A SIMULATION STUDY OF DECISION STRATEGIES FOR HOG PROCESSING PLANTS CONSTRAINED BY ENERGY SUPPLY REGULATIONS Dissertation for the Degree of Ph. D. MICHIGAN STATE UNIVERSITY GARY A. DAVIS 1977 4: {nu I III IIIIIIIIIIII I III III II III I {O 43%. T Cal OLD ICI ABSTRACT A SIMULATION STUDY OF DECISION STRATEGIES FOR HOG PROCESSING PLANTS CONSTRAINED BY ENERGY SUPPLY REGULATIONS By Gary A. Davis Energy use has been rapidly rising throughout the industrialized nations since the turn of the century. With the formation of the Oil Producing-Exporting Countries' cartel in 1974, energy supplies have become uncertain and energy prices have risen relatively rapidly. The United States government has responded to this energy situation by establishing the Federal Energy Administration (F.E.A.) and charging it with the responsibility of decreasing domestic energy consumption. In addition, the Federal Power Commis- sion (F.P.C.) has responded to relatively high demands on natural gas supplies by implementing a reduction program for industrial users. The proposed programs by F.E.A. and F.P.C. were ex- pected to affect hog processing plants. The F.E.A. has pro- posed an energy reduction goal for several industries, a goal of 12 percent had been set for the meat packing industry. The F.P.C. proposed a sequential reduction of interruptible natural gas supplies for several meat packing plants. In Gary A. Davis some plants, the natural gas supplies could be reduced as much as 30 percent by 1980. Since many meat packing plants had modified their activities prior to these (F.E.A. and F.P.C.) regulations, further energy supply reductions were expected to cause extensive adjustments and investments. Three strategies for adjusting production activities constrained by energy supplies were considered: ' 1. Change production levels 2. Change the composition of products produced, and 3. Use less energy intensive processes. A simulation model was designed to investigate the effect of these strategies on the energy consumption and the before tax earnings of a midwestern hog processing plant. The model has five basic components that simulate and relate (a) the flow of products, (b) the plant's price and supply expectations, (c) the plant's decision criteria, and (d) energy reducing technology to the earnings of the hog pro- cessing plant. A six-year (76-82) time horizon was simulated for three different scenarios. The first scenario simulated the affect of expected energy price increases and assumed energy reducing technology was not available. The second scenario was designed to provide the most likely estimate of the hog processing plant's situation, technology was assumed avail- able as expected energy price increases occurred. The final Gary A. Davis variation considered a profit maximizing criteria in place of the plant's normal production strategy. The research results show that the F.E.A. and F.P.C. energy reduction preposals can be simultaneously met without adversely affecting the hog processing plant's be- fore tax earnings. An initial investment of nearly $200,000 could decrease overall energy consumption about 20 percent. It was assumed, however, that sufficient fuel oil supplies would be available to replace decreased interruptible natural gas supplies. Fuel oil price increases of 300 per- cent were considered in the six-year analysis as other pro- ducers were also expected to increase their fuel oil de- mands. Decreasing production was not found to be a finan- cially rewarding strategy as annual earning reductions approaching 2 million dollars could occur. A profit maxi- mizing strategy would increase earnings 20 percent if com- peting firms would not change their production strategies. A SIMULATION STUDY OF DECISION STRATEGIES FOR HOG PROCESSING PLANTS CONSTRAINED BY ENERGY SUPPLY REGULATIONS By \\to Gary A. Davis A DISSERTATION Smeitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics 1977 DEDICATION TO JAMES J. AND BERNA M. DAVIS 11 ACKNOWLEDGMENTS Many individuals and groups contribute considerable time and resources towards the completion of any disserta- tion. There are four main contributors that deserve recog- nition for their assistance in the completion of this dis- sertation: The George A. Hormel Company, Inc., the Economic Research Service, U.S.D.A., Dr. Larry J. Connor, and my family. All three sectors, the federal government, the state government, and private industry contributed resources and information which were vital to this research. I would like to thank the George A. Hormel Company, Inc. for their contribution of data and personnel time. Their willingness to assist education and research was amply reflected in the warm and helpful attitudes of their staff. Particular thanks are due to Bruce Corey, Steve Wright, and Ed Gibson. The Economic Research Service provided financial support which funded this research project and their con- tribution is appreciated. Michigan State University provided the services of many capable individuals. Professor Larry J. Connor, as the major advisor, deserves a special thanks for his contribu- tions over the years. In addition, I would like to thank Professors John N. Allen, J. Roy Black, James T. Bonnen, Phillip L. Carter, and Thomas Pierson for their guidance and assistance. A special thank you is given to my wife, Susan, and my children, Burton, Cindy, and Kristy, for their patience, encouragement, and understanding during the last four years. My parents, James J. and Berna M. Davis, have contributed so much for so long that a thank you seems most inadequate. Consequently, I have dedicated this dissertation to them. Many other individuals have been very helpful. I would particularly like to thank Mrs. Louise Smith, the editor, Mrs. Linda Buttel, the programmer, and Mrs. Kathy Reed, the typist, for their invaluable assistance. Each of these people used their special skills and provided en- couragement which greatly contributed to the completion of this dissertation. Although many people have contributed to this dis- sertation, any errors or omissions are the sole responsi- bility of the author; and any such errors should not de- tract from the contributions of others. iv TABLE OF CONTENTS LIST OF TABLES . LIST OF CHAPTER I. II. III. IV. V. FIGURES INTRODUCTION Introduction . . Research Objectives . Research Methodology . Summary Organization of Thesis ENERGY REGULATIONS CONCEPTUAL FRAMEWORK . HOG PRODUCTION PROCESSES Description of Hog Production Processes. Hog Slaughtering Activity Carcass Cutting Activity . Rendering Activity . Clean- -Up Activity . . Description of Energy Utilization in A Hog Processing Plant . Hog Scalding Energy Demands Hog Dehairing Energy Demands . Inedible Rendering Energy Demands Edible Rendering Energy Demands Blood Drying Energy Demands Clean-Up Energy Demands . Refrigeration Energy Demands REVENUE AND COST ESTIMATION Revenue Estimation . . . Wholesale Value of Hogs Liveweight Value of Hogs . . Commercial Hog Supply Estimation Cost Estimation . . PAGE vii viii 3—: CHAPTER VI. SIMULATION MODEL Description of Simulation Model Expectation Component of The Simulation Model . . . . . Decision Component of The Simulation Model Production Alternative Component of The Simulation Model Description of Energy Reducing Technology . . Validation of Simulation Model VII. RESULTS VIII. SUMMARY AND CONCLUSIONS Summary Implications . Suggestions for Further Research . BIBLIOGRAPHY APPENDIX A. Production Component Equations of The Simulation Model . . B. Energy Demand Component Equations of The Simulation Model C. Predicted U.S. Commercial Hog Supply, 1976-1982 . . . 0. Predicted U.S. Commercial Hog Liveweight Value, 1976-1982 E. Operating Procedure of Simulation Model vi PAGE 78 81 84 87 9O 93 98 101 115 115 123 125 129 132 135 138 139 140 TABLE 10. 