F w.m mNm pr wom.N 4mm N4o.4N mo4 mNm.4 mo>44ovom .w oom.4 m.o— m4N.F 44m mm4.m N4N mon.4~ 4mm Fo_.4 _:;o4N mmz .N mom m.4 Nwm m44 mmm.N mmp 4N4.m_ mmm mmm.— mmmmchpoc>mz .m NNo.N N.FF nmm._ nmm m_4.m Now NNF.mN m4m 4mN.4 muwcmwz .m mom.m 4.4m 4mN.m mom N4m.m_ mpm mmm.m4 44m.N NoN.F_ moccmm .4 omm.m c.4N Foo.N Npm m4m.4 4m4 NNN.mF mmF.N Nom.m mwpxmLH .m 4mp.— 4.m NNN mmF 4mm.~ mN~ owm.NF M44 44o.N cocpmmxocwuwm .N mom._ 4.N mwm om_ NmN.N MNF oNo.mF mom o4~.4 mwpoaovom .4 em: 48: 2mm: ANNmFV N Amnmpv mmmonmu Co mmmucmu 40 com: mmmuguu 40 new; mcoh mcoh owcpmz 4o consaz Emcoh uwcpmz mo cmaszz mcop uwcumz mo Lona: mcowmwm Azuwpuanocm new: mmmucmu quoh . mmo: mumowiawmmm mpuumo .muwmgw .mwcouwumz .m .ume mmmugmu Co mac» uwgyme =_ 4:4 cam; 40 Logan: :4 mmcwgmucmzmpm xuopmm>w4 .Nmmp .FI>H mpnmk 76 1977. However, because of their very optimistic view-~acknowledged also by the specialist agronomists who made the projections--they can be safely considered as applying to the year 1980. The projected livestock supplies of all species combined have as follows: (a) Serres, 18,500 tons of carcass meat; (b) Kavala, 5,500 tons and (c) Drama, 6,580 tons. For entire E. Macedonia they reach the level of 30,580 tons (Table IV-l). No livestock and meat imports are assumed to take place through any point of E. Macedonia in 1980. The regional projections of livestock supplies were made by allocating province's projected total supplies among its regions. The allocation has been made according to the share of each region to its province's 1972 livestock supplies. That is, first it was calcu- lated the percentage of a province's 1972 total livestock supplies produced in each region of that province. Then, these percentages were multiplied by the projected livestock production of the province in question. Thus the projections of livestock supplies for each region of E. Macedonia were obtained (Table IV-l). Estimation of Regional Meat Consumption Regional meat consumption was calculated as follows: 1. The total meat consumption in each province was estimated by adding net exports (exports minus imports) onto the province's total meat production. 2. Total urban meat consumption of a province was estimated by multiplying the province's urban population (i.e., population of towns having more than 3,000 inhabitants) by the national 77 per capita red meat (beef, lamb and pork) consumption. The implicit assumption made here is that in towns over 3,000 inhabitants, people will consume meat, at the same level as the average Greek consumer. 3. Total urban meat consumption of each province was subtracted from its total (urban and non-urban) meat consumption. The difference represents the total meat consumption by the non- urban population of the province in question. 4. Total non-urban meat consumption in each province was divided by the total non-urban population of that province. Thus, per capita meat consumption by the non-urban population of each province was estimated. 5. Per capita non-urban meat consumption of a province was multi- plied by the non-urban population of every region belonging in the province under consideration. Thus, the total non- urban meat consumption was calculated for each region. 6. Total urban and non-urban meat consumption in each region was added and thus the total regional meat consumption was estimated as shown in Table IV-2. The procedure for calcu- lating per capita non-urban meat consumption in each province is given in Appendix Table A-3. Provincial and regional population was divided into urban and non-urban simply because there is plenty of evidence not yet tested, indicating that per capita meat consumption of urban popula- tion is much higher than that of non-urban population. Given the 78 camp was 4444 mmozp mo mc4mmmuoca cmguczm to 4444444: 4:4 4o 444444o 44cu=4>oLa .N4. .Logpam «:4 an «new .co4uqsamcou puma :o 4444 u4m4a may co4 mczu4au4cm< .4444444444 :4 4444 4:4 L44 444-444 444 44-44 .mm 1mm .44 .N444 .mommcu .mcmcu< .mommcw.mo :o4444aaom .mummcw 4o mu4>cmm 44u4um4444m 44404442 44V "mmucsom .m4qowa ooo.m Lm>o mo mczop 4o co4uw4=noa mg» mew; umcmu4mcou m4 co4um4=aoa cage: 4 NNm.mN co4unssmcou 4mpo4 .> momxm mucoqwm .>4 N44.4 4mN.o4m.N mcmsg< .4N mmm.4 mo~.omm 444co44mmmz4 .oN 44o.m m44.4 oom44 444.mm N4w.4m moo.4m «seen me muc4>oca .444 4m 4m -1 mmN.4 -1 mmN.4 :o4pmmcmgmm .m4 me no .. 4mm.N -1 4mm.N cocmcoc4n4m .m4 MNN mNN -1 mNN.m -1 mNN.w 4aoxoc>mz 0444 .44 ~44 emu mm4 4N4.m m44.m 4cm.m4 4:4mpommoc4 .o4 N44.4 444 mom.4 om4.44 NNm.om 44o.w4 424cc .m4 4mm mam N44 omm.44 044.m omm.44 cogmxoa .44 Nmmam m4m44 mm4.~ m4m.m4 NoN.wm 44m.wo4 444>4x 4o mu=4>ocm .44 4m 4m in mam.N -1 mwm.m moemuoq4o .m4 440 44m o4N 444.N4 mm4.m 444.44 m44oa=ommxcsu .N4 cum.N mNm 44m.4 m4m.N4 wa.m4 omm.mm 444>4¥ .44 4N4 444 «mm Nmm.m4 omo.m «No.4m m44oa=ocmgp4m4m .o4 mm4 mm4 -1 444.4 1- 444.4 co4cosuowoa .m 4444N mmN44 m4w.N mm4.4m4 mm4.mm mmm.~o~ moccmm 4o mu=4>oca .4 44m 044 4m4 4mm.N4 444.m 4m4.m4 mo>44o4om .m cum 444 4N4 4m4.m4 m44.m mmw.m4 4:;u4N 8z .4 mom mom 1- N4m.m i- N4m.m 4mm44mguoc>4z .m m4w 44m mom mmm.m4 4cm.4 4m4.4N 444cm4z .m 44m.~ m4N.4 404.4 mNm.mm 4mo.44 44m.m4 mmccmm .4 4mm m44 444 4mm.NN 4No.4 mmm.mN 444x444 .m 44o com 444 moo.m o4o.o4 mm4.w4 cogpmmxoc4u4m .N mNm m4m 1. 444.44 .1 444.44 m44oaouom .4 44404 cane: cane: co4444mmoa :o4444zqoa =o44442ma¢ concaucoz «cans: 4muo4 N444 .mco4 u4cumz mcmpcmu =o4uqssmcoo :4 =o4ugsamcoo 44¢: :o4444aaoa .NNm4 .mommcw .44co4mumz .u .mcou 044445 :4 =o4ugssmcou p445 44=o4mm¢ .N->4 44344 79 fact that the volume of meat consumption in each region is an important element in the optimal location of processing plants, this procedure was considered appropriate. To estimate the quantities of meat shipped to Athens and Thessaloniki, the surplus meat of E. Macedonia was allocated between them in a ratio of four to one, respectively. That is, 80 percent of exported meat was considered as going to Athens and 20 percent as go- ing to Thessaloniki. This allocation structure has been based upon the 1972 proportions of E. Macedonia's red meat exports to these two cities.27 The exported quantities of meat to these cities are much less of their total meat consumption. Estimation of Livestock Assembly Cost To estimate the livestock assembly cost--the cost of ship- ping live animals from the supply regions to the slaughterhouses, a small-scale survey was conducted in June, 1974. During this survey, cost data were obtained on meat distribution as well. The question- naires constructed by the author for these special purposes and used in this survey are given in the Appendices A-1 and A-2. Ten truckers, engaged in both livestock assembly and meat distribution, were interviewed. The information they provided with regard to livestock assembly refers to: (a) the sizes and types of trucks most commonly used in livestock transportation at various dis- tances; (b) the transportation cost they charge per full load of live 27Data provided by the veterinary offices of the Ministry of Agriculture in Serres, Kavala and Drama. 80 animals shipped at various distances with different sizes and types of trucks; (c) the degree of capacity utilization of trucks engaged in livestock assembly; (d) the truck capacity in terms of number of head of live animals of each specie. On the basis of these data, the average livestock assembly cost per full truck load was calculated (Table IV-3). To find the assembly cost per head of cattle or any other specie, assembly cost per full truck load was divided by the number of cattle or other ani- mals which each size of truck can transport (Table IV-3). In order to estimate the livestock assembly cost in terms of meat equivalents, the assembly cost per head of cattle was multi- plied by five, since one head of cattle yields an average carcass weight of about 200 kilograms of meat, or one-fifth of a metric ton. Thus, the livestock assembly cost per ton of meat equivalents is determined at 100 percent of truck capacity utilization (Table IV-4). To compute the livestock assembly cost in terms of meat equivalents at 50 percent of truck capacity utilization, the assembly cost (in terms of meat equivalent) at full (100 percent) truck capacity utilization was multiplied by two, since to transport a specified volume of live animals, trucks of certain size utilized at 50 percent of their capacity have to make twice as many trips as the same truck when it is utilized at full capacity (Table IV-4). In this analysis, the assembly cost rates of two ton trucks has been used, since this was the most commonly used truck in the study area, as indicated by truckers and butchers interviewed. Fur- thermore, the cost rates of two ton trucks at 50 percent of capacity 81 mmo; m4 so .mn544 mm co .m4uumu m mcou 4 mo mxuac4 .4 moo; 44 so .masm4 04 so .m4upmu m meow N\4 N on N we mxu=c4 .m 444; N4 44 .44244 44 Lo .444444 4 4:44 NN4 4 44 4 to 444444 .N moo; o co .mnsm4 mN Lo .444440 N mcou N\4 op 4x4 4o mxuac4 .4 m4ms4c< m>44 mo msco4 =4 mxuzc4 4o 44444440 m~4m xu=c4 mc4ucouum mzo44o4 m4 «L4 4444 vmmg4 "mcmxuacu umzm4>cwuc4 4:4 44 .pcoqmcmgp emu Nose» 4o w~4m some su4gz 444444 me 4455:: as» An umu4>4u m4: 44o4 Nose» 44:4 can umou x4nEmmm4 4;» .444440 me 444; can umou >4nemmmm mg» 4:44 04 44v U484 4:44 :4 .mommcu .m4cocmumz .m 4o 454cc .444>4¥ .mmccmm .mmuc4>ocg «meg» msu c4 mcmxuch umzm4>cmuc4 ”mucaom mN4 mm4 OON mNN coo.4 com com omn ooN . 4m4 N44 444 m44 omm com ooN CON com om4 . 4o4 4m mm mN4 o4N omo com com ON4 oo4 1 4m mm 40 oo4 om4 om4 OO4 oo4 omm om u 44 om mm mm m44 OQ4 omm owm omm o4 . 4m o4 m4 we mN4 QNm QNN oNN omN om . 4N 4m N4 mm oo4 oNN omN QNN OON ON 1 44 mN Nm m4 m4 ooN om4 ON4 om4 o4 1 o meow meow meow meow meow meow mac» 4 N\4 N o» N N\4 4 ca 4 N\4 on 4\4 mean 4 N\4 N on N N\4 4 op 4 N\4 0» 4\4 mcmumso44x 4o mxuac4 mo mxuth. .mo m48244 .40 mmu=44 mo mxusc4 mo m4u=g4 4o mxuzc4 mo mxu:g4. :4 44v 444449 mo 44¢: cma mmssumgo 44o; xuzc4 44:4 cwa mmsgumco mmuzmpm4o :4 a4nsmmm< xuopmm>44 4o pace :4 x4nsomm< xuopmm>44 mo umou _ 4o mmcmm .4444 6:34 .mummcw .m4cocmumz .m .444; 4c conga: 4o magma c4 moan; “moo x4nemmmm Nuoumm>44 .mu>4 m4n44 82 .Nu4umamu 44:4 pm umn: m4 44 cmzz cusp 44404440 mp4 4o ucmucma Om pm Ommz m4 44 cm:: ma4gp m4n=ou mxme cu m4;.xo=cp mgu .mss4o> msmm mg» ucoamcmcu op muc4m .ozp ma Om44a4p4zs.mmz 44404440 4434.44 “mom >4nemmmm mzu .co4umn444pz Ap4umamo xuscu 4o pcmucma Om 44 .mucm44>4:4m poms 4o magma :4 umou x4nsmmmm xuopmm>44 msu 4:44 o4 NNV .co» u4c4ms a mo 544441m=o so .msmNOo44x OON “scam 4o umms mmmucmo 4o usm4m3 mOmcm>4 cm mu4m4> m4uymu 4 muc4m .m 4a_4m44q4p4aa mm; Nm1>4 m4nm4v m4pumu 4o 44m; cma pmou z4nsmmm4 ms» .mpcm4m>4:4m 44mg 4o mEme :4 “mac >4nemmmm soapmm>44 mzp Oc44 o4 N4v .mi>4 m4n44 "mugzom OON.4 omm.4 OOO.N OmN.m ONO mOO OOO.4 ONO.4 OON i 4m4 Om4.4 ON4.4 OmN.4 OOm.m mmm mmm ONO OmN.4 Om4 . 404 O4O Omm OmN.4 OO4.N mO4 O44 ONO OmO.4 OO4 1 4m OOm ONO OOO.4 OOO.4 OON mmm OOm OOO Om 1 44 OOm OmO OOO OmN.4 OON m4m mN4 ONO O4 1 4m OO4 Om4 0 OOO OmN.4 OON mNN O4m ONO OO 1 4N O4m ON4 Omm OOO.4 ON4 O4N mNN OOm ON 1 44 OON ONO Om4 OON mN4 OO4 m4N mNm O4 1 O mcou meow meow mac» mcop mcou mac» 4 NN4 N on N NN4 4 o» 4 N\4 op 4N4 mac“ 4 NN4 N ow N N\4 4 Op 4 NN4 op 4N4 mcmmmso44¥ mo mxuzc4 4o mxosc4 4o mxuzg4 4o mxusc4 4o mxuzg4 4o mxu=c4 mo mxozg4 4o mxuac4 c4 .44»: 4u4umamu guac4 mo ucmucmm Om NmOOO .ummz any OO4 qu,mmsgumNO c4 .mxuac4 4o mmmcmm 4o :o4 cma mmeguch c4 Amucm4m>4=4m 44m: 40 :o4umN444uO >u4umamu 44am smug: Ampcm4m>4=4m mucmpm4O 4Nvmazm4 =4 Ommmmcnxmv pmoO z4nemmm< Nooumm>44 44m: :4 Ommmmcaxmv x4nemmm< xuoumm>44 4o pmou .4NO4 mesa .mumch .m4coumumz .N .mpcm44>4:4m poms 4o magma :4 .mmumc umou a4nsmmmm xooumm>44 .41>4 m4am4 83 utilization was used in the basic solution model, since the interviewed marketing firms have indicated that this is the most common case in livestock assembly in the area of E. Macedonia. Estimation of the Livestock Processin97Cost To estimate the total cost--and through them the unit cost-- of livestock slaughtering for different plant sizes, input-output re- quirements must first be determined and then cost rates on them must be applied. In this analysis, the slaughtering cost data were basi- cally obtained from the FAO study.28 However, some adjustments have been made on these data in order to incorporate in them the inflation which took place since 1966 when the FAO study was conducted, and also the input price differentials whenever they exist among the various regions for some inputs. From the 17 different plants of various locations (provinces) analyzed by the aforementioned FAO study, only 7 plants with distinctly different sizes were selected in this analysis. These are the plants of Tripolis, Ioannina, Lamia, Didymotichon, Komotini, Kavala-Drama, and Trikala-Karditsa. These plants were labeled with the letters A, B, C, D, E, F, and G, respectively. The process of reestimating the slaughtering cost in the above mentioned 7 plants under conditions of E. Macedonia at l974 price levels is shown in Table IV-5. However, for a better understand- ing of this table, a brief explanation on the costing follows. 23mm. 84 m4m.4 Nom.m omm.m mmN.m hmo.m mmm.4 mum._ AmmUPLQ 4Nm4v meagoaeu acamsogo =4 4ca4 can pewsa4=cm .mmc4u44zn 4o mumou 4mzcc< .44 OO4.4m Om4.0m O44.0N MOO.mN mmN.4N OOO.m4 ON4.m4 4mmu4ca 44O4V «assumcu ucmmsogu :4 4:44 4:4 ucmsa4=4m .mmc4u44zn mo mumou 4muo4 .O4 coo 04m o4m owe com omm ohm 4mm04ga 44m4v magguaea acamaogu =4 mmaosgmugmsm4m oz» 43 com: uco4 4o ma4m> .O ON O4 44 m4 N4 44 m Mmgmpmso44x menacm c4v «mac; -gmp; :m4m as» An 4mm: vcm4 4o mmg< .O OO4.4m O40.0N OON.ON O44.mN mO0.0N omm.m4 mm4.m4 mmu4ga 4404V magnumgu Ocmmzosu c4 ucmsq4som use mOc4u44zn 4o mumou .4 NOO.NN O4m.m4 4mN.O4 4N4.04 40m.m4 O40.0 OO4.0 4mmu4ga OOO4V mmszumcu vcwmaocu c4 uzmsq4sam ucm mmc4u44zn mo mumoo .O O44.4 O40.m OOO.m OON.m OOO.N OOO.4 O40.4 Ammu4ca OOO4V magnuogu ccmmzogg =4 ucmsq4zcm 4o mumou .m N40.44 O44.m4 4mm.44 444.m4 NOO.44 Omm.4 OOO.N 4mmu4ga OOO4V mascomgu Ocomzozp :4 mmc4n44=n mmsosgmwzmsm4m mcp mo mumou .4 mOO.N Omm.N OOm.N om4,N OOO.4 OOm.4 OON.4 AmmemE .cm :4V mmc4u44sn mmzogemugmsm4m msp we mmc< .m OOm.m4 OOm.m OO0.0 OOO.4 OOO.m OOO.4 Om4.N 4444mzccm _ paws mmmugmu mo mcou :4V au4umamu pcm4m .N mm mm mm mm ON 04 44 Aaav can paws mmmugmu 4o mcop :4V xpwomamu 4:44; .4 O m N O O m < memOH pmou mpcm4a O=4mesmzm4m .mummgOl.m4cocmumz .m .co4umu444p: xu4umamo 44:4 pm One mucm4a 4o mmN4m ma m=4gmpsmzm4m xuopmm>44 we umoo .m->4 m4nm4 85 .macmsumznum wum4gaogaam mg» mxme on cause :4 gospsm mgu 4n Ommmmoosg gmgugsm :wmn m>m:,mogOOm m4ca seem Omc4muno muwu u4mmn mg4 .mN mES. .NOO4 .wsom =.mummgO c4 paw: Ocm xooumm>44 4o mc4poxgmz= .O 4mpO4 .mN 440.0 440.m 4ON.m O40.4 440.m mOO.N O40.N 4mmu4ga 4404V mascomec ucmmzosu =4 =o4u -mcmno mmsoggmucmzm4m 4o mumoo 30x44 4ouo4 .NN OO4 4mm 4mm mmm 404 OOm 44N 4mmu4ga 4404v mascumgc Ocmmzosp :4 mwmcmaxm mm44gnzm mmmuoga new mucmcmp=4mz .4N O4m O44 Own O4m OOm OO4 OO4 4mmu4ga OOO4V mossumgu ucmmzocp c4 mmmcmaxm mm44aazm mmmuoea new mucmcmuc4mz .ON 4mO.4 44m.4 m4m.4 m4m.4 ONO 4mm O4O 4mmu4ga 4404v magnumgn Ocmmaocu :4 4mmmmzv mmmcmaxw :o4umgum4c4544 .O4 OmN.4 OOO.4 Omm Omw OOO Omm OO4 4mmu4ga OOO4V mmsgumgu Ocmmsogp c4 4mmmmzv mmmcmaxm co4umgum4c4su< .O4 OOO.4 Nm4.4 OOO 44m 4mm 4Nm NNN Ammu4ga 4404V muscuoec ucmmzozu :4 um~444uz swung ucm xu4u4gpum4m mo mumoO .44 OON.4 NmN. O4O N4O OOm mmm 444 4mmu4gg OOO4O mascuugu ucmmso;u :4 um~44443 Loam: 4:4 xu4u4epum4m 4o mgmou .O4 .mmm.4 Omm Omn 4mm N44 mNN Om Ammu4gg 4404v mascumgu Ocmmzosp =4 :o4pmgmao :4 4mm: 44o 4o pmoO .m4 OO4 44N m4N OON ON4 mo m.ON 4mmu4ca OOO4V mascumgu ucmmzo;u =4 :o4pmgmqo :4 now: 44o mo amoO .44 N40.m 400.4 400.4 mm4.m NmO.m O40.4 4mm.4 4mmu4ga 4404v mmssumgu vcmmzozu :4 . :o4amgmao ucm4a :4 Own: Lonm4 «:4 4o mama: .m4 O4N.m O40.N O40.N O4N.N OOO.4 O4N.4 OOO 4mmu4ga OOO4V maggomgu ucmmaocu :4 co4umgmao u=m4O :4 4mm: Loam4 ms» mo mmmmz .N4 86 l. The total costs of buildings in the Kavala-Drama plant (as it was calculated by the FAO study) was divided by the area covered by the buildings of this plant in order to find the costs of buildings per square meter. This cost then has been applied to all seven plants in order to take into consideration the regional input price differen- tials, since this Kavala-Drama plant belongs in the area of E. Mace— donia. 