11. LIST OF TABLES Daily Energy Demand for Selected Hog Processing Activities . . . Daily Hog Processing Costs - Slaughter Cut, Render - 1974 . . . . . . . Comparison of Hog Processing Plant Simulation Models . . Expected Reduction Schedule for Interruptible Natural Gas Expected Fuel Oil and Interruptible Natural Gas Prices . Energy Reducing Production Alternatives Considered in The Hog Processing Simulation Model . . . . . Net Present Value of Energy Reducing Production Alternatives - 1976 Results of Hog Processing Plant Simulation Model Simulated Energy Savings From Technology Available to a Hog Processing Plant . . Simulated Natural Gas and Fuel Oil Consumption for A Case Study Hog Processing Plant, 1976-1981 Simulated Energy Cost for A Hog Process Plant . . . vii PAGE 58 74 80 85 86 91 104 106 109 111 112 FIGURE 1. Omflm 10. LIST OF FIGURES Substitution Relationship Between Fuel Oil and Natural Gas as Boiler Energy Sources Effect of A Natural Gas Supply Constraint . Combined Effects of Energy Reducing Technology and Natural Gas Supply Constraints . . Energy Reduction and Total Revenue 'Decreases Occurring From Changing Output Composition Hog Slaughtering Processes Standard Dismembering Procedure . Flow Chart of Hog Production Process Flow Diagram of Simulation Model Production Strategies for A Hog Processing Plant Effects of Alternative Production Strategies On The Plant' 5 Earnings and Energy Consumption . . viii PAGE 32 33 35 36 41 45 50 92 103 107 CHAPTER I INTRODUCTION Introduction Energy use has been rapidly rising throughout the industrialized nations since the turn of the century. With the formation of the Oil Producing-Exporting Countries' cartel in 1974, energy supplies have become uncertain and energy prices have risen relatively rapidly. Rapidly rising oil prices have spurred oil importing countries to reduce oil consumption as their trade balances became less favor- able. Governments have reacted by encouraging consumers to lower their energy usage through a multiplicity of methods. Laws have been passed, governmental agencies were formed, and moral suasion has been used. In spite of reported energy savings which have oc- curred in the United States, energy consumption has not de- creased. Firms have been able to reduce energy consumption in many plants by as much as ten percent1 and residential consumers have taken voluntary actions to reduce home 1Howard Cross, Office of Energy Evaluation, Michi- gan Department of Commerce, Personal Correspondence, September 1975. 2 energy demands. But the trend of energy consumption between 1950 and 1971 has continued. Energy consumption in the United States doubled in this 21-year time span and petro- leum and natural gas have borne the brunt of this increased energy demand. By 1972, natural gas and petroleum supplied nearly 80 percent of the U.S. energy demands.2 Our production capabilities, however, have not kept pace with our energy demands. Because of the pricing structure, the demand for natural gas has exceeded the available U.S. supply through- out the seventies and thus creating a shortage of natural gas. This shortage has grown from 0.1 trillion cubic feet in 1970 to an expected 4.0 trillion cubic feet in 1976. Similarly, crude oil imports into the United States have nearly doubled between 1972 and 1976. Faced with rising energy consumption and rising energy prices, federal regulatory agencies and legislators begun to propose various forms of action to change the direction of the United States' energy consumption. The Federal Power Commission has established priorities on natural gas usage. "The highest priority users -- resi- dential and small commercial customers, as well as indus- trial use for plant protection, feedstock, and process needs -- are the last to be curtailed in times of 2DeGolyer and MacNaughton, Twentieth Century Petro- leum Statistics, September 1, 1973, p. 95? 3 shortage."3 Similarly, the Federal Energy Administration has begun to adapt energy conservation suggestions proposed by legislators. Further control and reductions of energy use are being sought by legislators. Although not having legal status at this time, the proposed legislation does indicate the position and concern of government leaders. Congress is proposing specific energy reduction goals as evidenced in H.R. 6860.“ This legislation is in- tended to reduce U.S. dependence on foreign oil within ten years. A specific schedule has been proposed which will limit the daily importation of oil into the United States. The goal is to decrease oil imports to less than 25 percent of domestic production by 1985. Additional taxes have been scheduled for natural gas, crude oil, and gasoline. Legislation, such as H.R. 7014, is intended to in- clude all major energy consuming industries which utilize at least one trillion British thermal units (B.T.U.) of energy per year. Energy conservation goals for each in- dustry will probably be established by the Federal Energy Administration for at least the ten most energy consumptive industries. Corporate officers of high energy consumptive 3Federal Energy Administration, The Natural Gas Shortage: A Preliminary Report, August 1975, p. 8. l'U.S. Congress, House, Energy Conservation and Conservation Act of 1975, H.R. 6860, May 9, 1975, 93rd Congress. 4 firms will be expected to report improvements in energy efficiency to F.E.A. on an annual basis. As a minimum, any group of corporate officers which refuses to report can be held in contempt of court. The Federal Energy Administration has taken the proposed legislation as an indication of the intent of Con- gress. Consequently, F.E.A. has begun to meet with several industries to establish reasonable energy reduction goals. In addition, several research studies have been funded by F.E.A. to study energy reduction potentials in several in- dustries. F.E.A. provided funds to initiate energy research in the food processing area by means of a survey for 12 of the 44 food and kindred products industries.5 This re- searCh was designed to: estimate types of energy use, to determine variations in energy use among plants, to iden- tify conservation potentials, and to determine key con- straints on current operations.6 The industries included within this study were: meat packing, sausage and other prepared meats, fluid milk, canned fruits and vegetables, frozen fruits and vegetables, animal feeds, wet corn mill- ing, cane sugar refining, beet sugar, malt beverages, SDevelOpment Planning and Research Association, Inc., Industrial Energy Study of Selected Food Industries, March 22, 1974, F.E.A. Contract No. I4FOILOOOI-1652. 6Ibid., p. I-2. 5 animal and marine fats and oils, and manufactured ice. Each industry was defined according to the SIC classification of the U.S. Bureau of Census. The meat packing industry ranks as the most impor- tant of the industries studied. It has the highest value of shipments, the most employees, and is the largest single user of energy. Over 100 trillion B.T.U. are used annually by the meat packing industry.7 Meat packing and the associated plant processing utilized more gross energy than any other food industry processor. Almost ten percent of the total food processing energy was used in the meat packing-processing area. The major portion (80%) of their annual energy requirements were derived from red meat and by-product processing. The remaining 20 percent was utilized for processing prepared meats at the slaughter house premises.8 Nearly half of the meat packing industry's energy needs were provided by natural gas and about a third were supplied by electricity. Petroleum derivatives, such as residual oils and middle distillates, along with coal each provided ten percent of their energy demands. Although propane provided only two percent of the industry's energy 7Ibid., p. II-l, II-4. 8Foster 0. Snell, Inc., Energngonservation in The Meat Packing Industry, F.E.A. Contract No. C-O4-50090-00, January 1975. 6 needs, the meat packing industry consumed one-fourth of the propane utilized by the twelve industries surveyed.9 I These simple averages of energy use patterns in the meat packing industry can be misleading. Energy consumption between plants and geographical regions is quite diverse. Natural gas provides about 40 percent of the Mid-Atlantic region's meat packing energy needs; but, in the Pacific region, it provides nearly two-thirds of the industry's energy needs. Since electrical energy provides between 29 and 34 percent of industry's energy demands, fuel oil and coal must offset the natural gas variances between geo- graphical regions.10 Energy consumption between plants within a region shows more diversity than regional research11 which revealed that the energy required per pound of liveweight processed could vary between plants by as much as 400 to 2,700 B.T.U. This difference has been attributed to the type of animal processed, the degree of by-product processing, and the final form of the products sold. The energy research studies funded by F.E.A. have reported conflicting Opinions on energy reduction potential in the meat packing industry. One reports that a five 9Development Planning and Research Association, Inc., Industrial Energy, Exhibit II-4, II-S. 10Ibid., Exhibit III-2. 11Ibid., Exhibit III-13. 7 percnet energy reduction goal would necessitate a reduction in output.12 Another considers a 30 percent overall energy reduction goal as reasonable.13 An immediate short-run re- duction of 13 percent is possible and an additional 13 per- cent could occur in the long-run if considerable investment were undertaken. A further intermediate adjustment to re- duce energy demand another 6 percent would reduce total energy demands more than 30 percent. The Federal Energy Administration is negotiating with major meat packing firms to reach a mutually accept- able energy reduction goal. While the F.E.A. advocates the 30 percent reduction goal, many of the major firms believe that a large number of energy reduction adjustments have already been undertaken and an additional 30 percent reduction is unreasonable. Secondly, they claim that F.E.A. research indicated energy reduction potentials without assessing economic and political feasibility. Energy re- ductions brought about by modifications of U.S.D.A. sani- tation regulations were particularly questioned by the meat packing industry. 12Ibid., Exhibit III-16. 13Foster 0. Snell, Inc., Energy_Conservation, Ex- hibit V-9. Research Objectives The meat packing industry is concerned about future energy supplies and costs. "Petroleum products are under allocation controls caused by their short supply, (and) the industry is faced with significant cutbacks in the supply of natural gas."1” In addition, the F.E.A. has been granted the authority to establish energy conservation goals and to withhold energy supplies if those goals are not met. Several questions have been raised regarding the effect of energy supply constraints even though the con- straints have not been fully identified. Major energy questions which require answers are: 1. What are the various energy demands in meat packing plants? 2. What production adjustments, equipment, or tech- nology are feasible to reduce energy demands? 3. What effect will further energy price increases have on the future financial health of the in- dustry? The purpose of this research was to evaluate the effect of energy supply constraints and rising prices on a 1“Development Planning and Research Association, Inc., Industrial Energy, p. III-17. 9 hog processing plant. To accomplish this purpose and to provide a tentative answer to current energy questions, the following specific research objectives were proposed: 1. To describe the hog production processes 2. To delineate mass-energy flaws for production pro- cesses which utilize natural gas and fuel oil 3. To identify and evaluate production adjustments and technologies which might reduce energy flows 4. To estimate the effect of alternative production strategies on the financial position of the firm 5. To evaluate the financial impact of a 12 percent reduction in total energy utilization. Research Methodology Meat packing plants are located in several areas of the United States and they have been constructed and modi- fied considerably over the last 100 years. Each area has changed over time as population, agricultural production, and transportation facilities grew and adjusted to our nation's needs. Many members of the meat packing industry have indicated that aiclassification system to identify typical or representative plants would be nearly impossible to develop.15 Changing technology and a myriad of produc- tion processes of varying size, which would be organized in lsFoster D. Snell, Inc., Energy Conservation, p. III-2. 10 many ways, seem to pose an array of firms which cannot be classified in a representative manner. The generalizations which appear acceptable to the industry are (1) that the major portion of the meat animals slaughtered are processed in plants with a rated hourly capacity in excess of 75 head15, and (2) the most numerous groups of meat packing plants will slaughter, process, bone, and render edible and inedible fats.17 A single plant analysis was chosen as the best means to consider the effect of energy constraints and rising prices on the meat packing industry. It was deter- mined that the analysis of one plant could provide specific information which would be preferred to an analysis of several firms, which would not be considered representative. A hog processing plant was also preferred to beef, veal, or sheep since the energy required per pound of hog product is considerably greater. Consequently, the proposed energy supply constraints were hypothesized to be more acute for hog processing than for other.red meat processing plants. A midwest hog slaughtering plant agreed to co- 0perate on this reseanch. The plant was sufficiently large (over 75 head per hour) to include many of the processes performed by other meat packing plants. Other livestock 16Allen J. Baker, Personal Correspondence, Economic Research Service, U.S.D.A., November 4, 1975. 17Ibid. 11 were not processed at the plant. The hog processing plant was expecting federal regulations which would force a decrease in total energy usage. In addition, a major pr0portion of its natural gas supplies would be eliminated. The plant manager has been informed that the natural gas supply limitation will be effective in 1980 and that the energy reduction goal must be met by 1982. As other industries are also forced to re- place natural gas by other energy sources, the price of these sources were expected by the plant to rise rapidly. The management of the hog processing plant was con- cerned with selecting a course of action when the con- straints were unclear, when future prices were unknown, and when technically feasible production adjustments had not been assessed for economic feasibility. Two questions which emerge from this uncertain situation are: 1. What decision strategies will the firm most likely follow? and 2. What decision strategies should the firm follow? Simulation has been selected as an appropriate technique to approximate the firm's behavior. This tech- nique allows the cause and effect information to be traced over time without actually changing the plant's operations. "A mathematical model of decision rules, information sources, and other interactions among the components of an organization are formulated, and the model's behavior 12 through time is generated on a digital computer."18 The components of the simulation model were: 1. Production processes component Energy demand component Expectation component Decision strategies component (JV-DOOM Production alternative component The production processes component was designed to simulate the flow of intermediate and final products through the plant. The hog processing plant was initially contacted to describe the production processes and general operational procedures. Production activities were defined as slaughter, cutting, rendering, and clean-up. The stages of slaughtering consisted of stunning, bleeding, scalding, hair removal, singeing, eviscerating, and carcass chilling. Stages of the cutting process includes blood drying, edible and inedible products. Plant clean-up was one activity that transcended across all production areas. Data concerning the quantities of hog components entering and exiting each stage of production was obtained from the firm's records. Production coefficients were ascertained by computing simple averages over the most 18Halter, A. N., and Dean, G. W., "Use of Simulation in Evaluating Management Policies Under Uncertainty: Appli- cation to A Large Scale Ranch," Journal of Farm Economics, August 1965, Vol. 47, No. 3, p. 557. 