2. The unit cost of buildings per square meter has been multi- plied by the area in square meters which the buildings of each of these slaughter plants cover. Thus, the value of buildings of all seven plants under conditions of E. Macedonia but at l966 price level have been calculated. 3. These costs of buildings and equipment (as given originally in the FAO study since they are mostly imported items) were summed up. Their sum then was multiplied by l54.7, because inflation has risen by 54.7 percent on the average from 1966 to 1974. Thus, the 1974 prices of buildings and equipment were estimated. 4. The value of the land covered by each slaughter plant was calculated by multiplying their corresponding total land area in stremmata (1,000 square kilometers) by 30,000 drachmae-ethe average price of a stremma in these areas--according to 1974'land cost data.29 5. The sum of costs of buildings, equipment and land at l974 prices was multiplied by l2.5 to estimate the annual costs (interest 29Data proVided by the extension agronomists of the Ministry of Agriculture in Serres, Kavala and Drama. 87 of the money invested on buildings, equipment and land, depreciation for buildings and equipment and taxes for land). This 12.5 percentage for calculating these annual costs has been used by the authors of the FAO study and this percentage has been adopted in this analysis, because it seems to be a fairly realistic one under the present eco- nomic situation in Greece. 6. The costs of all but oil items (e.g., electricity, water) were adjusted to 1974 price level from that of 1966, by multiplying their 1966 costs by 154.7 in order to incorporate in them the infla- tion of 54.7 percent which took place on the average from 1966 to 1974. 7. To adjust oil's 1966 cost to 1974 prices, its 1966 cost was multiplied by 343.3, since the price of oil has increased by 243.3 percent in Greece from 1966 to 1974. 8. To calculate the annual total fixed costs (TFC) of a slaughter plant, the following cost items were added: (a) annual costs of buildings, equipment and land; (b) all the administrative expenses; and (c) one-half of maintenance and other (office supplies, etc.) expenses. 9. To calculate the annual total variable costs (TVC) of a slaughter plant, the following cost items were added: (a) the cost of labor; (b) the cost of oil, water, electricity; and (c) one-half of the maintenance and other (office supplies, telephone, etc.) ex- penses. 10. To estimate the livestock slaughtering cost per unit (actually per ton of carcass meat equivalents), the total costs (TC), i.e., the 88 sum of total fixed and variable costs, were divided by the annual volume of livestock slaughterings (expressed in terms of meat equiva- lents) of the corresponding plant size. To indicate whether or not economies of size are in exist- ence in these 7 slaughterhouses of different sizes, their short-run (SR) costs were calculated (Appendix Table A-5) and the corresponding SR cost curves are graphically presented in Figure IV-l. In each cost curve, the numbers 1, 2, 3 and 4 represent 80, 90, 100 and 110 percent of plant capacity utilization respectively. Capital letters A, B, C, D, E, F and G reflect the sizes of plants equivalent to 2,750, 4,000, 5,000, 7,000, 8,000, 9,500 and 15,500 metric tons of carcass meat respectively. These SR slaughtering cost curves were constructed on the basis of the following calculations: 1. The volumes of livestock slaughterings (always expressed in terms of meat equivalents in this analysis) were calculated for four different levels (80, 90, 100 and 110 percent) of plant capacity utilization. 2. The total variable costs for these different levels of plant capacity utilization were calculated by multiplying the per- centages of plant capacity utilization by the total variable cost of the plant in question. 3. This new TVC was added to the unchanged TFC and a new TC of processing for the corresponding volume of livestock slaugh- terings was calculated. THOUSAND DRACHMAE PER TON OF CARCASS MEAT 2.2 2.l 2.0 1.9 1.8 l] 1.6 1.5 144 1.3 1.2 1.1 1.0 Figure IV-l: 89 l 2 3 4 5 6 7 8 9 l ll 12 THOUSAND TONS OF CARCASS MEAT l3 l4 .15 16 I7 Short-run cost curves of seven different size livestock slaughtering plants, E. Macedonia, Greece, 1974. 90 4. This new total slaughtering cost was divided by the corres- ponding volume of livestock slaughterings and thus, the unit cost of processing for the respective level of plant capacity utilization was calculated. 5. 0n the basis of unit processing cost (presented on the verti- cal axis) and the corresponding volume of livestock slaugh- terings (presented on the horizontal axis) the SR cost curves of Figure IV-l were constructed.. Estimation of Meat Distribution Cost The basic data required for estimating the meat distribu- tion cost-~the cost of shipping carcass meat from slaughterhouses to consumption centers--were obtained through the same survey used for obtaining the livestock assembly cost data. Details as to how this survey was conducted have been given already in section four of this chapter. The information provided by the interviewed truckers with regard to meat transportation cost refers to: (a) the sizes and types of trucks most commonly used in meat transportation at various dis- tances; (b) the transportation cost they charge per ton of carcass meat for different distances; (c) the degree of capacity utilization of trucks engaged in meat transportation, etc. On the basis of these data, the average unit meat transpor- tation cost was calculated. The unit cost is given in drachmae per ton of carcass meat shipped at different distances and with the cor- responding sizes of trucks, utilized at full capacity. (Table IV-6). 91 Table IV-6. Meat transportation.cost rates in drachmae per metric ton of carcass meat, E. Macedonia, Greece, June 1974. Meat Transportation Cost Rates Size of Truck Most . Distance in Drachmae per Ton of Carcass Commonly Used for Ranges Meat Under Full Truck Capacity the Corresponding in Utilization Distances in Meat Kilometers Transportation 1 - 30 300 2.5 or 6 tons 31 - 60 350 61 - 100 400 101 - 150 500 6, 10, or 12 tons 151 - 200 600 201 - 300 800 301 - 400 1,000 10 or 12 tons 401 - 500 1,200 501 - 600 ' 1,400 601 - 700 1,600 Source: Questionnaires having the interviews with ten truckers in E. Macedonia, Greece, in June 1974. 92 As this table shows, the longer the distance, the bigger trucks are utilized in meat transportation, since the chances of shipping large amounts of carcass meat are greater. As the interviewed truckers have indicated, the trucks which are used for shipping carcass meat to distances beyond 30 kilometers should be equipped with a refrigerating system. Within the distance of one to 30 kilometers any type of non-refrigerated truck can be used. In relatively short distances of one to ten kilometers--a usual distance between a slaughterhouse location and the nearest large consuming center--the most commonly used truck is the tricycle of 1/2 ton capacity or any other small non-refrigerated truck. Summar In this chapter, the regional livestock production and meat consumption, the unit livestock assembly and processing cost, and the unit meat transportation costs were estimated. These estimates are very important because they are the basic data for the computer analysis. Specifically, the regional livestock supply and meat consumption (along with plant sizes) consti- tute the "constraints" column of the computer matrix while unit live- stock assembly and processing and meat distribution costs constitute its "unit cost" row. Regional livestock slaughtering supplies were estimated on the basis of 1972 slaughterings of each community which belongs in the region under consideration. That is, the livestock slaughterings (expressed in tons of carcass meat) of all communities belonging in 93 a certain region were added up and thus gave the region's livestock supplies for the year 1972. The projections of livestock supplies for each region were made by allocating each province's livestock projections to every region belonging in that province in accordance with the region's share to its province 1972 supplies. Regional meat consumption was estimated as follows: each region's urban population was multiplied by the national per capita meat consumption to give the total meat consumption of the urban pop- ulation of the region in question. On this was added the total meat consumption by the non-urban population of the region. The latter was estimated by subtracting the total net meat exports from the total meat production of the province in which the region in question be- longs. From this difference the province's total urban meat consump- tion was subtracted to arrive at the total non-urban meat consumption. This, then was divided by its total non-urban population to obtain per capita meat consumption of the non-urban population of the province in question. Then, this was multiplied by the total non-urban popu- 1ation of each region belonging in the province in question to obtain the total regional non-urban meat consumption. Livestock assembly and meat distribution costs per unit of product have been estimated on the basis of cost data provided by ten interviewed truckers of the area engaged in these activities. These unit costs are averages of the cost data of all interviewed truckers and refer to both shipping distances and sizes of trucks employed. 94 The unit livestock processing cost was estimated for dif- ferent sizes of plants and different levels of their capacity utiliza- tion. The basic livestock, processing cost data were taken from the FAO report. Only the appropriate adjustments were made on them to incorporate both the inflation taken place since the study was con- ducted and also the local prices of the inputs (labor, land, etc.) used. CHAPTER V NUMBER, SIZE AND LOCATION OF LIVESTOCK SLAUGHTERING PLANTS Introduction This chapter contains the empirical results of the computer analysis undertaken on the basis of the data generated in the prev- ious chapter. These results refer to: a. the optimum number, size and location of the slaughtering plants; b. the optimum flow of live animals from the supply regions to the slaughtering plants; c. the optimum flow of carcass meat from the slaughtering plants to the consumption centers; and d. the minimum aggregate costs of livestock assembly, processing and meat distribution. A set of two tables summarize the results of the optimum solution in each model. The first table gives the optimum flow of live animals from the supply regions to the slaughterhouses, as well as the total livestock volume supplied by each region. The second table gives the optimum flow of carcass meat from the slaughterhouses to the consumption centers, as well as the total meat volume consumed 95 96 in each consumption center (region). Both tables give the optimum number, size and location of the slaughtering plants. A brief elaboration of the findings is added, accompanied by the relevant graphs whenever it is felt necessary. Furthermore, a comparative analysis of these findings for all the alternative sol- ution models will follow in order to facilitate the decision-making process. The Basic Solution Model As it has already been described in the previous chapter, this model is characterized by: (l) 1972 livestock supplies; (2) 50 percent capacity utilization of trucks engaged in livestock assembly; (3) 100 percent capacity utilization of trucks engaged in meat dis- tribution; (4) 100 percent capacity utilization of slaughtering plants; (5) use of modern technology in livestock slaughtering; and (6) 20 supply regions, 21 consumption centers and 10 potential slaugh- tering plants. The optimum (minimum cost) solution of this model was ob- tained for two plants, those located in Serres and Kavala. The plant sizes are 15,500 tons of carcass meat for Serres and 8,000 tons for Kavala. The first plant is used at full capacity, while the second one is used at about 97 percent of its capacity. As mentioned in Chapter III, no plant is allowed to be utilized above its full capac- ity. The procedure for determining the optimum number of plants was described in the previous chapter. This is a stepwise procedure. 97 It starts from all the potential plants and ends up with the optimum number. Indeed, the computer analysis has shown that as the number of ten potential plants was reduced--and therefore the volume pro- cessed in each of the remained plants was increased--total costs (processing plus transportation) were continuously declining. When the number of plants was reduced to four, the solution was stabilized. That is, total costs did not automatically decline further since neither the number of plants declined nor the volume of livestock processed by each plant changed in the next computer run. At first glance, one might think that this was the optimal solution. However, when the number of plants was reduced to three--by eliminating the plant with the smallest processing volume--total costs continued de- clining. They continued to do so until the number of plants became two. The analysis did not proceed further to one plant, since the biggest plant (15,500 tons of carcass meat) under consideration in this study cannot process all the livestock supplies equivalent to 23,372 tons of carcass meat. In addition, looking at Figure IV-l (p. 89) it is clearly seen that the long-run slaughtering cost curve (the envelop curve of the short-run cost curves--which does not ap- pear in the graph) has almost flattened beyond the level of 15,500 metric tons of carcass meat equivalents. This means that no signifi- cant economies in slaughtering would be expected to be realized with a plant of size, say, 24,000 metric tons of carcass meat equivalents, so that to be capable of processing all the supplied livestock in the area which amounts to 23,372 metric tons of carcass meat equivalents. In contrast, total transportation cost (livestock assembly plus meat 98 distribution) is expected to substantially increase with one slaugh- ter plant, since both livestock and meat must be transported in rel- atively longer distances. In other words, with only one slaughtering plant in E. Macedonia, total transportation cost is expected to in- crease more than slaughtering cost is expected to decrease and thus total costs would be greater as compared to two slaughtering plant system. Given the fact that total costs were lower under two plants than under any larger number of plants, the optimum number of live- stock slaughter plants is two in the area of E. Macedonia under the specified conditions of this analysis. The optimum size of plants is simultaneously determined with the optimum number of plants. This is given by the volume which each plant is going to process according to the computer analysis. If this volume does not coincide exactly with the size of any one of the seven plant models considered in this study (p. 88), then the size of the plant model which approximates the most to the estimated volume of livestock to be processed by a certain plant specifies its optimum size. Thus, for the plant of Serres the optimum plant size is 15,500 tons of carcass meat, since the optimum solution has deter- mined this amount to be processed by this plant. For the plant of Kavala the optimum plant size is 8,000 tons of carcass meat since the remained volume of 7,872 tons which was estimated to be processed by the plant of Kavala approximates the most to the model plant size of 8,000 tons of carcass meat. To find the optimum location of the two (optimum number) plants, various combinations of two plant sites were investigated. 