13 recent 250 production days (one year equivalent). Energy utilization within a hog processing plant was needed for heating and cooling. Steam was commonly used as the major heat source and required nearly 80 per- cent of the total energy usage in a hog processing plant.19 The steam boilers have generally been equipped to utilize either natural gas or fuel oil interchangeably. The refri- geration processes utilize natural gas, propane, and elec- tricity. About half the refrigeration energy demands and all of the steam boiler energy demands have been provided by natural gas. These energy demands are the most affected by natural gas limitations and rising prices of substitute energy sources. Energy demands for refrigeration and boiler steam were studied in the hog processing plant. Mass-energy flows were determined for each production process which utilized boiler steam and required cold storage. Various tests were combined with standard engineering methods by the cooperating firm's engineers to determine the energy use within the packing plant. Total plant energy utilization was estimated from linear regression equations derived from engineering studies of hog packing plants.20 Derived energy demands for hog 19Johns-Mansville, Inc., "How Pork Plant Rates On Energy," National Provision, January 31, 1976, p. 30. 20Ibid., p. 34. 14 scalding, hair removal, blood drying, edible rendering, and clean-up were obtained from the plant personnel. Refri- geration energy demands were determined for the carcass chill cooler, the carcass cutting floor, the loin cooler, .the fresh meat cooler, the offal freezer, and the shipping area. The expectation component of the simulation model reported monthly costs and revenues. Production cost esti- mates were derived from two data sources. The actual costs of production were obtained from the plant. One year's data was utilized to ascertain processing costs over a wide range of production; and an appropriate functional form was fitted to the data. Secondary data based on a 1974 Corn Belt-Lake states survey21 of hog slaughter plants was uti- lized to shift the functional form vertically. The second- ary data was from plants of approximately the same size as the case study plant. Shifting the firm's actual cost function prevented disclosure of the firm's proprietory information. Labor costs were maintained as a separate cost item in the accounting procedures due to the peculiarity of the industry-union contract. The plant's union contract guaranteed 36 hours of wages to the employees regardless of the hours worked. If the employees worked less than 144 21Food Manager, Inc., Cost Compgnent - Cattle and Hog_Slaughter Plants, U.S.D.A., E.R.S. Contract No. 12-17- 03451943, October 1974. 15 hours a month, the difference between hours paid and hours worked was carried over to the following month. The labor hours carried over were credited against any overtime hours worked in the following month. This provision allowed the plant to essentially store labor costs for 30 days while guaranteeing a minimum income level to the employees. Con- sequently, production decisions were influenced by the amount of labor hours carried over from the previous month. Revenue for the firm has been determined by the product price and the quantity of product sold. Although a hog processing plant does produce a multiplicity of pro- ducts, it is fortunate that the products sold are a linear combination of the basic input - the live hog. By extending the composite wholesale price per unit of live hog pub- lished by the U.S.D.A.ZZ, an estimate of total revenue was obtained. Total revenue for the hog packing plant was esti- mated by extending the composite monthly wholesale hog price six years into the future. This extension required three separate estimation steps: 1. Estimate the U.S. monthly commercial hog supply by a harmonic Fourier Series approximation of the four-year hog cycle 2. Hog slaughter price estimation by linear regression 22U.S. Department of Agriculture, Livestock and Meat Statistics, 5.8. NO. 522, ERS, SRS, AMS, JUTy 1973, p. 28. 16 3. Estimate the wholesale price - slaughter price relationship as a linear function of the U.S. hog supply. The composite output price was estimated by multiplying the hog slaughter price by the wholesale slaughter price ratio. Subsequently, monthly revenue was determined by a composite output price and the quantity of hogs slaughtered. The short-run decision component of the simulation model was designed to select the best level of production for each month. The decision criteria for the firm was as- certained and was used to determine the hog slaughter rate for each month. The decision criteria proposed by economic theory and profit maximization was also considered. A comparison of these two criteria was utilized to determine the effect of these decision criteria. The long-run decision component was concerned with annual decisions regarding energy utilization adjustments. The production adjustments which were selected for economic analysis are: 1. Shell and tube ammonia condensers Continuous cooker heat reclamation Low temperature rendering Hog singer heat-exchange equipment Centrifuge blood drying 0301th Selling whole blood The firm's decision criteria for selecting 17 production alternatives was based upon the plant's internal rate of return (15%) and expected energy prices. The net present value approach was included within the model. The firm had considered the possibility of convert- ing to other energy sources such as coal. Their analysis eliminated coal as a substitute energy source. The produc- tion alternatives listed above will be adopted as they meet the firm's criteria. If none were selected by 1982, the firm would select a sufficient number of alternatives that would result in a 12 percent overall energy reduction. This total energy reduction goal by management was deemed essen- tial to maintain federal assurance of sufficient fuel oil supplies beyond 1982. Summary The hog processing plant has three decision strategies from which it can choose, either singularly or in combination: 1. Reduce output 2. Change output composition 3. Change energyTuse by utilizing less energy inten- sive processes. This research was intended to appraise the best strategy for the plant as energy constraints were imposed and as energy prices increased. The decision criteria of the firm was compared with the decision criteria proposed by 18 economic theory. Simulation was selected as an appropriate technique to approximate the behavior of the plant over a six-year time horizon. Although specific plant peculiarities exist, the same energy constraints are being imposed on other meat processing plants. In addition, many hog processing plants utilized the same energy processes as the selected mid- western hog plant. Consequently, the evaluation of various energy reduction technology and decision strategies should be readily applicable to other meat processing plants. Ogganization of Thesis The hog processing plant has been forewarned that natural gas usage and total energy usage must be decreased. Following this introduction, the regulatory power granted to the Federal Energy Administration and the Federal Power Commission is reviewed. Production theory was re-examined in regards to energy constraints and rising energy prices in Chapter III - Conceptual Framework. The physical production processes of the hog plant were described in Chapter IV. The description starts with a live animal in the holding pen and proceeds in the same order as a hog would be processed. The derived energy de- mands of the production processes were enumerated in the same order as the physical processes occur. Revenue and cost estimation procedures are eluci- dated in Chapter V. Input supply and price estimation 19 procedures were developed in the first section, and cost estimations were derived from the level of input use. The simulation model was explained in Chapter VI. Here the decision strategies and expectations of the firm were integrated with the physical and financial information from Chapter IV and V. The results, summary and conclu- sions of this analysis are reported in Chapters VI and VII, respectively. CHAPTER II ENERGY REGULATION The Federal Power Commission (F.P.C.) and the Federal Energy Administration (F.E.A.) have been given the authority by Congress to regulate natural gas and energy, respectively. Natural gas was considered an important national resource many years ago when Congress first pro- vided for the regulation of natural gas supplies and prices with the National Gas Act of 1938. Since that time, other energy sources had escaped direct supply controls, except in times of war, until 1974 when Congress established the Federal Energy Administration. Both congressional acts granted authority of supply and price control to these regulatory bodies. The 1938 congressional action gave the "Federal Power Commission authority for regulation of interstate natural gas companies, including the exportation or impor- tation of natural gas, rates and charges, determination of cost of production and transportation, ascertainment of cost of property, records and memoranda, and rates of de- preciation." An amendment in 1954 did remove the Federal Power Commission's authority for intrastate natural gas 20 21 regulation, provided that state regulation existed?-3 With the establishment of the Federal Energy Ad- ministration (F.E.A.) in 1974, Congress assigned 12 specific functions. The three most pertinent functions for this research are:2'i 1. "Develop and oversee the implementation of equit- able and mandatory energy conservation programs and promote efficiencies in the use of energy resources" 2. "Develop plans and programs for dealing with energy production shortages" 3.- “Assure that energy pragrams are designed and im- plemented in a fair and efficient manner so as to minimize hardship and inequity while assuring that the priority needs of the nation are met". Although the F.E.A. is a young agency, it has been granted considerable powers. Initially, the F.E.A. flexed its muscles by notifying both energy users and suppliers that it could directly affect them. Suppliers of crude oil, residual fuel oil and refined petroleum products produced in or imported into the United States were notified that they must supply all end-users which purchased an allocated 23American Gas Association, Gas Rate Fundamentals, 1969, p. 91. 2"U.S. Congress, House, Public Law 93-275, H.R. 11893, 93rd Congress, lst Session, May 7, 1974. 22 product from them prior to January 5, 1974. This decree affected contractual relations between users and suppliers and the F.E.A. made it clear that it would use its author- ity to transfer energy supplies from one region to another and between industries if such action were necessary.25 In addition, the Federal Energy Administration has classified industries according to a priority system. If an end-user wants to maintain an established "base volume" and its priority rating, it must certify to the F.E.A. that it has an energy conservation program in effect.26 The F.E.A. has required end-users of energy supplies to have a "base period volume" of energy consumption which was deter- mined on a monthly basis for each of the twelve months prior to February 1, 1974. Adjustments to this base period volume may occur under "unusual growth" circumstances. Growth in excess of 10 percent in any one year can be used as an adjustment to the base period volume. Agricultural industries which have received a priority rating (which provides 100 percent of their base period volume) are classified according to the standard in- dustrial code numbers (Bureau of Census) in Division A, Agriculture, Forestry, and Fishing, and Division 0, Manu- facturing of Food and Kindred Products, Major Group 20. 25U.S. Government, Federal Register, Friday, March 29, 1974, Vol. 39, No. 62, pp. 11771-11777. 260p.cit., p. 11778. 23 Exceptions to these general classifications have also been made by F.E.A.27 The Federal Energy Administration has been given the authority to withhold or assure supplies of crude oil, residual fuel oil, and refined petroleum products. Al- though, specific agricultural industries have been granted an allocation equivalent to their base period volume, this allocation is not assured if the end-user does not have an energy conservation program in effect. The meat packing industry has relied upon natural gas and petroleum products to directly supply nearly two- thirds of its annual energy demands.28 Electricity supplies another 10 percent and part of this has been generated from gas or petroleum products. Both natural gas and petroleum derived products are regulated by federal authorities and adjustments in these regulations can affect the cost and production levels of meat packing firms. The meat packing industry has historically oper- ated with two types of natural gas contracts. One contract specifies a quantity of natural gas which must be delivered to the firm. This is referred to as a firm gas contract. 27Specifically excluded industries are 0181, 0189, 0271, 0279, 0742, 0752, 0781, 0782, 0849, 2047, 2065, 2067, 2084, 2095, 2097. Specifically included industries are 2141, 2411, 2421, 2873, 2874, 2875, 4971. 28Development Planning and Research Association, Inc., Industrial Engineering, Exhibit II-4. 24 The second type of contract is referred to as an interrupt- ible natural gas (I.N.G.) contract. This contract specifies a quantity of natural gas which will be delivered only if higher priority users do not require gas. Interruptible natural gas has provided half to two-thirds of the non- electrical energy demands of meat packing plants. The past operating procedure followed by the in- dustry has been the reduction of I.N.G. during the winter months when home heating demands are high. During these months, the industry uses other energy sources, such as residual fuel oils. In the summer months, interruptible natural gas has been plentiful. Interruptible contracts generally have specified lower per unit gas prices than the firm contracts to compensate users for the unreliable gas supply. The relatively recent emphasis of energy conser- vation and the depleted natural gas supplies have changed the energy supply situation. The Federal Power Commission has established natural gas curtailment priorities, and the meat packing industry's natural gas usage fits in the lowest priority categories - boiler fuel use and interrupt- ible usage when alternative fuel capabilities exist.29 The Federal Energy Administration has established energy conservation goals for industries under the authority 29Federal Energy Administration, The Natural Gas Shortage: A Preliminary_Report, August 1975, Table 1. 