99 However, these combinations were restricted to most reasonable ones, i.e., to those locations in which either livestock production in the first place or meat consumption in the second place or both are in high density. The following three combinations of two plant sites were examined: a. Serres - Kavala, yielding a total cost of 51.38 million drachmae; b. Serres - Drama, yielding a total cost of 51.59 million drachmae; and c. Serres - Doxaton, yielding a total cost of 51.92 million drachmae. As it is seen above, the combination of Serres - Kavala plants gave the minimum aggregate costs of livestock assembly and processing, and meat distribution. This implies that the optimum lo- cation of plants under the specified conditions of this basic solu- tion model is Serres and Kavala. This optimum solution reveals that economies of size exist in livestock processing, since as the number of plants was reduced-— and therefore the size of plants was increased--total costs Of pro- cessing and transportation were declined. It is interesting also that the plants have a tendency to be located in those regions in which either livestock production or meat consumption or both are high. It is noticeable that the biggest plant of the optimal solu- tion is located in the region of Serres which ranks first in both 100 livestock production and meat consumption and is centrally located. However, the other optimum plant location is Kavala despite that it is not centrally located and its region ranks eighth in the volume of livestock supply, but which ranks second in the volume of total meat consumption. This optimum solution seems to be a logical one, because in areas of dense livestock production the assembly cost is expected to be comparatively low, since trucks do not have to travel in relatively long distances to assemble the live animals available for slaughter- ing. On the other hand, in regions with high meat consumption, the meat distribution cost is expected to be low, since it is not neces- sary for carcass meat to be shipped relatively long distances from the slaughterhouses to reach the consumption centers. Table V-l gives the optimum flow of live animals (expressed in terms of meat equivalents) from the supply regions to the slaugh- tering plants of Serres and Kavala. It is readily seen from this table that the slaughtering plant of Serres is supplied by all the surrounding regions of the province of Serres, except that of Mavrothalassa which supplies the plant in Kavala. The plant of Serres also processes all the imported livestock. Prossotsani, a region of the province of Drama, partially supplies the plant of Serres. The plant of Kavala is supplied by the rest of the regions. Table V-2 gives the optimum flow of carcass meat from the slaughtering plants of Serres and Kavala to consumption centers. As this table indicates, the plant of Serres supplies with carcass meat all the regions of the province of Serres, plus two regions-- 101 mmaoggmugmzm4m . . uum c Oc4mmmuogm NNO.ON NNO 4 OOO O4 ; xuowmm>44 4muo4 . cozuosogm .ON OOO.O OOO O =o4pmmcmgmm .O4 4O4 4O4 cogmcog4v4m .O4 OO OO 4Ooxog>mz oumx .44 non OOO ON4 4=mm46mmogm .O4 mm¢ mm o MEMLQ .mp OOO.4 OOO.4 coumxoO .44 m2; 3 F 82385 .2 44N 44N. m44oazommxsgu .N4 OO4.4 OO4 4 m4m>mx .44 43 43 3488853; .2 OOO OOO co4goguonom .O OON OON ONO mo>44ouom .O 8O SN; .25: 82 4 O4N.4 mmmm4mgaog>mz .O 4OO 4OO NOO.4 mu4LO4z .O 4mm.4 4O4.O mmggmm .4 404.0 4OO.N m44xmg4 .O Mmm.N N44 cogpmmxom4u4m .N m44o ouom .4 :mwmuc cog» zumm an cop m44 m44oa 4:;upN a» co ~44 -mmxog » AER... xuoum. 25.5 .. 18:8 figs. -3955 mm. 14.3 mm.» m 3:24 1 . 203mm .4433 .49.: 438 can -35 at: z . 42 .55 mucm4mimmwcmusmsm4m .FwUOE memn "AuOOE mmmogmu 4o meow o4spms =4 Omgammme mE=4o>v N404 .mummgO .mpcoumumz .m .mmmzosgmugmzm4m mg» on mco4mmg :o4pusuoga ms» sog4 m4me4cm m>44 mo 3o4m .4u> w4nm4 102 amnesgmpsmam4m NN0.0N NNO.N OO0.04 zoom :4 O=4mmmuogm xuoumm>44 4muo4 N44.N 440.4 OO0.0 mcmsu< .4N OOO.4 OOO.4 4x4co4mmmmg4 .ON 4O 4O :o4ummcmgca .O4 OO OO cosmcog4u4m .O4 ONN ONN 4Ooxog>oz oumx .N4 N44 N44 4cmmuommoga .O4 N4N.4 N4N.4 6E2O .O4 NOO NOO copmeO .44 8 4m 823an .2 44O 44O m44oa=ommxggo .N4 ONO.N ONO.N m4m>mx .44 ONN ONN m44oazogmsgww4m .O4 OO4 OO4 :o4gozuouom .O N4O N4O mo>44ouom .O ONO ONO 4:;u4N mmz .N OOO OOO Ommm4mguog>mz .O O4O O4O mu4gm4z .O 440.N 440.N muggmm .4 4OO 4OO m44xmgm .O ONO ONO cogpmmxog4u4m .N ONO ONO m44oaouom .4 gamma; zoom Na O44 m44oa coca O4Oqsm xuoum OEOLO uanO -oaaom m4m>m¥ usogmgp 4JMMMN -4mw42 mmngO -“whH -mmxog mco4mwm O4OO3O -m>44 4muo4 -O4LOO -4m4O . -404O mucm4a chgmunmsm4m .4muos o4mOO "NNO4 .mummLO .Owcoumumz .O .mgmpcmu :o4uassmcoo mg» on mmmaosgmusmzm4m msu sogm mcop u4gpms :4 name mmmugmu mo zo4u .Nu> m4nO4 103 Prossotsani and Kato Nevrokopi--of the province of Drama, and lastly the exporting points of Thessaloniki and Athens (partially). The plant of Kavala supplies all the regions of the province of Kavala, all but the aforementioned regions of the province of Drama, and par- tially Athens. The optimum flows of both livestock slaughterings and car- cass meat is also given graphically in Figure V-l. The two-way ar- rows mean that a region Supplying a slaughter plant with live animals is also receiving carcass meat from that plant. One-way arrow from a supply region to a slaughter plant means that while this region sup- plies with livestock the indicated plant, it does not receive back carcass meat from that plant. To the contrary, one-way arrow from a slaughter plant to a region means that while this plant supplies car- cass meat to that region, it does receive from it any livestock sup- plies. The Alternative Solution Model II This model differs from the basic one by only the livestock assembly cost. Specifically, it was assumed that under this model, the trucks engaged in livestock assembly will be utilized at full ca- pacity instead of 50 percent capacity assumed in the basic model. The optimum solution of this model ended up with the same number, size and location of the slaughter plants as that of the basic model. Concretely, the optimum solution of this model consists of two plants, located in Serres and Kavala and having sizes equiva- lent to 15,500 and 8,000 tons of carcass meat, respectively, since 104 .H Hove: "muouamu :owuqasmcoo onu ou momaonuounmamam onu scum umoa mmmoumo mo 30am cam mmmnonuounwamam mnu ou mdoamou hammsm on» scum mamafiam 0>HH mo 30am "HI> muamfim dowuonuovom mamnu<.v a»? SHOE 8% o) wwa.asommhuno O - Q . OH H OOQNHO ._ fiOSUQfl—OHM «moxou>oz oumm noumnouwvfim 105 the corresponding volumes of livestock estimated to be processed by these plants are 15,500 and 7,872 tons of carcass meat equivalents. The minimum aggregate cost of the optimal solution of this model amounts to 45.87 million drachmae. Comparing this with that of the basic model, 51.38 million drachmae, it is lower by 5.51 million drachmae. Obviously, this difference can be solely attributed to the reduced assembly cost due to the full capacity utilization of the trucks engaged in livestock assembly. The optimum flow of live animals from the supply regions to the slaughtering plants of Serres and Kavala is given in Table V-3. It is clearly seen that this flow is exactly the same as that of the basic model (Table V-l). The optimum flow of carcass meat from the slaughtering plants of Serres and Kavala to the consumption centers (Table V-4) differs very slightly from that of the basic model (Table V-2). In particular, under this model, Prossotsani and Kato Nevrokopi consump- tion centers are supplied with carcass meat from the slaughter plant of Kavala, while in the basic model they were supplied by the plant of Serres. Given the fact that the sizes of these plants are the same under both models, Athens is necessarily supplied with larger quantities of carcass meat by the plant of Serres and smaller quanti- ties by the plant of Kavala under this model II. The Alternative Solution Model III This model differs from the basic one by only the number of supply and consumption regions and number of potential slaughtering 106 masongmusmso4m . comm c4 O=4mmmuoga NN0.0N NNO.N OOO O4 xuoumo>44 4Ouo4 . conumfiosm .ON OOO.O OOO O .co4ummcmgmm .O4 404 404 cosmcog4u4m .O4 OO OO 4Ooxog>mz apex .N4 OOO OOO ON4 4=mmu0mmogq .O4 OO4 ON . usage .O4 OOO.4 OOO. couaxoa .44 mm4.4 mm4 P mosapoama .m4 44N 44N. m44oaaommxggo .N4 OON._ OON P apasmg .__ 4NO 4NO m44oazogmgu4m4m .O4 OOO OOO 84.8588 .O OON OON ONO mo>44ouom .O me m¢Na_. .pCEUvN 6&2 .N O4N.4 mmmm4mzuog>mz .O NOO NOO Nmm.F mp4gm4z .O NOO.4 4ON. mmggmm .4 44N.m 4OO.N ~4_¥mgH .m 400.N NNN cosummxogwu4m .N Nmm OOO m44oaouom .4 m cowmuc O cog» umm NO O44 m44o 4:;u4N mu muggw O44 -mmxos mco4Omm O4OOOO a4amzw sumwm OEOLO -umwo -mmumw m4m>m¥ smedw mmz _.OLO4z O -xmgH -4O4O 1 fi I m>44 4 u 4 1mmmm4mimcmgmuzusm4m me=_o>O «Na. . m 04: " paws mmmugmu .40 95.4 2:4qu 4:. vmgzmwme . .. O O .mumeO .O4cocmomz .O .mmmaosgwwgwaw4m OOM op m=o40ms :o4uuscoga Os» sosm m4ms4cm m>44 4° :o4O O > 45 4 107 mmaoggmpsmam4m NN0.0N NNO.N OO0.04 comm an mucus -a4smgao paw: 44p04 N44.N OOO OO0.0 mcmgu< .4N OOO.4 OOO.4 434cc4mmmms4 .ON 4O 4O :o4ummcmsua .O4 OO OO cogmcog4u4m .O4 ONN ONN 4Ooxog>mz oung .N4 N44 N44 4cumuowmogg .O4 N4N.4 N4N.4 msmsO .O4 NOO NOO coumeO .44 4O 4O mosmuoa4O .O4 44O 44O m44oaaommNgOO .N4 ONO.N ONO.N m4m>mx .44 ONN ONN m44oazocmsu4m4m .O4 OO4 OO4 :o4gosuouom .O N4O N4O mo>44ouom .O ONO ONO 4csu4N 8z .N OOO OOO mmmm4mguog>mz .O m4O m4O MOPLOPz .O 440.N 440.N mmggmm .4 4OO 4OO m44xmgH .O ONO ONO cogummxog4u4m .N ONO ONO m44oaovom .4 cowmug zoom On O44 m44oa cogu x4ansm xuoum OsOLO numWO -oazom O4O>O¥ isogogp 4me4N upmw4 mmggmm -“WhH -mmxog mgmucmo co4paszmcou -ms4p Papo4 -maggo -Om4m z . .2 -444m .wucm4m:ucwgumga=m4m .HH 4muos "NNO4 .mummgO .m4conmumz .m .meuch cowHaEzmcoo 9.3. Cu. mmmzorzmpsmzmpm ms“ SOLO. mCOH 0?»qu 4:. #004: mmmusmu ..._.o 30F...— .4u> m4nO4 108 plants. Specifically, under this model some regions were integrated into one region and thus the entire area of E. Macedonia ended up with 14 production regions, 15 consumption centers and 8 potential plants. The optimum solution of this model is also the same as that of the basic model with regard to the number, size and location of slaughtering plants. In particular, the optimum solution of this model is comprised of two plants, located in Serres and Kavala and slaughtering livestock equivalent to 15,500 and 7,872 tons of carcass meat respectively. This suggests that the optimum size of the Serres plant is 15,500 tons of carcass meat, and the optimum size of the Kavala plant is equal to 8,000 tons. The minimum total cost of the optimal solution of this model is 50.45 million drachmae. This is less than that of the basic model by only 930 thousand drachmae. This relatively smaller cost is entirely due to the simplification of both livestock assembly and meat distribution cost in those regions which joined each other. That is, when one region joined another, then both total livestock assembly and meat distribution costs between the joined regions became zero. Under the transhipment model employed in this analysis, the intra- regional transportation cost (both livestock assembly and meat dis- tribution) is equalized to zero because a basic assumption of this model is that both livestock supply and meat consumption are concen- trated in one point of each region. The optimum flow of live animals from the supply regions to the slaughter plants of Serres and Kavala (Table V-S) slightly differs 109 mmao;gmpsmam4m NN0.0N NNO.N OO0.04 scam »n mang -O4smuzo puma 4m4o4 OO0.0 OO0.0 :ogumsoga .44 OON OON :o4ummcmgmm .O4 4OO 4OO 4cmmuommoga .N4 OOO.4 OOO. 5:2O .44 .OO4.4 OO4.4 coumeO .O4 NNO.4 NNO.4 m44oazowmxggo .O 4NO 4NO m4m>ax .O O4O O4O m44oO=ogmcu4m4O .N ONO 404 NOO mo>44ovom .O O4N.4 O4N.4 4cgo4N Omz .O 440.4 440.4 mp4gm4z .4 4ON.O 4ON.O mmggmm .O OO4.0 OO4.0 m44xmgH .N NNN NNN cogummxog4u4m .4 co4mms sumw mp4 . co» . 4csu4N m.44 :4 :o4aaszm OEOLO . nonzom m4m>m¥ mu4gm4z muggmm . -cou acme 4muo4 OxOO -mNgOO mmz xmgm mco4mmm x4OO=O mpcm4mlmcwgwnmmsm4m .H4H 4muoe “Numme mmmogmu me One» u4gpms =4 umgsmmme maa4o>v NNO4 .mummgO .m4coumumz .O .mmmsocgmpgmam4m mg» on mco4OmL co4pu=uoga ms“ EOL4 m4ms4cm o>44 4o 3o4m .Ou> w4nO4 110 from that of the basic model (Table V-l), taking of course, into con- sideration the aggregation of livestock supplies in the integrated regions. More specifically, Mavrothalassa, which appears now jointly along with Nigrita, processes all its livestock supplies in the plant of Serres. Also, the region of Prossotsani joined that of Kato Nevrokopi processes its total livestock supplies in the plant of Drama, while in the basic model Prossotsani's livestock supplies were processed in the plant of Serres. In addition, Rodolivos' supplies are processed partly in the plant of Serres and partly in the plant of Kavala, while under the basic model all its supplies were pro- cessed in the plant of Serres. The optimum flow of carcass meat from the slaughterhouses of Serres and Kavala to the consumption centers (Table V-6) shows exactly the same pattern as that of the Model II (Table V-4), taking into account, of course, the fact that some regions appear jointly under this model. All the deviations of the optimal solution of this model from that of the basic model, seem that can be attributed to the changed volumes of livestock supplies and meat consumption of the jointly appearing regions and to the changed livestock assembly and meat distribution costs between them and the plants. The usefulness of this model is that it gives an indication of the bias generated in estimating the aggregate cost due to ignor- ing the intra-regional transportation cost. The larger a supply or consumption region, the greater is the downward bias in estimating intra-regional transportation cost. Thus the total costs of assembly, 111 mmaosgmucO=O4m NN0.0N NNO.N OO0.04 gunm =4 Oc4mmmuoga xuopmm>44 4muo4 N44.N OOO OO0.0 mcmzu< .O4 OOO.4 OOO.4 4x4co4cmmmgh .44 NO NO :o4ummcmgma .O4 OOO OOO 4cmmuommoga .N4 N4N.4 N4N.4 usage .44 NOO NOO coumxoO .O4 OON OON m44oaaowmxggu .O ONO.N ONO.N O4O>O¥ .O NNO NNO m44onaogmsu4m4m .N N4O N4O mo>44ouoa .O ONO ONO 4:;u4N 82 .O O44.4 O44.4 mp4gO4z .4 440.N 440.N mmcgmm .O OON.4 OON.4 O44xmgH .N ONO ONO :ogpmmxog4n4m .4 :o4Omg OOO O44 zumm ma O4ansm OEOLO -oaaom 4cgu4N ~44 xuoumm>44 4OOO4 -meO -OOLOO O4O>O¥ mmz mmggmm -xmLH mcmucmo :o4unssmcou TOMOO4O oc4gmpsmmu4m .HHH 44405 .mgmucmu :o4uOE:mcou msu o» mwmzoggmusO=m4m ms“ Eogm mcou u4gpms :4 paws mmmugmu 4o zo4O ”NNO4 .mummgo .O4coumumz .O .Ou> m4nO4 112 processing and distribution incurred are underestimated. To cope with this problem one of the following two things can happen: (a) either to construct the mathematical model in such a way as to include the intraeregional transportation cost; or (b) to designate regions as small as possible so that the bias generated is small. The Alternative Solution Model IV This model differs from the basic one by only the volumes of the regional livestock supplies, including the assumption of no meat imports. Projected livestock slaughterings instead of those ac- tually taken place in 1972 are considered in this model. These pro- jections basically refer to the year 1977. However, because of their optimistic view--acknowledged even by the experts themselves who did the projections--they are considered applying to the year 1980. The optimum number of plants was again two. However, both the optimum plant locations and optimum plant sizes appeared to be somewhat different from those of the basic model. Concretely, the optimum plant location was achieved for the plants of Serres and Drama by yielding a total cost for livestock assembly and processing and meat distribution equal to 68.07 million drachmae. Two other com- binations of two-plant sets were also considered. Both of them came out with higher total cost than that of the optimal sblution. These sets were Serres - Kavala on the one hand, yielding a total cost of 68.65 million drachmae and Serres - Doxaton on the other hand, yield- ing a total cost of 68.67 million drachmae. 