25 granted by Congress. The power to enforce energy conser- vation goals currently is derived from F.E.A.'s authority to allocate petroleum and petroleum products when energy demands exceed supplies. If energy conservation goals are not met, F.E.A. can transfer energy supplies between geo- graphic regions and between industries. The hog packing plant has been informed by the suppliers of natural gas that all interruptible natural gas will be completely shut off by 1981. In addition, after 1977, interruptible natural gas contracts will be limited to half of the previous years' contracted supplies. As natural gas users switch to other energy sources, those sources are expected to suffer price increases. The impact of this general anticipation is that fuel oil suppliers are limiting their contractual agreements to 30 days. Every 30 days, price and quantity will be re-negotiated. The F.E.A. has proposed an energy conservation goal of 12 percent for the meat packing industry. All meat packing plants will most likely be required to reduce their total energy demand 12 percent below their 1974 level by January 1983. If this expected goal is not met, the firm will not be assured of more than 30 days of energy supplies. The threat of losing all energy supplies is sufficient to convince the hog packing plant to reduce overall energy demands. 26 The combined effect of federal regulations, F.P.C., and F.E.A. on the hog processing plant is: 1. A scheduled reduction in interruptible natural gas supplies, and 2. An overall energy reduction goal near 12 percent. CHAPTER III CONCEPTUAL FRAMEWORK The meat packing industry was anticipating energy supply constraints and rising energy prices. Rising energy prices would affect all producers, but the energy supply constraints were not intended to affect all producers equ- ally. Small producers, those using less than 50 MCF on a peak day, had a higher natural gas priority than larger producers and they appear to be exempt from natural gas curtailments. This legal distinction and the paucity of agreements on the characteristics of a representative meat processor has limited this research to investigating the decision strategies of a hog processing plant subjected to energy supply constraints. A midwestern hog processing plant was anticipating energy supply constraints which were to be imposed by the F.E.A. and F.P.C. Natural gas, used in boilers to produce steam, was scheduled to be incrementally decreased between 1976 and 1980. After 1980, natural gas would no longer be available for use in steam boilers. In addition, a 12 percent energy conservation goal was being imposed by the Federal Energy Administration. Failure to meet this 12 percent energy reduction goal could jeopardize the 27 28 agricultural priority of the hog processing plant and could subsequently result in losing federal assurance of future energy supplies. This research was primarily concerned with identi- fying and evaluating the decision strategies available to the hog processing plant. The nature of the constraints imposed upon the plant, however, were important in the identification of possible strategies. The imposed energy constraints were scheduled to be implemented during the next six years. Consequently, decisions could be imple- mented during different time periods or prior decisions could be reversed in a later time period. In addition, the hog processing plant had a production constraint imposed upon it by the parent firm. Consequently, the decision strategies available to the plant were constrained by time, the parent firm, and energy supplies. An important distinction must be made concerning the analysis of this hog processing plant and the analysis of a firm or an industry. In many respects, the hog pro- cessing plant could be considered a firm, but sufficient differences exist which deserve amplification. First, the plant does not fit the neo-classical definition of a firm and it does not attempt to maximize profits. The plant is not an individual business, and its production goals and output prices were establiShed by the plant's parent firm. Similarly, the plant, per se, can not be con- sidered representive of the industry nor any major segment 29 of the industry. The plant is assumed to operate in a mar- ket situation where its actions will not change the market behavior of other plants, firms, or the industry. Although the energy constraints imposed on the plant are also being imposed on most other plants, the possible effect of sev- eral industry adjustments are not included in this study. It was assumed that the market behavior of competitive plants would remain unchanged. The only industry and national adjustment that affect the plant is the plant's energy price expectations, which subjectively accounts for energy use adjustments. The hog cycle is expected to con- tinue and hog prices are assumed to follow historical patterns. It is true, however, that many meat packing plants will be subjected to both the F.E.A. and F.P.C. energy con- straints. Since this study concentrated on hog processing activities, it is applicable to other meat packing plants which use similar rendering and by-product processing methods. . Firms may utilize this analysis to provide an indication of the impact of modified production goal criteria by assuming the absence of any change in the be- havior of its competitors. Similarly, any conclusions con- cerning industry adjustments must be modified to conform with the assumptions imposed on the plant analysis. Since the analysis assumed that the plant's behavior would not modify the behavior of other plants, then it must also be 3O assumed that the joint behavior of several plants would not be significantly changed. Ignoring this assumption could cause misleading implications about industry behavior. Production theory appeared more germaine to this analysis than the traditional theory of the firm. The energy constraints could be considered and adjustments for the plant could be hypothesized without imposing a profit maximizing goal. In addition, the time factor could be considered in the short-run and long-run as various deci- sion strategies were investigated. The production function of the hog processing plant was described by the neo-classical production func- tion, 0 = F (X1. X2. x3 x1 xr/xt . X") where Q = output X1 = natural gas as a boiler energy source X2 = fuel oil (#5 or #6) X3 . . . X1 = possible energy saving technology currently at a zero level of usage X1 . . . X = other variable factors Xt . . . X = embodies all other fixed factors of produc- tion. 31 Since a sufficiently long time horizon was being considered, the number of variables which were to be considered in- creased from X1 to X2, which were normally considered short- run variables to X3 through Xr. fit the economic defini- The variables X and X 1 2 tion of perfect substitutes when they are used as boiler energy sources. After they are ignited to produce heat, fuel oil and natural gas could be considered as the same commodity. Fuel oil and natural gas are commonly measured in gallons and thousand cubic feet (MCF), respectively. In these units of measurement, about five gallons of fuel oil produce the same quantity of heat (B.T.U.) as one MCF. Since both energy sources produce B.T.U.'s, a cost minimizing plant would prefer to use the input which had the lowest price per B.T.U. if a price differential existed. In this instance, natural gas has a lower cost per B.T.U. than fuel oil. This situation is depicted in Figure 1, where OB quantity of natural gas is purchased and a zero quantity of fuel oil is purchased. The effect of the constraint on natural gas boiler supplies becomes more obvious in the framework of economic theory. From Figure 1, it is clear that the firm will prefer natural gas until fuel oil prices become less expensive relative to natural gas prices. Since fuel oil prices have not changed in this manner, a regulatory con- straint would force the plant to use fuel oil if it wanted to maintain its level of production. Given the price 32 relationship in Figure 1, the plant utilized only natural gas as a boiler energy source. Gallons of Fuel Oil isoproduct - lbs. of steam 0 B MCF FIGURE 1. SUBSTITUTION RELATIONSHIP BETWEEN FUEL OIL AND NATURAL GAS AS BOILER ENERGY SOURCES By imposing a supply constraint of OR, as shown in Figure 2, the plant is forced to use OX quantity of fuel oil and OR quantity of natural gas to maintain the same quantity of steam production as OB units of natural gas would produce. Energy cost increases were shown in line CB, shifting outward to position DE since fuel oil costs per B.T.U. are higher than natural gas costs. It is necessary to note that the isoproduct line in Figure 2 becomes discontinuous at point S. The imposi- tion of the energy constraint (OR) removes line segment SB fr: wi‘ wi‘ Fm fuel Ship Cree “‘9‘ 33 from a previously existing substitution relationship. Even with production decreases, fuel oil usage would increase without increasing total energy costs. FueI 011 ,1 iSOproduct S . isocost X I. \ y/ C \ \ J”! / \ Q \Q\ ‘\ \\ \\ I \\ \ \ \ \ - mcovugonoga .pmu'nxu m? m:_uu:u we uogume mwzu :o:ozu_< "who: mcnxcmh ucu mmmmgo .Lvo: .uoopm .mmcwmmu mcwuspuc~ Amy mocwcwd cumsoum uca m>mcvwx .mmachP .umoz uwpnwu .ummz vcomuoz .uom: uwppau .mxua—a mcwuapu:_ “my muzocm new mavd .mogmz .uomz vow: new xomgu .mcvugm .mpvuh .mmcom xumz .ummu acruapucn n_v Roo.oop EIuHmzm>H4 s< z< tom... .m.: ,3 ad: mm<¢m>< . hzmu mun 46 for shipment in various coolers and freezers. Rendering Activity The rendering activity is divided into three separate sub-groups. These are rendering edible fats, in- edible fats, and blood drying. Edible fats in hogs are the leaf and back fat, clean fatty trimmings from the viscera and fat from the edible cuts of meat.33 The leaf and vis- cera fat are separated in the slaughter process while the back fat and fat from edible meat comes from the cutting floor. "Inedible fat, on the other hand, includes contami- nated trimmings, visceral parts, clean-up scraps, and any other parts declared unfit for food", including hogs con- demned during the inspection processes.3“ Edible fats are cooked in cylindrical vats with a capacity of 20,000 pounds. Each vat is sealed and cooked for three and one-half hours under 60 pounds per square inch of steam pressure. Steam is entered through the bottom of the vat for two and a half hours; but, during the last hour, steam enters only from the top. The procedure, which involves changing the steam entrance point in the vat, causes the contents to settle in layers. Three def- inite layers are formed in the settling process, 1. Prime steam lard 33American Meat Institute, By-Products, p. 19. 3"Ibid. 47 2. An emulsion 3. Tank water. About 65 percent of the raw fat is converted into prime lard which is loaded directly into a railroad tank car. The emulsiOn is stored in an available processing vat for future recooking. The tank water contains about seven per- cent solid matter and is stored for further preparation. About 600 gallons of tank water are derived from the cooking process and eventually is completely evaporated by steam heat. The final dehydration step requires drip- =ping the concentrated liquid onto a stick roller, which is similar in appearance to a stainless steel rolling pin, one foot in diameter. Steam is injected into the roller and the condensed tank water is dripped onto the_roller and dried almost instantly. The resulting residue is scraped off as the roller turns. This residue is processed through a hammer mill, bagged, and sold as a high protein animal feed. The inedible rendering process receives meat and fat scraps from the slaughter and cutting floor on a con- tinuous basis. The raw material is placed in a pre-breaker which shreds and breaks condemned parts, bone, and other scraps prior to entering a storage tank. Material from the tank is combined with steam and pumped into a continuous cooker. The Continuous cooker is a series of horizontal 48 tubes stacked on top of each other. The raw material enters the top tube and is augered to the end where it drops to the next lower tube and is augered back. This process is repeated for about 20 minutes until the material reaches the bottom of the cooker. Approximately 10,000 pounds of material can be contained in the continuous cooker as the raw material passes through the tubes. The cooked matter is augered to a screening and pressing machine, commonly called an expeller or a french press. The inedible grease from the material is separated by draining screens and by the presses. Two products are derived from the continuous cooking process; these are white grease and cracklings. White grease is used in the manufacture of commerical pro- ducts such as soap. Cracklings are generally sold in bulk quantities to feed manufacturers to be utilized in animal feeds. House grease is a product used in feed manufac- turing, primarily as an adhesive in the production of pellets. House grease is derived from skimming the fat and grease off the top of the water storage tank which is filled from the water drains within the plant. The skimmed material is then cooked with steam for four hours before it is readied for sale. Whole blood is cooked approximately six hours by means of a dry cook method. The blood is piped into a jacketed container. Live steam is injected between the 49 jacket and the container to provide the heat source which evaporates the whole blood. Dried blood is used as a high protein feed ingredient and/or as an organic fertilizer. Clean-Up Activity The clean-up activity is directed toward the entire plant and is not separated according to production activities. Ten employees are involved in this facet alone and 43,000 gallons of 170°F water are utilized to clean the plant. The hog production processes are shown in Figure 7. Description of Energy Utilization In A Hog_Processing Plant Energy utilization within a meat packing plant can vary considerably. This variation has been attributed to the type of production process within the plant and the type of livestock processed. Past research35 has shown that hog processing requires about twice as much energy as beef processing when both types of livestock are slaugh- tered in plants with by-product processes capabilities. Energy consumption also varies considerably between plants which process the same type of livestock but produce a different combination of products. Beef processors, which produce boxed meat, have twice the energy demand per pound 35Foster 0. Snell, Inc., Energy Conservation in The Meat Packing Industry, Federal Energy Administration Contract No. C-O4-50090-OO, January 30, 1975. 