113 The optimum plant sizes were determined to be 15,500 tons of carcass meat for both plants. This is so, because the optimal sol- ution of this model determines that the optimum volume of livestock to be processed by the plant of Serres is equivalent to 15,500 tons of carcass meat, and by that of Drama is 15,080 tons. The latter corresponds to 97.3 percent of the full capacity of a plant size equivalent to 15,500 tons. Neither the size nor the location of the first plant, Serres, of this model is different from that of the basic model. What is different is the optimum size and location of the second plant, which is Drama in this model, while Kavala was in the basic model. The difference in the optimum plant location between this model and the basic model might be explained by the projected compar- atively greater increases of the livestock supplies in the province of Drama than in the province of Kavala. The optimum size of the second plant, Drama, of this model is 15,500 tons of carcass meat, as contrasted to 8,000 tons of that (Kavala) of the basic model. The enlargement in the size of the sec- ond optimum plant (Drama) of this model as compared to the size of the corresponding plant (Kavala) of the basic model became necessary to process the higher volume of livestock supplies assumed in this model. The optimum flow of live animals from the supply regions to the slaughtering plants of Serres and Drama is given in Table V-7. As this table shows, the plant of Serres is supplied by all the re- gions of the province of Serres, except the regions of Mavrothalassa, 114 mmzosgmngOe4m OO0.00 OO0.04 OO0.04 :uem c4 Ocpmmmu -on xuopmm>44 4epo4 O zosuesoga .ON OOO OOO =o4pmmceeem .O4 O44 O44 eogmcoepupm .O4 ONO ONO 4Ooxoe>mz apex .N4 NOO NOO peempommoem .O4 OOO.N OOO.N eseLO .O4 400.4 400.4 capexOO .44 NOO NOO moEepoapO .O4 OO4.N OO4.N m44oazommxggu .N4 ONO.4 ONO.4 e4e>ex .44 OOO OOO m44oaaoemgpmm4e .O4 NO4 NO4 copeosuoeoa .O OON.4 OON.4 mo>44ovom .O OOO.4 OOO ONO.4 4:;u4N emz .N OOO OOO emme4ecpoe>ez .O NNo.N NNO. eppgmpz .O OO0.0 OO0.0 mmggmm .4 OO0.0 OO0.0 e44xeLH .O 404.4 404.4 cogpmexogpcpm .N OOO.4 OOO.4 m44oaouom .4 :o40mL seem an O44 m44on cogp >4OO=m xUOpm eEegO cap -oaaom e4e>e¥ -Ooemzp 4OOU4N ep mmgewm e44 -mexog mcopOmO O4OO=O -msp4 4euop -exoa -mNggo -empm emz -pampz -xeeH -pepm mpOe4OTOO4mescse4O .mummLO .epcoumoez .O .mmmzosgmngze4m esp Op mco40mg coppusuoea mgp seem m4eepce m>44 4o zo4O .cowuapom EaEquOII>H POUOE "AHMOE mmmugmo $0 mcou 074ng 4:. Dmgzmme OE:_.O>V .mep .Nu> m4ne4 115 Rodolivos and Nea Zichni. The latter supplies it partially. All the other regions supply the plant of Drama. The optimum flow of carcass meat from the slaughterhouses of Serres and Drama to the consumption centers is given in Table V-8. As the table indicates all the regions of the province of Serres, plus Podochorion of the province of Kavala, Thessaloniki and to a large extent Athens are supplied with carcass meat by the plant of Serres. All the other consumption centers are supplied by the plant of Drama. Figure V-2 presents graphically the optimum flows of both livestock and carcass meat for the optimal solution of this model. Because the recommendations regarding the number, size and location of slaughter plants in the area of E. Macedonia will be based upon the projected livestock supplies, for this reason a second best solution of this model will be presented as well, in both tables and graph. This was done in order that alternative solutions could be available and thus some flexibility be provided in the decision-making process. As a second best solution in this model is considered the optimum solution for three plants. This was achieved for the plants of Serres, Kavala and Drama yielding a total cost of 69.74 million drachmae. Three additional combinations of three plant sites were also examined, all of which generated a higher total cost than that of Serres, Kavala and Drama. These are: a. Serres, Chryssoupolis and Drama, yielding a total cost of 70.48 million drachmae; 116 O mmnassmp OO0.00 OO0.04 OO0.04 unpae4m spew an mpcme -3588 p8... :38 ON4.04 O40.0 OOO.4 mcmzp< .4N OON.O OON.O 4x4ca4emmwg4 .ON 4O 4O :a4pmocegem .O4 OO_ OO cagmcag4vpm .O4 ONN ONN 4Oaxae>az apex .N4 .N44 N44 paempammasm .O4 .N4N.4 N4N.4 eaeeO .O4 NOO NOO capmmaO .44 4O 4O masep 4O .O4 44O 44O m44aO=ammxu=O .N4 ONO.N ONO.N e4e>ex .44 ONN ONN m44aqzaemspmm4u .O4 OO4 OO4 ca4gagaaOaO .O N4O N4O ma>44aeam .O ONO ONO 4c5u4~ emz .N OOO OOO emme4egpae>ez .O O4O O4O eppeOpz .O 440.N 440.N mmeemm .4 4OO 4OO e44xee~ .O ONO ONO :aepmexaepupm .N ONO ONO m44aaavam .4 ca40mg seem c4 O44 m44an caep cappqszmcaa eseLO -mmwO -aasam e4e>ex usaemsp 4JMM%N upwwpz mmgemm -“WWH -mexae mempcmu cawpassmcau p8... 4304 -925 .3: . . 36.5 Opce4O OcpgmngOe4O .ca4pa4am cseppaanu>H 4muae .O .meopcma :a4pgssmcaa esp ap mmmzacemng=e4m esp Eaew mcap a4epme :4 pews mmeueea 4a za4m ”OOO4 .mammLO .e4canmaez .Ou> m4ne4 117 .HOHpaaom adaHpOOII>H Hove: "mumpnmu nappgasmaou map ou momsonumpnwnmam map Baum pmma ammoumo mo 30am cam momsonuopsmamam map 0p macawou haamsm map Baum mamaaam o>HH mo 30am "NI> munwwm [V muonp< i mafiuonuavom _ . .mmwamnpou>mm\\V fixpaoamwmmna V mu .4sz «deem a. \ a \\ A‘\A“uummwumwwvvv . «HaxmuH ~ . o O .aHpmmamum ._ ’ ’ paoxauefiz apex coumaaumva .mp .Oaammmun :aupmmxoumv mwaomavam 11, canumaoum O 118 b. Serres, Kavala and Doxaton, yielding a total cost of 70.91 million drachmae; and c. Iraklia, Chryssoupolis and Doxaton, yielding a total cost of 73.40 million drachmae. The optimum plant sizes are 15,500 tons of carcass meat annually for the plant of Serres and 8,000 tons for both plants of Kavala and Drama, since the estimated optimum volumes of livestock to be processed by the corresponding plants are equivalent to 15,500, 8,000 and 7,080 tons of carcass meat respectively. As it is seen above, for the first two plants, the optimum volumes processed by them coincide exactly with the recommended plant sizes. For the third plant, Kavala, the optimum processed volume of 7,080 tons while approximates the most the model plant size of 7,000 tons than that of 8,000 tons, the latter was selected to represent Kavala's plant size, since no plant was assumed to operate above its full capacity. The optimum flow of live animals (expressed in terms of meat equivalents) from the supply regions to the slaughtering plants of Serres, Kavala and Drama is given in Table V-9. As this table shows, all the regions of the province of Serres supply with live- stock the plant of Serres, except the region of Mavrothalassa which supplies the plant of Kavala, Nea Zichni whose supplies are split be- tween the plants of Serres and Drama, and Rodolivos which also sup- plies the plant of Drama. The plant of Kavala is supplied by all the regions of the province of Kavala plus Mavrothalassa (totally) of Serres and Doxaton (partially) of Drama. The plant of Drama is sup- plied by all the livestock supplies of all the regions of the province 119 mmaangmpsOse4m OOO.om ooo.e OOO.N OO0.04 some cw Ocpmmmu raga xuapmm>44 4epa4 O cagaeEan .ON OOO OOO cawpmmceeem .O4 O44 O44 caemcaLOOOO .O4 ONO ONO pqoxoe>mz apex .N4 NOO NOO Ocempammagm .O4 OOO.N OOO.N eseeO .O4 400.4 OON.4 4NO maumwMMMO .M” NOO NOO .O . OO4.N OO4.N O44aO=aOOOgOO .N4 ONO.4 ONO.4 e4e>ex .44 OOO OOO Oppogaoeegpeo4m .op NO4 NO4 caOLaguaOaO .O OON.4 OON.4 Oa>44avam .O OOO.4 OOO ONO.4 OcsapN emz .N OOO OOO eOOe4ecpae>ez .O NNo.N NNo.N eppempz .O OO0.0 OO0.0 Omegwm .4 OO0.0 OO0.0 eO4xeLH .O 404.4 404.4 cagpmexaLOOOO .N OOO.4 OOO.4 O44aqauam .4 caOOmg :aem On O44 m44ag caep O4OOOO xaapm eseeO cap -aOOaO e4e>ex -Oagmsp OasawN ep Omgemm eO4 -Oexae OcaOOmm O4OO=O -ean emz -FLOpz -xee4 -m>O4 4epa4 -OOLOO -Om4O . . -OOOO Opce4O Omweapamse4m .ca4p24am pmmn Ozaummuu>4 4muas .mameO .eOOaOmuez .O .OmmaagempsOOe4O mgp ap OcaOOmL :aOpaOanO esp Eagm O4e54ce m>44 4a 3a4O ”Apems Omeaeea 4a Ocap a4epms Op vegameme usa4a>v OOO4 .On> m4ne4 120 of Drama except that of Doxaton (partially), plus the aforementioned regions of Rodolivos and Nea Zichni belonging in the province of Serres. The optimum flow of meat distribution from the plants of Serres, Kavala and Drama to the consumption centers is given in Table V-lO. As this table shows, all the regions of the province of Serres are supplied by the plant of Serres. Similarly, the plant of Kavala) supplies only regions of the province of Kavala, and the plant of Drama supplies only regions of the province of Drama. Thessaloniki is supplied by the plant of Serres, and Athens is supplied by all three plants. Figure V-3 presents graphically the optimum flows of both livestock and carcass meat from the regions to the slaughterhouses and vice versa. The meaning of the arrows has already been explained in the second section of this chapter. The Alternative Solution Model V This model differs from the previous one by only the degree of plant capacity utilization. That is, in this model plants are as- sumed to be operating at 90 percent of their capacity instead of 100 percent as was assumed in all the previous models. Because of this assumption, at least three plants would be required to process the projected volume of livestock equivalent to 30,580 tons of carcass meat. The optimum number of plants is three. The optimum location of plants is the same as that of the second best solution of Model IV, i.e., Serres, Kavala and Drama. The optimum sizes of these plants are 121 wmzagemng=e4m OO0.00 OO0.0 OOO.N OO0.04 suem On mpcms uapgmpsa pews 4epa4 ON4.04 OOO.4 ON4.0 400.0 Ocmsp< .4N OON.O OON.O _ 4x4ca4emmmg4 .ON 4O 4O :aOpOmcegeO .O4 OO OO caemcaLOOOO .O4 ONN ONN 4Oaxag>mz apex .N4 N44 N44 4=empammaem .O4 N4N.4 N4N.4 eEeLO .O4 NOO NOO capean .44 4O a 8238.5 .2 44O 44O O44aOOaOOOe5O .N4 ONO.N ONO.N e4e>ex .44 ONN ONN O44aazaemgp4m4u .O4 OO4 OO4 _ caOLaOUaOaO .O N4O N4O Oa>44auam .O ONO ONO 4:;a4N emz .N OOO OOO emme4egpag>ez .O O4O O4O epOLOOz .O 440.N 440.N Omeemm .4 4OO 4OO e44xeLH .O ONO ONO caepmexag4u4m .N ONO ONO O44aaavam .4 :a4OmL seem :4 O44 m4 aO :agp :a4passmcoa eEeLO immWO -aazam e4e>ex usawwgp 4JmufiN u4mw4z mmggmm -MWWH -Oexag mgmpcmo caOpOEOOOaO pewE 4epa4 -mxgsu -4m4m . . -4000 mp=e4O OOOLepOOOe4O .ca4p34am pmmn Ocaammuu>4 4muas "OOO4 .muemgO .e4canmaez .O .Oempcmu :a4pnszmcau esp ap OmmzagempsO=e4O esp Eagm Ocap oOeme c4 pews mmeageu 4a 3a4m .O4-> m4ne4 122 .GOOpaaow pawn Odouom|I>H Hove: "muopuou defipnaamcou esp ou mmmnonuopswsmam wnu Baum umoa memoumu we Beam Ode amazonumuzwsmam onu 0p maoammu hammsm map Scum mamafiam m>HH mo 30am "OI> wuswfim m=m£u¢ woamOomfla cosomaoum O :o weapmv O, .‘ o .. . flammpommoum nouummxouwuw mHHo.owom .OOumwcmumm H Haoxou>mz apex 123 15,500 tons of carcass meat for Serres and 9,500 tons for both Kavala and Drama. This is so, because the optimum volumes processed by these plants were 13,950 tons for Serres (which is exactly equal to 90 per- cent of 15,500 tons), 8,550 tons for Drama (which is exactly equal to 90 percent of 9,500 tons) and 8,080 tons for Kavala (which is equal to 85 percent of 9,500 tons). The minimum aggregate cost of the optimal solution of this model amounts to 71.62 million drachmae. This is greater than that of the second best solution of the previous model by 1.68 million drachmae. This difference can be largely attributed to the smaller (90 percent) capacity utilization of the plants--and therefore greater unit processing cost-~assumed in this model. Table V-ll gives the optimum flow of live animals (expressed as always in this analysis in terms of carcass meat equivalents) from the livestock supply regions to the slaughter plants of Serres, Kavala and Drama. As this table shows, out of all the regions of the prov- ince of Serres, Sidirokastron, Iraklia, Serres and Nigrita totally and Rodopolis by about two-thirds supply the plant of Serres. The plant of Kavala is supplied by all the regions of the province of Kavala plus Mavrothalassa of Serres, and Doxaton of Drama by 86 percent. The plant of Drama is supplied by all the regions of the province of Drama except that of Doxaton which sends only 14 percent of its total live- stock supplies, and the remained regions (Nea Zichni and Rodolivos totally, and Rodopolis partially) of the province of Serres. Table V-12 gives the optimum flow of carcass meat from the slaughter plants of Serres, Kavala and Drama to the consumption centers. 124 mmaagemsz=e4M . . aem =4 OOOOOmaae OO0.00 OO0.0 OOO O OOO O4 ; xaapmm>44 4epa4 casueeaga .ON O cappmmcegem .O4 OOO OOO caeecaLOOOO .O4 O44 O44 paaxae>wz apex .N4 ONO ONO 4cempammaem .O4 NOO NOO eEeeO .O4 OOO.N OOO.N . :apean .44 4OOJ4 OON 4NO 4 masepaO4O .O4 NOO. NOO. O44aazammaggu .N4 OO4.N OOW.4 e4e>ex .44 ONO 4 ON m44aazagmzp4m4m .O4 OOO OOO capgaguaOaO .O NO4 . NO4 Oa>44auam .O OON.4 OON.4 pagaON eaz .N OOO.4 OOO 4 emme4espae>ez .O OOO. OOO NNO. ep4gO4z .O NNO N OO0.0 Omegmm .4 OOOHO OO0.0 e44xeeH .O OOO O 404.4 caepmexaLOOOO .N 4O4.4 OOO O44aganam .4 OOO.4 OO4 caOOOL cagp saem NO O44 O44aa 4: UON ep e44 . Om O OO: O4OOOO xaapm eEeeO immWO -aasam e4e>ex -zaemsp “w: -OLOOZ Omegmm -xeLH wwwaw. Oca4 a 4 O na>44 4epa4 -Oagzu -4m4O . . 11 mpce4O OO4meOOOe4O .> 4muas "Npems Omeueeu 4a Ocap owgpae c4 Oeesmems me=4a>v OOO4 .mammgO .e4caumuez .O .momzaggmpnOze4m esp ap Oca4OmL caOpUOOaLO asp Eagm O4es4ce m>44 4a 3a4m .44-> m4ne4 125 mmsaggmng=e4O OO0.00 OO0.0 OO0.0 OO0.04 caem OO pews napsmpaa peas 4epa4 ON4.04 ON0.0 400.0 440.0 Ocegp< .4N OON.O OON.O Ocha4emmmg4 .ON 4O 4O caOpOmceeeO .O4 OO OO OaxacaLOOOO .O4 ONN ONN 4Oaxag>mz apex .N4 N44 N44 Ocempammaga .O4 N4N. 4 N4N. 4 23.5 .O4 NOO NOO capean .44 4O a 85383 .O4 44O 44O OO4aO=aOOOLOO .N4 ONO.N ONO.N .e4e>ex .44 ONN ONN OO4aO=agmzp4e4O .O4 OO4 OO4 =a4eazaavam .O N4O N4O ma>44auam .O ONO ONO 4cza4N emz .N OOO OOO eOOe4espae>ez .O O4O O4O ep4gOOz .O 440.N 440.N meegmm .4 4OO 4OO eO4xeeH .O ONO ONO caspmexas4000 .N ONO ONO OO4aOaOaO .4 :a40me uem :O OO4 O44aa caep canOEOOOau eEeLO -MMWO -anaam e4e>ex -saemgp OJMMMN -4ww4z Oegemm -NWWH -Oexag Oempcmu caOpOEOOOaO peeE 4epa4 -OOLOO -404O . . -404O OpOO4O.OOHump;a:e4O . .> 4muas ”OOO4 .mammsO .ewcaumaez .O .Ogmpcma caOpOEOOOaa esp ap mmmzagemszse4m esp Eagm Ocap u4epms cw pews Omeugea Oa za4O .N4u> m4ne4 126 As the table indicates, each plant supplies with carcass meat only the regions of the province in which each one belongs. Thessaloniki is supplied by the plant of Serres, while Athens is supplied by all the plants but in different quantities. The Alternative Solution Model VI This model assumes a standard unit processing cost regard- less of the size of plant and regardless of the volume processed in each plant. This is exactly what is happening today in Greece with the livestock slaughtering as has already been described in Chapter II. In other words, no economies of size in livestock processing are assumed in this model as contrasted to all the other models. The unit livestock slaughtering cost is taken as equal to 1,050 drachmae per ton of carcass meat equivalent. This cost reflects both slaughtering fees (averaged at 50 drachmae per head of cattle or 250 drachmae per ton of carcass meat, since one head of cattle yields on the average 200 kilograms of carcass meat) and slaughterers' pay- ment (averaged at 160 drachmae per cattle or 800 drachmae per ton of carcass meat equivalent).30 Since economies of size are not assumed in this model, the result is that the optimum solution is solely affected by the assembly and distribution cost. Because the sum of the total livestock assem— bly and meat distribution cost becomes smaller and smaller as the num- ber of processing plants becomes larger and larger, the optimum 30About these data see the text in pages 31-32 and 82. 127 solution of this model was achieved when all the potential (ten) plants were included in the livestock slaughtering industry of the study area. This optimum solution generated an aggregate minimum cost of livestock assembly, processing and meat distribution equal to 41.60 million drachmae. However, when the number of plants was re- duced to nine, the total cost was increased to 41.98 million drachmae, and when the number of plants was reduced to six, the total cost was increased to 44.92 million drachmae. These figures clearly indicate that under no economies of size in plant processing, the optimum (min- imum cost) solution has a general tendency to build as many plants as there are supply regions. The optimum flow of live animals from the supply regions to the slaughterhouses is given in Table V-13. The optimum flow of car- cass meat from the slaughterhouses to the consumption centers is given in Table V-l4. It is obvious from these tables that both livestock assembly pattern and meat distribution pattern are entirely determined by the corresponding transportation cost pattern, i.e., the structure of livestock assembly plus meat distribution cost. This can be read— ily seen from a quick comparison of these Tables V-l3 and 14 with Tables V-15 and 16. The latter ones give the optimum flow of live animals from the supply regions to the slaughterhouses (Table V-15) and the optimum flow of carcass meat from the slaughterhouses to the consumption centers (Table V-16). Both these Tables (V-15 and 16) have been computed on the basis of only livestock assembly and meat distribution cost, and as it is seen have almost the same pattern as Tables V-l3 and 14. Slight differences exist only in the plants of Sidirokastron and Iraklia. 128 mmsasempsO=e4m NN0.0N NOO.N OO4.4 OON.4 400.N ONN 4NO.N 440.4 4ON.O 4ON.N 4N4.0 gaem :4 Oc4mmmaapa xaapOm>44 4epa4 OO0.0 OO0.0 cagaesaem .ON 404 404 =a4pmmcegea .O4 OO OO caemcanO4O .O4 OOO OOO 4Oaxag>mz apex .N4 OO4 OO4 4cempammaem .O4 OOO.4 OOO.4 eseeO .O4 OO4.4 OO4.4 capean .44 44N 44N OasepaOOO .O4 OON.4 OON.4 OO4aOOaOOOLOO .N4 4NO 4NO - e4e>ex .44 OOO OOO OO4aO=aemsp4m4O .O4 OON 44N OO (ca4gagaaOaO .O ONO ONO ma>44auam .O O4N.4 O4N.4 OOOUON emz .N NOO NOO emOe4espae>ez .O NOO.4 NOO.4 ep4eO4z .O 4ON.O 4ON.O mmgemm .4 400.N 400.N e44xeLH .O NNN NNN cagpmexaL4O4O .N OOO OO4 OON O44aaauam .4 ca4OwL gaem On O44 O44aa cagp O4OOOO xuapm eseg -mmw -aazam e4e>ex -Oagmsp 4me4N upmw4 Omgemm new“ -Oexag O=a4OmO O4OOOO -2: :38 O a -925 .85 z . .z c. O -25. mpce4O OO4gmpsze4O .H> 4muas "4pees mmeaeea 4a Ocap awepme OOTOOLOOems was4a>v NNO4 .mammLO .e4caumuez .O .meaaggmngOe4m esp ap OcaOOme :a4pazuaga mzp Eaem O4es4ce m>44 4a 3a4O .O4n> m4ne4 129 mmsanempnOOe4m NN0.0N NOO.N OO4.4 OON.4 400.N ONN 4NO.N 440.4 4ON.O 4ON.N 4N4.0 nuem On peas na4nmp=a peas 4epa4 N44.N NON OO OOO.4 404.4 OOO NOO.4 mcwnp< .4N OOO.4 OOOO4 4xpca4emmen4 .ON 4O 4O cappmmceeea .O4 OO OO :aemcae4000 .O4 ONN ONN 4Oaxae>mz apex .N4 N44 404 ONN 4cempammagm .O4 N4N.4 N4N.4 _ esegO .O4 NOO NOO capean .44 4O 4O OasepaO4O .O4 44O 44O m44aa=ammOenO .N4 ONO.N OON 400.N e4e>ex .44 ONN ONN m44oa=oem5pmm4m .op OO4 OO4 :a4eanuavam .O N4O N4O Oa>44auam .O ONO ONO 4=na4N emz .N OOO OOO emme4enpag>ez .O O4O O4O ep4eO4z .O 440.N 440.N Omggmm .4 4OO 4OO e44xegH .O ONO ONO cagpmexaLOOOO .N ONO ONO m44oaoeo¢ .F capOmL naem :4 O44 O44aa casp caOpOEOOcaa cap . . - . 4cnu4N ep e44 - pews 4epa .4 28.5 -ean -mmfimw e4e>ex any.“ emz 4.5.42 8.23 3.9: New“; 23:8 539.528 mpce4O chgmpzmze4m .H> 4muas "NNO4 .mammnO .e4caumuez .O .mgmpcmu =a4pae=mcaa mnp op OmmaangmpnOze4m enp sag» Ocap u4gpme c4.pems Omeugea 4a 3a4O .44-> m4ne4 130 mmzanempnO=e4m NN0.0N NOO.N OO4.4 OON.4 400.N ONN 4NO.N 440.4 4ON.O 400.N 4ON.O nuem c4 chmmmaaea xaapme>44 4epa4 OO0.0 OO0.0 canoesaem .ON 404 404 ca4pmm=eeem .O4 OO OO caemcaLOOOO .O4 OOO OOO paaxae>mz apex .N4 OO4 OO4 Oeempammaea .O4 OOO.4 OOO.4 eEenO .O4 OO4.4 OO4.4 capean .44 44N EN 85385 .O4 OON.4 OON.4 O44aOOaOOOLnO .N4 4NO 4NO e4e>ex .44 OOO OOO OO4aO=aemnp4m4O .O4 OON 44N OO ca4eanuaOaO .O ONO ONO Oa>44auam .O O4N.4 O4N.4 4cna4N emz .N NOO NOO emme4enpag>ez .O NOO.4 NOO.4 ep4nO4z .O 4ON.O 4ON.O mmggmm .4 400.N 400.N e44xeLH .O NNN NNN caepmexag4u4m .N OOO OOO O44aaauam .4 :a40en nuem On O44 m44aO cagp O4OOOO xaapm eEeeO -mmw -aazam e4e>ex usagmnp 4me4N 4mw4 Omgemm ewh -mexag Oca40mm O4OOOO -2: 43.8 a -35 at: z -. .z s. H 35.5 wuce4miuc4gmpncae4wi .mammLO .ewcaumaez .O .OmmzanempnOOe4m mnp ap O=a4Omn :a4pasvaeq mnp EaLO O4ee4=e m>44 4a 3a4m .mpmaa :a4pan -prO4O.O:e O4nsmmme O4=a can: OmOen .Apems Omeaeea 4a Ocap u4epms :4 Ooezmeos mE=4a>v NNO4 .O4u> m4ne4 131 mmzanempnO=e4O NN0.0N NOO.N OO4.4 OON.4 400.N ONN 4NO.N 440.4 4ON.O 400.N 4ON.O nuem On p5...O -OOnOpaa pews 4epa4 N44.N O44 OOO.4 OOO 404.4 N44 OON.4 Ocmnp< .4N OOO.4 O40. OOO.4 4x4ea4emmmn4 .ON 4O 4O OaOpOmceeeO .O4 OO OO caemOaL4O4O .O4 ONN ONN OOaxae>mz apex .N4 N44 NON O4N Ocempammaea .O4 N4N.4 N4N.4 eseeO .O4 NOO NOO capean .44 4O 4O mosepOOOO .OF 44O 44O O44aO=aOOOgnO .N4 ONO.N OON 400.N e4e>ex .44 ONN ONN m44aOanmnp4m4O .O4 OO4 OO4 :a4nanuaOaO .O N4O N4O OO>O4OOOO .O ONO ONO 4cna4N emz .N OOO OOO emme4enpag>ez .O O4O O4O OOOOOOZ .O 440.N 440.N Omegmm .4 4OO 4OO e44xenH .O ONO ONO canpmeanOO4O .N ONO ONO OO4OOOOOO .4 :aOOmL naem =4 OO4 O44aO :aep caOpOEOOOaa eseeO -umw -aOOam e4e>ex -anmnp 4meON 4mw4 OOLLOO new“ -Oexag Ogapcmu cappOEOOOOO pews 4OOO4 O -OOOOO -OO4O z -. .z x O -OOOO mpce4O OOOmenmae4O .mpmau :a4pznOLpO4u One O4nsmmme O4=a OaO: Ommen .NNO4 .mammeo .eOOaOmuez .O .Ogepcmu :a4pOEOOcau mnp ap Ommaanempnan4m mnp Eaew Ocap u4gpms OP pews Omeagea 4a 3a4O .O4-> m4ne4 132 The fact that the optimum solution of this model VI gener- ates an aggregate cost of 41.60 million drachmae as contrasted to 51.38 million drachmae of the optimum solution of the basic model which has the same characteristics as this model VI except that the basic model assumes a modern technology in livestock slaughtering, it does not mean that this model VI is better than the basic model for the following main reasons: a. b. The unit processing cost in model VI does not include all the costs (fixed and variable) involved in livestock processing, as is the case with all the other models. Actually, this cost does not reflect any specific fixed or operational costs, ex- cept the labor cost (i.e., the slaughterers' payment) incurred. "Charges for the use of slaughterhouses are not necessarily "31 related to actual operating costs, as the FAO study points out. Model VI assumes continuation of the old slaughtering system; it does not assume the use of modern technology in livestock assembly as all the other models assume, and which technology, by the experts' opinions, affects the quality of meat. However, this model has some importance from both an analyt- ical and practical point of view. Analytically, it shows that when no economies of size are assumed in processing, the general tendency is to locate as many processing plants in an area as there are supply 3'Ibid., p. 32. 133 regions. Practically, it shows that if the current slaughtering sys- tem can continue in Greece without the occurrence of any special prob- lems (e.g., management problems and maintenance costs of slaughter- houses, meat quality deterioration, etc.), then it might be better for the country in general and E. Macedonia in particular to go on with the current system of livestock slaughtering in the existing slaugh- terhouses. However, the opinion of FAO and other experts is that the present slaughtering system needs a replacement. Based on this fact, the study was conducted. In other words, the current analysis starts from the following point: In case the present slaughtering system is to be replaced, what should be the optimum number, size and location of new slaughtering plants, so that the aggregate cost of livestock assembly, processing and meat distribution is minimized? The question of continuing with the current slaughtering system versus adopting a modern one is another problem which needs a detailed benefit-cost analysis. But this is beyond the scope of the present study. Summar Empirical results for six alternative solution models have been obtained in this chapter. Of these, four models (I, II, III and IV) refer to 1972 livestock supplies and the remained (IV and V) mod- els refer to projected livestock supplies. All models but VI assume economies of size in processing, and all models except V assume 100 percent plant capacity utilization in processing. Furthermore, all 134 models but II assume 50 percent truck capacity utilization in live- stock assembly, and all models but III assume 20 production regions, 21 consumption centers and 10 potential slaughter plants. The empirical findings for each of these six models have as follows: 1. The optimum solution for the basic model ended up with two plants, located in Serres and Kavala and having sizes equal to 15,500 and 8,000 tons of carcass meat. The minimum aggre- gate cost of livestock assembly and processing and meat dis— tribution amounts to 51.38 million drachmae. 2. Model II yielded the same optimum number, size and location of processing plants. Its minimum total cost is 45.87 mil- lion drachmae, i.e., by 5.51 million drachmae lower than that of the basic one. This can be totally attributed to the re- duced livestock assembly cost due to the full capacity utili- zation of trucks engaged in livestock assembly. While the optimum flow of live animals is exactly the same as that of the basic model, the optimum flow of carcass meat slightly differs from that of the basic model. 3. Model III gave also the same optimum number, size and loca- tion of slaughtering plants as that of the basic model. Its minimum total cost amounts to 50.45 million drachmae. This is less than that of the basic model by 930 thousand drachmae and is primarily due to ignoring more intra-regional transpor- tation cost as a result of enlarging some regions through joining sets of two smaller regions into one. The optimum 135 flow of live animals and carcass meat slightly differs from that of the basic model, as long as the aggregation factor is taken into consideration. Model IV--referring to projected livestock supplies--gave an optimal solution of two plants, having optimum sizes equal to 15,500 tons for both plants and optimum locations Serres and Drama. It yielded a minimum aggregate cost equal to 68.07 million drachmae. As second best solution for this model was considered the minimum cost solution for three plants. The optimum plant location for this solution is Serres, Kavala and Drama, and optimum sizes are 15,500 tons for Serres and 8,000 tons for both Kavala and Drama. Its minimum cost amounts to 69.94 million drachmae. Model V--assuming 90 percent plant capacity utilization-- ended up with three plants as an optimum number, located in Serres, Kavala and Drama and having optimum size, equal to 15,500 tons of carcass meat for Serres and 9,500 tons for both Kavala and Drama. Its minimum total cost is 71.62 mil- lion drachmae. Model VI does not assume any economies of size in processing and reflects the current livestock slaughtering system in Greece. Its optimal solution basically ends up with each region's production processed locally unless there is no plant in that region. In such a case, that region's live- stock supplies are shipped for processing in the nearest plant. CHAPTER VI SUMMARY, IMPLICATIONS, LIMITATIONS AND NEEDED RESEARCH Summar Greece is a developing country. To achieve rapid economic development, it needs to rationally utilize its limited resources. In this way the maximum total output will be produced from a given amount of resources. On the other hand, imports of goods and ser- vices must also be reduced as much as possible--without deteriorating the welfare of people--to prevent the corresponding outflow of for- eign exchange. The savings of foreign exchange achieved in this way can be invested in other productive economic activities. Either case (rational utilization of resources and reduction of imports) can sub- stantially contribute to the economic growth and development of the country. Under this spirit, Greece has started developing its live- stock and meat industry these last few years in an effort to match livestock production with the notably increased total meat consump- tion. Large amounts of loans with low interest rates along with higher output prices and input subsidies were offered to livestock producers to accomplish a relatively faster development of the indus- try. The incentives given seem to have been working and the industry started to grow up significantly. 136 137 The anticipated substantial expansion of the livestock pro- duction by the end of the 1970's, coupled with the fact that the existing livestock slaughterhouses are becoming more and more obso- lete in buildings, machinery, equipment and technology, sooner or later may necessitate the establishment of new slaughtering plants. However, before an action be undertaken in such a case, the following problems should first be investigated: 1. How many slaughtering plants should be built in total in order to slaughter the anticipated higher volume of live- stock production? 2. How large should the plants be, so that economies of size can be achieved and thus the costs of slaughtering be mini- mized? 3. Where should these slaughtering plants be located, so that the aggregate costs of (a) assembling the live animals from the production points to the plant locations, (b) slaughter- ing the live animals in the slaughterhouses, and (c) trans- porting the carcass meat from the plant locations to the con- sumption centers be minimized? To answer these questions the present analysis was conducted. The analytical tool was the transhipment model, a special kind of a transportation linear programming model. The main characteristic of this model is that it takes simultaneously into consideration the as- sembly, processing and distribution costs. The matrix format was originally constructed by professor Stephen Harsh of Michigan State University and modified by the author. 138 The size of the matrix used in this analysis is 81 rows by 420 col- umns. Because of its large size, the APEX-I computer program was utilized in the analysis. The computer analysis was done in the com- puter center of Michigan State University. The data needed for this computer program are: 1. Regional livestock supplies available for slaughtering; 2. Regional meat consumption; 3. Livestock assembly cost per unit of product between all the livestock supply regions and all the potential plants; 4. Livestock processing unit cost for different plant sizes and levels of capacity utilization; 5. Meat distribution cost per unit of product between all the potential plants and all the meat consumption cen- ters. The procedures used for obtaining these basic data for the computer analysis are described in brief below. Regional livestock supplies were calculated on the basis of annual livestock slaughterings given for every community (village or town) of each province. These data were provided by the provincial offices of the Ministry of Agriculture. The livestock slaughterings (expressed in terms of carcass meat) were summed up for all the com- munities included in a specific region. Thus, the annual livestock supplies measured in tons of carcass meat were calculated for that region. Regional meat consumption was calculated on the basis of meat inflows and outflows taken place in the province in which the region in question belongs. In this way, the total meat consumption 139 in that province was estimated. To estimate the total meat consump- tion by the urban population of a province, the national per capita meat consumption was multiplied by that population. This amount was subtracted from the total provincial meat consumption and this repre- sents total meat consumption by the non-urban population of that prov- ince. Dividing this amount by the number of non-urban population of the province, per capita meat consumption of this population in that province was calculated. Finally, on the basis of a region's popula- tion structure and per capita meat consumption of urban and non-urban population, total meat consumption for each region was estimated. Livestock slaughtering cost was calculated on the basis of the FAO study conducted for Greece in 1966. The calculations were basically directed to making the appropriate adjustments needed to incorporate the inflation taken place in the country since then. Also some other adjustments were made to take into consideration the input price differentials existing among variods provinces. That is, the input prices of all plants used in this analysis were adjusted to those of E. Macedonia. Livestock assembly and meat distribution costs were ob- tained through questionnaires. The interviewed people were both truckers and butchers involved in either or both livestock assembly and/or meat distribution. Then, the cost averages were calculated for the same sizes of trucks and the same distances. These costs were used along with the matrix of distances between supply or con- sumption regions and slaughterhouses to construct the transportation cost matrices for both livestock assembly and meat distribution. 140 Both the unit transportation cost for the various distances and the unit processing cost for the different plant sizes comprise the "unit cost" row of the computer matrix formulated. This matrix, having a size of 81 rows by 420 columns, was used in the computer analysis of the problem in question. Six alternative solution models were constructed and tested in order to find out what might be the impact of changing the corres- ponding variable--characterizing each model-~upon the optimal solution of the basic model. The basic characteristics of these models are sum- marized in Table VI-l. As this table indicates, all the models except IV and V refer to 1972 livestock supplies. The latter models refer to 1980 projections of livestock supplies amounting to 30,580 tons of car- cass meat. Out of the six models, only model II refers to full capac- ity utilization of trucks engaged in livestock assembly. The remaining five models refer to 50 percent capacity utilization of those trucks. All models except III assume that the entire area of E. Macedonia is divided into 20 livestock supply regions and 21 meat consumption regions as well as 10 potential slaughtering plants. Only model V assumes 90 percent maximum plant capacity utilization and only model VI assumes standard processing cost, i.e., it does not assume economies of size and modern technology in livestock processing. The optimum solutions for these six models with regard to number, size, and location of slaughtering plants plus the minimum ag- gregate costs of livestock assembly, processing and meat distribution are summarized in Table VI-2. As this table shows, when slaughtering plants are allowed to be utilized at full capacity and economies of 141 x x x VA x x x x x” x x x x x x x x x x x x wesamme m4 Ocpmmmoagq :4 NONOO 4a OOOEacaoe acv pOaa p4=3 ugeucepm vessmme O4 OcOOOeaaLO xuapme>44 cw NONOO 4a Oepeacauev OOa4acnaep cgeuaz mpce4O 4eOpcmpaO O use OcaOOme caOpOEOOOau O4 .mca4Omg O4000O cempgzam mpce4O 4e4pcepaO O4 Oce mgepcea :aOpOEOOOau 4N .OOOOOOL O4OOOO Opce34 Opce4O O=4LepnOze4m 4a ca4pe~44wp= Op4anea 44am Opce4O Oc4eepnO=e4O ma cappeN444p= Op4oeaea pceoeeq Opmc4z :a4pOn4OpOOO pews :4 OeOeOce Oxoaep 4a ca4peN44Op= Opwoeqea 44am O4nemmme xoapme>44 :4 OmOeOce Oxuzep 4a :a4peNO44pO Op4aenea 44am O4nsemme xuapme>44 :4 eeOeOce Oxosep 4a :aOpeNO4Op: Op4aneu pceoeaa Op44O OOO4 ap OOO4OOOO xuapme>w4 Oepamnaea NNO4 :4 OO44OOOO xuapmm>44 ue~44eem .44 H> > >H HHH HO uwmeO m4muaz caOp4OaO m>4pecgmp4< Ou4pm4gepoeeenu 4euaz L7 .O4euae cawpa4am e>4pecemp4e Oza4ge> mnp 4a OquO4meueeeno mn4 .4-H> e4ne4 142 Table VI-2. Main research findings for each alternative solution model. Research Variables Basic Model Model II Model III 1. Optimum number of plants 2 2 2 2. Optimum location of plants Serres Kavala Serres Kavala Serres Kavah 3. Optimum volume of live- stock processed by each 7, meat 4. Suggested optimum plant sizes (in tons of car- 15,500 8,000 15,500 8,000 15,500 8,0I cass meat) 5. Minimum aggregate costs of livestock assembly and processing and meat distribution (in mil- lion drachmae) #j *See Table V-l3 or V-l4. 51.38 45.87 50.45 **Unit slaughtering charges do not include all the cost items, as all the other models do. They include only labor cost (slaugh- terers' payments) plus "slaughtering fees." Source: Tables V-l through V-l4. 143 Model IV Model V Model VI Optimum Solution 2nd Best Solution 2 3 3 10 Serres Drama Serres Drama Kavala Serres Drama Kavala * 15,500 15,080 15,500 7,080 8,000 13,950 8,080 * 15,500 15,500 15,500 8,000 8,000 15.500 9,500 * 68.07 69.94 71.62 41.60** 144 size assumed in livestock processing, the optimum number of plants ends up to be two in all the models. When the livestock supplies amount to 23,372 tons of carcass meat (actual supplies of year 1972), the optimum location of plants is Serres and Kavala. Their optimal sizes were determined to be 15,500 and 8,000 tons of carcass meat respectively. When the livestock supplies amount to 30,580 tons of carcass meat (projected supplies), then the optimum location of plants was found to be Serres and Drama. In this case, their corresponding optimal plant sizes were estimated to 15,500 tons of carcass meat for either plant. When the optimal solution includes three plants (2nd best solution of model IV, or optimum solution of model V), then the optimal location of plants was indicated to be Serres, Karala and Drama, i.e., the capitals of the corresponding provinces. Only under no economies of size in livestock processing (as it is the current livestock slaughtering system in Greece), all the potential plants are included in the optimal solution. Implications In this study no specific recommendations shall be made. What will be done is to present the trade-offs (i.e., advantages and disadvantages) of alternative solutions. Then, it is left up to the political process to make the final decision, presumably taking into consideration (explicitly or implicitly) not only economic and finan- cial factors but social and political factors as well. Out of the first five models, which assume use of modern technology in livestock slaughtering, only those models (IV and V) 145 'which are based upon the projected livestock supplies shall be con- sidered here. The main reason for this is that this analysis was primarily conducted for planning purposes. Therefore, if new slaugh- ter plants with modern technology are to be constructed in E. Mace- donia, they should be planned on the basis of having the capacity to process the anticipated future volume of livestock production. From these two models IV and V, which are based upon 1980 livestock supplies, the former, which assumes a full plant capacity utilization, is selected for further analysis. The reason for this is that model IV gives more reasonable solutions and lower minimum aggregate cost than model V. The full capacity utilization of plants which model IV assumes can be accomplished without any special prob- lems, since it was based on the assumption that plants will operate only 250 days a year and 8 hours a day. However, the operating days for a plant can be readily expanded to 270 or more days annually. This essentially implies that the degree of plant capacity utiliza- tion will be adequately below its full capacity and, therefore, the estimated volumes of livestock to be processed by these plants seems to be possible. Model VI, which reflects a continuation of the current livestock slaughtering system, will also be considered as an alterna- tive solution to the problem. That is, it will be examined if it is to the benefit of the participants of the livestock-meat industry in E. Macedonia to continue with the current system of livestock slaugh- tering or to modernize its slaughtering systems and plants. 146 Thus, to help the decision making process, the trade-offs of (a) model IV versus model VI, and (b) optimal solution versus sec- ond optimal solution of model IV will be presented next. Model IV versus Model VI Should the findings of model IV (either of optimal solution with two plants or second optimal solution with three plants) be adopted for implementation, then the probable implications which might be generated could be both positive and negative ones, or compared to the currently existing slaughtering system (model VI). As positive implications (advantages) of model IV over model VI can be considered: 1. Positive externalities will be possibly created under a sys- tem of two or three slaughtering plants suggested by model IV. The substantially greater volume of livestock slaughter- ings in each of these plants will generate greater amounts of livestock by-products, such as blood, etc. These, in turn, may make beneficial the establishment of related plants to process those by-products, which can be used as ingredients of fertilizers, feeding stuffs, etc. Thus, additional in- comes might be provided to producers and probably others. . The transportation industry may be forced to be adjusted ac- cordingly, both organizationally and operationally. Since the products (both live animals and carcass meat) will be transported in relatively longer distances and larger volumes 147 under the proposed system of two or three plants than the existing many plants system, the industry will probably be equipped with the proper size and types of trucks to perform efficiently the transportation functions. . The livestock assembly function may be forced to be organized more efficiently. Livestock public or private markets might become necessary for assembling live animals and supply them for slaughtering to the plants in larger amounts. Thus, the livestock function will be coordinated and the trucks will most probably be utilized in greater capacity than before, thus reducing the transportation cost per unit of transported product. . Other marketing functions which might be improved in response to better reorganization of livestock slaughtering might be the grading of both livestock and meat. This may help to make relatively easier the trade of live animals without re- quiring a personal inspection of them by the buyers. Meat packaging is also expected to be developed in the locations of the slaughtering plants in order to facilitate the safe shipment of carcass meat to relatively longer distances. The marketing information system regarding the availability (num- ber, kinds of animals, grade, etc.) of livestock supplies in each location (village or town etc.) and other related infor- mation is expected to be significantly improved in order to allow for an efficient performance of other marketing func- tions. 148 5. Marketing institutions or firms (e.g., meat wholesalers, re- tailers, etc.) are expected to be reorganized and improve their performance. The improvement of marketing functions and the development of some marketing institutions (such as auction markets, etc.) will enable them to give up some of their unnecessary functions (e.g., personal inspection of the purchased live animals or carcass meat). Thus, the freed time they may devote for a better management of their busis ness. In addition, some marketing channels (such as commis- sion men and possibly local dealers) might be eliminated from the livestock-meat marketing system as a consequence of the development of auction markets, grading function, etc. 6. Livestock producers' welfare is also expected to be improved, since they are going to be paid according to the quality of their product. Thus, progressive and successful producers are going to be paid better than less efficient producers be- cause of the improved quality of their products. This will encourage them to expand even more their livestock production. Towards the same direction of expanding the quantity and improving the quality of livestock production may work the additional incomes which producers are likely to earn from the processing of livestock slaughtering by-products, and the accurate weighing of live animals since automatic scales are expected to be established in key places, such as auction markets, slaughtering plants, etc. 149 7. Consumers' welfare will probably be improved as well, as a result of expected improvement in the quality of meat (accord- ing to meat technologists) due to employing modern livestock slaughtering systems. The expected development of meat grad- ing and standardization function will better satisfy the di- versified needs of different consumers and thus to improve their welfare. The continuous flow of adequate quantities of meat at reasonable prices which might be achieved as a result of improving the overall livestock-meat marketing system, will also work towards improving consumers' welfare. 8. Private business might be encouraged to enter into the live- stock slaughtering industry since the prospects for making satisfactory profits from processing substantially larger volumes of livestock in each plant are favorable. Government, as a consequence, may give up its responsibility from these activities and enter into other ones in which the private sector is reluctant to enter. Such activities could be the production of any public goods, such as the construction of roads, investments in national health and education programs, etc. 9. Economies of size in veterinary inspection of slaughtered animals is expected to be achieved, since one or two veteri- narians in each of the two or three new slaughtering plants can inspect the total volume of slaughtered animals. In con- trast, under the old system of 21 plants (requiring at least one veterinarian in each plant) it will require at least 21 10. 150 veterinarians in total in E. Macedonia and, therefore, the social cost of veterinary inspection (not directly born by the marketing firms, since veterinarians work for the govern- ment) will be comparatively much higher. An additional impact of improving the livestock slaughtering system might be the establishment of new rules and regula- tions concerning the livestock slaughtering and the informa- tion data which might flow as a consequence. Such a rule, for example, could be all the livestock be slaughtered in the authorized slaughterhouses. If such a thing happens, then it would be easy to obtain accurate information data regarding the number and kind and also age of animals slaughtered. This, in turn, will help future research to end up with more accurate and objective conclusions. As negative implications (disadvantages) of model IV over model VI can be considered the following: 1. Labor (slaughterers') displacement as a result of both ex- pected reduction of slaughter plants to two or three from the currently existing 21 and especially as a result of substitu- tion of capital (modern livestock slaughtering machinery and equipment) for labor. Of course, at the present time--when not much unemployment exists in the country because its sig- nificant industrialization progress--it may not be a serious problem to absorb these unemployed slaughterers in other jobs. However, the social cost which will be incurred for training 151 them in new jobs (if their age will allow it) must not be ignored. . Revenue loss for the communities currently having slaughter- houses, since they will lose the "slaughtering fees" paid to them for the right of using the slaughterhouse facilities. . Unit slaughtering cost, charged upon the marketing firms (local dealers, butchers, etc.) who assume the responsibility of livestock slaughtering, will be higher under the new sys- tem than under the old one. The reason is that under the new system marketing firms will bear the entire cost burden while under the old system they bear only part of it such as slaugh- terers' labor cost and slaughtering fees. . Total transportation cost will be higher under the new system of two or three plants than under the old system of 21 plants. The reason is that under the new system trucks will travel relatively long distances in order to ship livestock from supply regions to slaughterhouses and carcass meat from slaugh- terhouses to consumption centers. With the sharp upward move- ment in oil prices, the transportation cost problem will be- ? come more accute and in such a degree so that it may over- weigh the benefits of the economies of size which are expected to be generated by the modern slaughtering systems and plants. . Outflow of foreign exchange is expected to take place with the new slaughtering system in order to import the required new machinery and equipment. Under the old system it is assumed that no change will take place with regard to slaughterhouses' machinery and equipment. 152 6. Problems of disposing larger amounts of waste will have to be faced by the administrators of the new system of two or three plants. In other words, a pollution problem may be generated under the new slaughtering system while under the old system such a problem has not been serious. Optimal Solution versus Second Optimél Solution of Model IV Should the government proceed with the establishment of new slaughtering plants and systems in E. Macedonia, the question which will be raised will be: what solution should be adopted for implemen- tation? Optimal or second optimal solution? That is, should two or three plants be built in the area? Again the decision maker will con- sider the trade-offs which will appear in deciding one versus the other solution. Specifically, in deciding to implement the findings of opti- mal solution (two plants) rather than the second optimal solution (three plants), the possible positive trade-offs (advantages) might be the following: 1. Aggregate cost of livestock assembly, slaughtering and meat distribution will be lower (68.07 million drachmae) as com- pared to 69.94 million drachmae of the second optimal solu- tion. This, at least, implies that social cost will be lower under the optimal solution proposal than under the second one. 2. Comparatively less slaughtering machinery and equipment might be imported under a two plant system than under a three plant 153 system and, therefore, less outflow of foreign exchange may take place under the former than under the latter alternative solution. 3. Larger volumes of livestock by-products will be concentrated in the slaughter plant locations under a two-plant system than under a three-plant system. This may make more encourag- ing the establishment of plants for processing these by- products to the benefit of the industry participants. In contrast, the possible positive trade-offs (advantages) in implementing the second optimal solution rather than the optimal solu- tion of model IV, i.e., in building three instead of two new slaugh- ter plants, might be the following: 1. A fairer regional economic development and decentralization of economic activity (which is a basic goal of every Greek government) will be achieved, since this solution suggests, at least, one plant for each of the three provinces. It is obvious that in such a case, resources (labor, etc.) will be utilized from each province, thus contributing to their eco- nomic development. 2. Because each province constitutes an administrative entity, the provision of establishing at least one plant in each prov- ince will prevent the possible creation of political problems on the national government. 3. Total transportation cost will be lower as compared to opti- mal solution. With the continuously rising oil prices, 154 transportation cost may become a decisive factor upon the efficient performance of the entire livestock-meat marketing system. Limitations This study to determine the optimum number, size and location of livestock slaughtering plants through the transhipment linear pro- gramming model is obviously a static analysis of a clearly dynamic industry. That is, the analysis refers to a specific point of time with regard to livestock supplies, meat consumption, processing cost, transportation cost, etc., while all these variables are continuously changing with the passage of time. The consideration of the alterna- tive solution models alleviates to some extent this problem but it does not eliminate it. More complex computer programs, such as poly- period or dynamic linear programming might solve this problem to a large extent, at least, from the operational standpoint. To conduct the analysis in terms of tons of carcass meat in- stead of number of head of livestock, changed the structure of the analysis from a multi-product (cattle, sheep/goats, hogs) analysis to essentially a single product (carcass meat) analysis. This per se constitutes a limitation. However, the fact that the greater propor- tion of slaughtered animals are cattle (on the basis of which the cost data were essentially calculated) largely alleviates this prob- lem. Of course, the application of multi-product computer program- ming of the transhipment model can eliminate this problem. But the linear programming formulation of the transhipment model makes the 155 solution of multi-product plant location problems both difficult and costly. The aggregation of supply points into supply areas and disre- gard of the intra-regional transportation costs constitutes another limitation, because at least it underestimates the total transporta- tion (livestock assembly and meat distribution) cost. This problem can be largely avoided either by considering each supply point separ- ately in the computer program or by constructing very small supply regions so that the aggregation bias be negligible. However, such an approach will greatly increase the cost of computer analysis while it may not add too much information, as can be concluded from a quick comparison of the results of models I and III. The restriction of the potential plant sites instead of giving the chance to all livestock production and meat consumption points to be potential plant sites is a limitation per se. However, such a lim- itation can be substantially alleviated by carefully selecting the po- tential plant sites so that they satisfy the criterion of density in either livestock production and/or meat consumption. Another limitation imposed by the nature of the linear pro- gramming formulation of the transhipment model upon the computer anal- ysis is the procedure used in arriving at the final solution. Spe- cifically, in order to obtain the final (minimum cost) solution, an iterative procedure must be followed. That is, in every computer run adjustments must be made upon the unit processing cost according to the flow of raw product to the processing plants given in the imme- diately preceding run. Furthermore, when the solution is stabilized, 156 i.e., when the flow of product or total cost does not continue chang— ing, it does not mean that the final solution was obtained. It must be tested with another computer run with a smaller number of plants until a solution is obtained with a greater total cost than the prev- ious one. When this is done, the computer run which generates the minimum total cost gives the optimum solution. This is obviously a troublesome procedure and can be eliminated with other computer pro- grams which automatically yield the optimal solution. The clearly existing seasonality in livestock slaughterings (as Table II-3 indicates) constitutes another limitation to this an- alysis, which assumes a regular flow of live animals to the slaughter- houses throughout the year. These peaks and slumps in the volumes of livestock slaughterings will most likely affect the processing cost, since during the peak seasons overtime work (which is usually paid at a higher rate) will be required. Also, they may affect upwards the size of the plant, which may be constructed on the basis of peaks rather than on the average volumes of livestock slaughterings. This might be so in order that the plant be capable of processing those peak volumes without any special problems. However, in off-peak sea- sons the plants will operate at a lower level of capacity and unit processing cost for any size of plant will be increased. The quality of the data used in this analysis may also be a limitation. Thus: (a) the projected livestock supplies run the risk of being crude ones since they were based upon the experience and knowledge of the area by extension agronomists in Serres, Kavala and Drama, who made the projections. No micro-production study, using 157 any conventional analytical technique (e.g., econometric model, etc.) and including in it causal variables such as meat prices, etc. were used to this end; (b) total regional meat consumption was estimated through inductions from the national and provincial per capita meat consumption. No household consumption survey was undertaken to this end, while the structure of population, per capita income and other variables, which significantly affect per capita meat consumption, differ to a lesser or greater degree from region to region; (c) pro- cessing cost data reflect an average cost in processing a certain amount of livestock. No cost differentials are estimated for proces- sing different mixes of livestock and for different levels of capacity utilization of plants. These are important cost data, since they can give insights about the organization and performance of the livestock slaughtering system. Furthermore, in adjusting the processing cost data of the FAO study (conducted in 1966) to current prices, the in- flation taken place in the country since then was almost uniformly applied to all cost items (due to the lack of detailed such cost data) while this is not generally the case. Needed Research The primary objective of this study was the determination of the optimum number, size and location of livestock slaughtering plants in the area of E. Macedonia. The only decision making criterion used in the analysis was that of minimizing the total cost of livestock assembly, processing and meat distribution. The data upon which the analysis was based were either primary (such as those on livestock 158 assembly and meat distribution cost) or secondary ones, such as those of processing costs. Meat consumption data are essentially inductions from those of the nation or provinces to the specific regions. Pro- jected livestock supplies were based upon the experience, and the per- sonal knowledge of the situation by the extension agronomists of each province. Since a limited amount of time and funds were available for this study, and since this was an individual effort, there is room for additional research in this and related areas, if more detailed infor- mation is to be obtained and more successful decisions are to be made. In the following pages are listed some topics upon which a more thor- ough investigation should be undertaken in the future. 1. Regional Livestock Production. An econometric study for pro- jecting the regional livestock production or supply is re- quired. If time, money and personnel is available, it would be preferable to undertake a micro-production study with pri- mary data and thus to formulate a livestock supply function for each region separately. If this will not be possible, then the analysis can be conducted on a wider area basis, such as the whole province or geographic area (E. Macedonia, etc.) If the latter will be the case, then the appropriate inductions should be made. In either case, the variables which should be taken into consideration would be meat prices, input prices (e.g., prices of feed grains, alfalfa, etc.), input and output subsidies, availability of resources (arable land, pasture, etc.). Number and size of farms, age of 159 farmers, climatic and soil conditions, credit policies, etc. may also be taken into consideration. . Regional Meat Consumption. A household consumption survey for estimating regional meat consumption by kinds of meat is also required. It would be preferable to formulate for each region a separate demand function, since meat consumption is greatly affected not only by purely economic variables (e.g., meat prices, per capita disposable income, etc.), but also by socioeconomic variables, such as age, family size, education, occupation, etc. . Livestock SlaughteringgCost. An economic-engineering study is needed to give the following information: a. Requirements of variable inputs (labor, oil, water, elec- tricity, etc.) for operating livestock slaughtering plants of: (1) different sizes operating at the same capacity and processing a certain product mix; (2) different plant capacity utilization but having the same size of plants and operating at the same product mix; (3) different pro— duct mixes but operating at the same plant size and capac- ity utilization. b. Requirements of fixed inputs (land, buildings, machinery and equipment) for different plant sizes and different product mixes, if these make any difference upon the re- source requirements. c. After specifying the input requirements and given the input prices, the unit livestock slaughtering cost must be com- puted for: (1) different sizes of plants but operating at the same capacity and product mix; (2) same sizes of plants but operating at different capacities and using the same product mix; (3) same sizes and capacities of plants but using different product mixes. d. Estimation of the short run and long run livestock proces- sing functions and curves to find out if and to what extent economies of size exist in livestock slaughtering. 160 e. Estimation of regional seasonality in livestock slaughter- ing and its impact upon the slaughtering cost. 4. Transportation Cost. In this area it would be useful to esti- mate both the livestock assembly cost function and the meat distribution cost function on either owned or rented trucks. As exogenous variables can be selected the following: a. size of truck used in transportation; b. the travelled distance; c. the livestock species transported; d. the degree of truck capacity utilization. Such an analysis may provide a useful information as to effi- ciently organizing the livestock assembly function which so much affects the total marketing efficiency of the livestock- meat industry. 5. A Benefit-995; Ana]ysj§. Such an analysis is finally required on a very detailed basis so that both the benefits and costs of the proposed new modern system of livestock slaughtering as contrasted to the old system can be estimated. This, in turn, will help in making successful decisions to the benefit that would flow to industry participants and society as a whole. BIBLIOGRAPHY BIBLIOGRAPHY Adamopoulos, Anthony. 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Irish Journal of Agricul- tural Economics and Rural Sociology, Vol. 1 (1967-1968), 267-281. OECD. "Agricultural Development in Southern Europe." Paris, France, pp. 65-131. OECD. "Agricultural Policy in Greece." Paris, France, 1973. OECD. "Agricultural Projections for 1975 and 1985." Paris, France, 1968, PP. 61-110. Olson, Fred L. "Location Theory as Applied to Milk Processing Plants." Journal of Farm Economics, Vol. 41, No. 5 (December, 1959), 1546-1556. Orden, Alex. "The Transhipment Problem." Management Science, Vol. 2, No. 3 (April, 1956), 276-286. Papageorgiou, Euthymios. "Agricultural Policy." Agricultural Coopera- tive Press, Thessaloniki, Greece. Perrin, R. K. "Transportation and Transhipment Problems: Formulation and Solution Using MPS/360." North Carolina State University, Department of Agricultural Economics Paper 70-14, Raleigh, N. C., August, 1970. Polopolus, Leo. "Optimum Plant Numbers and Locations for Multiple Product Processing." Journal of Farm Economics, Vol. 47, No. 2 (May, 1965), 287-295. Polopolus, Leo. "An Analytical and Operational Framework for Solving Problems of Plant Location." Contemporary_Agricultural Marketing. Edited by Irving Dubov. Knoxville: The Univer- sity of Tennessee Press, 1968. Rizek, R. L.; Judge, G. G.; and Havliceck, J. "Spatial Structure of the Livestock Economy, III. Joint Spatial Analysis of Re- gional Slaughter and the Flows and Pricing of Livestock and Meat." South Dakota Agricultural Experiment Station Bulle- tin 522 (North Central Regional Research Bulletin 163), October, 1965. 167 Ruefli, T. W. "Decentralized Transhipment Networks." Operations Re- search, Vol. 19, No. 7 (Nov.-Dec., 1971), 1619-1631. Schrader, L. F., and King, G. A. "Regional Location of Beef Cattle Feeding." Journal of Farm Economics, Vol. 44, No. 1 (Feb- ruary, 1962), 64-81. Shepherd, G. S., and Futrell, G. A. Marketing Farm Products. 5th ed. Ames, Iowa: The Iowa State UniversityTPress, 1969. Smith, D. M. Industrial Location: An Economic Geographical Analy- §j§,“ New York: John Wiley & Sons, Inc.,'l97l. Sorenson, V. L., and Keyes, C. D. "Cost Relationships in Grain Plants." Michigan State University Agricultural Experi- ment Station, Technical Bulletin No. 292, East Lansing, Michigan, 1963. Stollsteimer, John F. "A Working Model for Plant Numbers and Loca- tions." Journal of Farm Economics, Vol. 45, No. 3 (August, 1963), 631-645. Thompson, W. W., Jr. Operations Research Technigues. Columbus, Ohio: Charles E. Merrill Books, Inc., 1967. Toft, H. I.; Cassidy, P. A.; and McCarthy, W. 0. "Sensitivity Testing and the Plant Location Problem." American Journal of Agri- cultural Economics, Vol. 52, No. 3 (August, 1970), 403-410. Wagner, H. M. Principles of Operations Research with Applications to Managerial Decisions. Englewood Cliffs, New Jersey: Prentice-Hall, Inc., 1969. Warrack, A. A., and Fletcher, L. B. "Plant Location Model Suboptimi- zation for Large Problems." American Journal of Agricultural Economics, Vol. 52, No. 4 (November, 1970), 587-590. Williams, W. F., and Stout, T. T. Economics of the Livestock-Meat In- dustry. New York: The MacMillan Company, 1964. Williamson, J. C., Jr. "The Equilibrium Size of Marketing Plants in a Spatial Market." Journal of Farm Economics, Vol. 44, No. 4 (November, 1962), 953-967. Newspapers Economicos Tachydromos, a weekly financial newspaper. Data and gen- eral information received from several issues of years 1969- 1974. 168 Express, a daily financial newspaper. Data received from the annual special issue Agricultural Economy '74, April, 1974. Letters National Statistical Service of Greece. Recent data on livestock and meat imports, and meat consumption. Ministry of Agriculture. Data on meat prices. Provincial offices of the Ministry of Agriculture in Serres, Kavala and‘Drama. Detailed data on livestock population and slaughterings (number and head) by kinds of animals. Technical Offices in Serres, Kavala and Drama. Maps of the provinces with distances among the villages. Veterinary Offices of the Ministry of Agriculture in the capitals of each province. Detailed data on slaughterings in each slaughterhouse monthly and annually and by livestock spe- cies, livestock and meat inflows and outflows taken place annually in each province. APPENDICES APPENDIX A-l QUESTIONNAIRE FOR TRUCKERS Name Location of your activity acy. Please answer the following questions with great care and accur- The information you provide may contribute to the improvement of your business. 1. Do you charge a standard rate of transportation services? Yes No When you determine the transportation rates, what factors do you take into consideration? a. Distance d. Feature of road construc— tion (e.g., asphalt, b. Volume shipped gravel, etc.) c. Land topography (e.g., mountainous land, etc.) e. Other Do you apply uniform transportation rates in transporting any kind of carcass meat? Yes No . If no, what are the rates per kind of meat? When you return from the shipment destination to your headquarters, do you come with empty or utilized truck? a. Empty backhaul b. Full backhaul What is the radius in kilometers of your activity for transporting the following: a. Live animals b . Carcass meat On the following table, indicate which type of marketing firm does the shipment of either live animals or carcass meat. 169 170 Types of Marketing Firms Shipping: a) Live Animals c) Carcass Meat a. Local Dealer b. Butcher c. Meat Semi-wholesaler d. Meat Wholesaler e. Other 171 mmpaz msava4 4a xazsp umpeseO4s4mm .N4 Osav O4 4a xaasp uepeseO4s4mO .44 Osap.O ea xaasp uepesmOmsmeO .O4 Osap.O 4a xazsp uepeseOOsmeO .O Osap N4 use O4 4a xossp sasEaO .O msap O use O 4a xuzsp saEEaO . mcov v .._.o Jon’s. 2044-4—00 . U) N O sap O.N 4a xussp saEEaO .O Osap N 4a xoasp sasEaO .4 msop NN4 4 use 4 4a xazsp saEEaO .O Op NN_ OO OOOOO OOEEOO .N sap 4N4 4a m4uOu4s4 .4 smpesa44x see use pews Omeuseo 4a sap sea Ompes pmaa saOpepsaOmses4 OOa: OpeaONOmenO e4ppeO smpesa44x see use 4e24se 4a usOx naem 4a uewn sea eesnaesu sO Ompes pmaa saOpepsaOOsesp xoapme>4n mpanO naem sae O4e24se e>44 4a .a: 4a 2:3 :4 OpOoneO xoss4 4a Osap awspes OpanOeO xaas4 pews Omeasea 4a mesmp s4 pews Omeosea sa O4eepse e>44 emsp4m Ospnnwnm sO uem: mmuae sa4pepsaOmses4 . msepmsa44x seq use pews mmeuseu 4a sap see so 4es4se 4a us4x naem 4a ueen sea mepes pmaa_sa4pepeaOOsesp enp use pen: use OpanOeo sOmnp OO pen: Npeee mmeusea saNuse O4e24se «>44 senp4e OsOsO4nm :4 mm: OaO au Oeuas sa4pepsaamsesp 4a mus4x pen: .N 172 "Ompaz OON 4OO OOO 4OO OOO 4o4 OO4 4OO OOO 4ON OON 4ON OON 404 OO4 4o4 OO4 4O oO 44 O4 4O OO 4N ON 44 O4 O Osap msap msap Osap msap Osap msap msap msa Osap sap N4 2 O O N40o4 OOO 4 O.N N-N\4404 N\4 "4a Oxaasmrvepese Osmem wma Oxmss4.sassao sap 4N4 4a e4an4s4 meOses easep -O4u Ospusaemessau mnp Law use meuas sappepsaamsesp Os43a44a4 esp npwz uea4 44:4 see weanuesu s4 pews mmeusea use O4e54se e>O4 span sa4 Oepes pmau sa4pepsaemses4 Osmpmsa44x s4 OeOses easepmpO NmeOses easepmpu psese444u sae use euae sa4pepsaOOsesp 4a e~4O use usOx naee 4a uea4 44:4 LOO messaesu sO pews mmeaseu use O4es4se e>p4 span 4a pseeOOnO esp sam eOsena :aO au OOO4 sa4pepsasmsesp pen: .O 173 APPENDIX A-2 QUESTIONNAIRE 0F LIVESTOCK AND MEAT SHIPMENTS (It is directed to butchers, local dealers, meat wholesalers, etc.) Name Location of activity (village, town or city) Occupation (e.g., butcher, local dealer, etc.) Please answer the following questions, clearly and accurately, keeping in mind that the information you will provide may help to im- prove the organization and performance of livestock and meat transpor- tation function. 1. Who assumes responsibility of shipping live animals from livestock producers to slaughterhouses and carcass meat from the slaughter- houses to your store? Person who assumes responsibility of Live Animals Carcass Meat shipping: a. Yourself b. Local Dealer . A person of your staff 0 d. Other 2. Do you own or rent trucks for shipping either live animals and/or carcass meat? Transportation In the Shipment of: a. Live Animals b. Carcass Meat a. Owned b. Rented 174 . Is it more beneficial to own or rent trucks for shipping the re- quired live animals and/or carcass meat? Yes No Why? . Do you negotiate the transportation rates with truckers? Yes No . Do you think that the transportation rates for shipping live ani- mals and/or carcass meat are high? Yes No . If yes, how do you think they can be reduced? . How many times per month do you make either livestock or meat ship- ments and in what amount? Frequency of Amounts shipped Shipment Of: shipments per month each time . Live animals . Carcass meat 175 ”Oopaz NOsZop so OOOe44O> oOonp smozpon 4OsOpoE -o44x sOV mosep -O4u onp we: pen: OOso4on msoosuoss emonp u4u Os3op so OOOe444> Ones son sH Nomeo noem Os OOO4 sen Oee OoO ewe noes 3o: Nmsop -oEo44x sO oosep -O4u Os4o -Opem OOO we: pen: Oospp noeo Opnm O44e -Opoe :oO u4u o4o -osm noeo 4a m4es -4se e>O4 OseE 3o: e4oosm noeo sow m4es4se 4o sonsss 4o mesmp s4 .uomo xossp enp 4o Op4 -oeaeo mnp we: pen: 4.opm .Osap N 4o xossp O .Dowv New: ooO uOu ouos so4pepsos -msesp 4o e~4O use uspx pen: Neopeu opee -4xosOOe OOOOO m4es upse o>44 OsOOOOnO 4o memeO OsoOpeoo4 omenosoa onp Ease soOpepsoOOsesp xoopmm>44 4o mmOeo pmpn NoOeo noee sO 4epap s4 Oea ooO u4u Oosos noes son use oosepmwu Os4OOOnO OsOusoOOossoo onp Oez pen; .oEOp noeo o4nm O44espoe :oO u4u m4es4se 4o us4x pen: use Osee 3on .Oppoeseo s4onp we: pen: .os4p noeo om: :oO u4u muos so4pepsaOmsesp 4o ONOO use us4x pen: .memsonsOpnOse4m onp op OmepeO .Osooouass xoopmo>4r ..O.wv . comeHLOQmO—MLP xuoamw> _.n_ . N 176 ”wepoz Nemeo noeo s4 uea4 son OeO oaO u4u noes 3o: OmsOp -meo44x s4 osopm s=oO op oaseam O4OO=O onp Eos4 oasep -O4u Os4O -O4nm onp we: pen: Npsos -O4Om OOO exes soO O4O :.Ope .se4eme4on: pews 4o memoonosez so mmmson -sopnO3e4OO Oso4peoo4 no4n3 EosO OO4OO OoO 444 pews 4a mus4x so OO4: pee: Nos4p noeo O4nm O44e -Opoe :aO u4u pews Omeuseo 4o 40sopv Op4p useoo pen: Opeoe Omeoseo 4o Osop 4o Ossep s4 uom: xoesp enp 4o Op4 -oneo mnp we: pen: 4.opo .Osop O4 4o xossp uope -sOO4s4os a .D.@V New: :oO u4u ouoE so4pepsos -Osesp 4o us4x pen: Newpeu mpeE4xosO -Oe pOOOO mpson4nm pews Omeoseo 4o OoOeO Nomeo noeo s4 Oes ooO u4u noes zon use oasepm4u Os4OO4nO onp me: pen: .os4p noem O4nm :aO u4u Op4pseoo pen: .Op4oeseo s4onp me: pen: .es4p noeo mm: soO u4u euoe so4pepsosmsesp 4a ON4O use us4x pen: .osapm sooO op mso4emo4on3 peoE 4o momoon lose: so mesonsepnOse4O onp sos4 so4pepsoOmsesp pews mOeoseo 4o memeo pm4n . co wumuLonmcm4P .4me . m 177 .eEesO use e4e>ex .Oossom.4o Omo444O 4eo4snom4 onp On uou4>osO seen o>en epeu oOon4 ”moossom ONO OOO 4ON ONO OOO OOO OOO OOO 4OO ONO msmnp< .NN OO4 ON4 404 OO4 O44 ON4 OO OO 4O O44 4x4so4emmmn4 .4N 444 ON4 404 OO4 N44 4N OO 44 ON O4 sonueEosO .ON 44 mm 404 ON NO 4m OO4 444 444 NO4 so4pmmceseO .O4 N4 OO 444 Ow OO4 NO OO4 N44 N44 O44 sasmsas4u4m .O4 N4 OO OO4 ON OO 4O OO4 4O 444 N04 4Ooxas>mz epex .N4 O4 NN NO NO OO ON NN mm mm 4O 4sempammasO .O4 0 44 NO OO OO O4 NO ON OO4 OO eEesO .O4 44 o OO ON N4 4O OO4 4O 444 No4 sapean .44 ON OO OO O4 NO O44 N44 O44 ON4 NN4 mosepaO4O .O4 NO OO O 4O O4 NO4 OO4 NO4 NO4 OO4 m44aOOommOsno .N4 OO ON 4O O N4 ON NO4 OO4 OO4 NO4 e4e>ex .44 OO N4 m4 N4 o OO Ow OO ON4 4N4 m44onaosmnp4m4u .O4 ON NO ON N4 ON O4 OO ON OO4 OO so4sonoauaO .O OO O4 NO 4O 44 ON NN OO Ow ON ma>44ouom .O o4 4O No4 ON OO O NO OO OO OO 4snu4N emz .N Om OO4 OO4 NN OO O4 OO NO ON ON emme4enpas>ez .O NO . OO4 OO4 No4 Ow NO o NN O4 O4 ep4sO4z .O ON 4O . NO4 OO4 OO OO NN o om ON mmsswm .4 OO4 444 NO4 OO4 ON4 0O O4 OO 0 O4 e44xesH .O OO No4 OO4 NO4 4N4 OO O4 ON O4 O sospmexos4u4m .N 404 O44 4ON ON4 NO4 4O ON 4O 4O OO O44aaouam .4 esesO sopexoO O44oO e4e>ex O44oosos 4sno4N ep4sO4z mossem e44xesH sospmex -OoOOOsnO -mnp4o4u eoz -os4u4m so4peoon o4 O m N O O 4 O N 4 .oooosO .e4soumoez pOeO .msopesa44x s4 x4speE oosepO4O .O-< o4ne4 x4usoqa< Appendix Table A-4. 178 Estimation of per capita meat consumption by the non-urban population of each province in E. Macedonia, Greece. 1972. Economic Variables Serres Kavala Drama Total Meat Production in tons* 12:07] 3:389 3:319 Net Exports (exports minus imports) in 4,927 _ 53 i 808 tons* Total Meat Consumption in tons* 7,l44 3,952 3,011 Urban Population 68,735 58,762 37,842 Per Capita Meat Consumption of Urban Population in kilograms 41°4 41'4 41°4 Total urban Meat Consumption [(4) x (5)] in tons* 2,845 2,433 1,566 Total non-urban Meat Consumption Non-urban Population 134,l63 49,515 53,167 Per Capita Meat Consumption of Non- urban Population [(7) % (8)] in 32.04 30.68 27.l8 kilograms *Metric tons Sources: l. The Agricultural and Veterinary Offices of the Ministry of Agriculture in Serres, Kavala and Drama. 2. National Statistical Service of Greece, "The Population of Greece," 1972, pp. 53-55, 92-93 and 157-160. 179 NON.4 ON0.04 O44.0 4ON.O OO0.0 O44 ON4.4 O44.0 440.0 4ON.O OO4.0 OO OOO.4 NO0.0 OO4.4 4ON.O OON.N OO OON.4 OO4.04 ONO.4 4ON.O OO0.0 OO4 O N4N.4 NO0.0 4ON.4 O40.4 OON.N O44 NN4.4 4NN.O OO4.0 O40.4 OO0.0 OO NOO.4 OON.O NO0.0 O40.4 OO0.0 OO OOO.4 OO4.0 O40.4 O40.4 OOO.N OO4 O NOO.4 OOO.N ONN.O N40.0 OO0.0 O44 440.4 OO0.0 O4N.N N40.0 OOO.4 OO OOO.4 NO0.0 OO0.0 N40.0 OOO.4 OO OO4.4 OON.N OO4.0 N40.0 OO0.0 OO4 O 444.4 ONN.O OON.O OOO.N OO4.4 O44 NOO.4 400.0 4NO.N OOO.N OON.O OO 400.4 400.0 OOO.N OOO.N OO0.0 OO NO4.4 NN0.0 400.N OOO.N OOO.4 OO4 m OOO.4 400.4 4ON.N ONO.N ON0.0 O44 400.4 440.4 440.4 ONO.N OON.N OO ONO.4 ONO.4 OOO.4 ONO.N ON4.N OO O4N.4 ONN.4 OOO.N ONO.N OON.N OO4 < ummz mmmugmu 4mms 4mms Nome 4pmme mmmo N mo copNWLc -Oumgu OOO.V -comgn OOO.V -comgu OOO.V -Lmo mo mcouv pcm4a we acm4 “moo “4:: Smog 4mpo4 “moo m43m4cm> “moo caxwa ummmmaoga =o4pme44pz a 4muo4 4mpoh wE=4o> Ou4umamu .mommgu .m4coumomz .O .co4pmn444u: Nu4ooamo pcm4a mo m4m>m4 pcmgmmm4v On ucm mm~4m pcm4a an “moo O=4LmuOO=m4m .O-< m4nm4 x4u=maq< 180 .O->H m4am4 mo mumu mg“ Oo m4mma msp co umuaaeou coma m>ms mumu mmmg4 "mogzom 4NO OO0.04 NN0.0 4N0.0 OOO.N4 O44 NOO.4 OON.O4 NO0.0 4N0.0 OO4.N4 OO OOO.4 OOO.44 NOO.N 4N0.0 OO0.04 OO OOO ONO.44 NON.O 4N0.0 OO0.04 OO4 O OO4.4 OOO.N4 OO0.0 4N0.0 OO4.04 O44 OOO.4 NN0.04 400.4 4N0.0 OOO.N OO ONN.4 400.04 OON.O 4N0.0 OO0.0 OO OON.4 OO4.44 440.0 4N0.0 OO0.0 OO4 O 4mm: mmmugmu Nome 4mms 4mme Npmms mmmo 4 yo cop\mgu -gumxc coo.v -gumgu coo.v -gumgu coo.v -Lau yo mcopv pca4a $0 umou 44:: pmoO 4mpo4 “moo m4nm4gm> “moo umx4u ummmmooga :o4umN444pO pcm4m 4mpo4 4muo4 was4o> Nu4omnmu .tmzcwucoo .Ou< m4nm4 x4ucmnn< Typed and Printed in the U.S.A. Professional Thesis Preparation Cliff and Paula Haughey 144 Maplewood Drive ' ' .-'7 East Lansing. Michigan 48823 3 Telephone (617) 337-1527