50 LIVE HOG Bleed Scald ' Oehair ' Singe ' Waste and viscerate Offal and Direct Sale . ' Chitterl ings Blood Dried Drying Blood Inedible Rendering . Edible Dressed Pr1me . . Crax Lard Rendering Carcass , White Grease ' Carcass Res1due ' ' Chilling aging Shrink I Department Feed . Carcass Cutting Loins Hams Butts Picnics Bellies Ribs Other FIGURE 7. FLOW CHART 0F HOG PRODUCTION PROCESS 51 of liveweight as beef processors which slaughter and pro- cess by-products only. Similarly, pork processing shows a wide range of energy requirements. As interest in energy utilization increased, engineers began to concentrate on energy use within the meat processing plant. Johns-Mansville Corporation36 studied a medium-sized (300 hogs per hour) hog slaughtering and cutting Operation in Iowa. Total energy use was as- certained by months and quantified by linear regression. They estimated total energy as a function of production; the resulting equation was: Y = 10,419 + .6975X where Y = million B.T.U. used per month X = 1,000 pounds of production per month Standard error for Y was 12,363. Although energy use within the plant was not correlated with various energy sources, they did ascertain that about 70 percent of the total plant energy demand was derived from the steam boiler requirements. At the mean, approxi- mately 1,000 B.T.U. would be required per pound of live hog processed. 36Johns-Mansville, Inc., "How Pork Plant Rated On Energy", National Provisioner, Jan. 31, 1976, p. 30. 52 In addition, the Johns-Mansville researchers esti- mated that only three-fourths of the boiler steam was uti- lized in the production processes. The remaining 25-30 per- cent of the steam heat was lost or used for space heating, vacuum pumps, and etc. This research is concerned with the derived demand for energy from each hog processing activity. Energy re- ducing alternatives are being proposed for Specific pro- duction processes. Research, however, has lagged behind' the profusion of proposed energy saving technology, and information regarding energy use versus energy reduction is not available. The current state of knowledge was limited to the Johns-Mansville study which showed that about 50 percent (70% * 75%) of the hog processing plant energy use was actually used in the production processes, per se. The Johns-Mansville research effort was intended to be extended by this research by the addition of esti- mates for the derived energy demand for the following pro- duction activities: 1. Hog scalding Hog dehairing Inedible rendering Edible rendering Blood drying Clean-up VOW-5mm Refrigeration. 53 Estimates of energy utilization were obtained from coopera- ting engineers with the hog processing firm. Other pro- cessing estimates were obtained by metering production activities and utilizing standard engineering tachniques. Energy consumption was ascertained by energy source and for varying levels of plant production. Hog Scalding Energy Demands Approximately 13,000 gallons of water are heated to 148 degrees Fahrenheit and maintained at that tempera- ture throughout the work day. Each gallon of water re- quires 666 B.T.U. to raise its temperature to 148°. This energy estimate is derived from standard engineering tables which show that 79.81 B.T.U.'s are required to heat one pound of water.37 Sixty degree Fahrenheit water is used to fill the scald tank and each gallon of water weighs approximately 8.345 pounds. In addition, the plant engi- neers have ascertained that approximately 1,000 pounds of steam per hour are required to maintain the scald tank temperature at 148 degrees throughout the work day. As- suming boiler efficiency is 80 percent and a five percent steamline loss occurs, 1,413,170 B.T.U. are required from the boiler energy source for every scald tank hour of 37Lionel S. Marks and Harvey D. Davis, Tables and Diagrams of The Thermal Pr0perties of Saturated and Super- Satugated Steam,_TLongsmans, Green and Company, 1920), pp. -10. 54 Operation. (This estimate includes 500 B.T.U. per square foot for surface water evaporation.) The estimated B.T.U.'s of boiler energy for the scald tank is: B.T.U. 1,413,170X + 876.3X 1 2 where X1 number of hours scald tank is Operated X2 gallons of water in scald tank. Hog Dehairing Energy Demands The sequence of dehair machines utilized both electricity and boiler steam. Electricity is the main power source for the equipment and steam is used to heat water which washes the hogs and floats the bristles out of the machines. Two machines require 4,000 pounds Of steam per hour to heat the wash water and 750 pounds Of steam per hour to maintain the desired temperature. Assuming a five percent steamline loss and an 80 percent boiler efficiency rate, 7,187,500 B.T.U. per hour of operations are required at the boiler to properly Operate the dehair machines. Inedible Rendering Energy Demands The inedible rendering process is a continuous operation which utilizes steam in a wet cooking process. The steam is injected into the equipment and directly con- tacts the raw material. Based on plant engineer estimates, 55 1.125 pounds of steam are required to render each pound of raw product. After including the steam line and boiler losses, approximately 1,293.75 B.T.U. are needed at the boiler for each pound of product processed. Edible Rendering Energy Demands The edible rendering process employs large conical vats which are sealed to utilize pressure as well as heat in the cooking process. This plant has eight vats for ren- dering edible lard. Two of these vats are used for fat from the slaughter floor, five are used for fat from the cutting floor, and one is used to render the emulsion which is a by-product Of previous edible rendering activities. Ap- proximately 3,700 pounds of steam must be injected directly into the vat to complete the three and a half hour render- ing process. In addition, approximately 600 gallons of water must be evaporated to obtain the high protein animal feed from the vat tank water. The combined boiler energy requirement of the rendering process plus steamline and boiler loss were computed to be 5,598,700 B.T.U. by the plant engineers. The boiler energy required to evaporate 600 gallons of water is 1,112 B.T.U.38 per pound plus line and boiler losses, or 7,326,000 B.T.U. for each edible rendering vat. 38Marks and Davis, 0p.cit. 56 Blood Drying Energy Demands The blood drying process utilized a dry cooking method. Steam is injected between the container and a surrounding metal jacket. Plant engineers attached meter- ing devices to measure the total steam required per cooking unit. Engineers determined that steam required to evapo- rate the water was almost doubled due to heat radiation losses and steam condensation. After accounting for steam line loss (5%) and boiler efficiency (80%), it was found that 2,714 B.T.U. were needed to evaporate one pound of whole blood. Clean-Ug7Energy,Demands The clean-up Operation required 43,000 gallons of water heated to 55 degrees to 170 degrees.' Utilizing stan- dard steam tables, approximately 115 B.T.U. were required to raise one pound of water from 55 degrees to 170 degrees. Allowing for the assumed steam line and boiler efficiency, 51,582,000 B.T.U. were required for each daily clean-up Operation. One dry cooking unit was used daily to render house grease. Based Upon the metering test conducted by plant engineers, 7,127,000 B.T.U. were required at the boiler for this operation. Refrigeration Energy Demands The refrigeration demand at the plant is currently supplied by natural gas, electricity, and propane. 57 PrOpane is used as an alternative source only when natural gas is in short supply. Normally, about 40 percent of the energy demand is provided by natural gas and the remainder is provided by electricity. Refrigeration energy demand are highly plant specific. Insulation and cool storage location relative to heat areas greatly affects refrigera- tion energy demands. Also, the quantity Of meat processed and the time of year affect refrigeration requirements. Energy demands for six cooling areas are estimated based upon the plant conditions and the quantity of hogs pro- cessed. These are: the hog chill cooler, the hog cutting floor, the loin cooler, the fresh meat cooler, the offal freezer, and the shipping area. The shipping area energy demands were estimated with three equations which accounted for cooling losses associated with weather. The daily energy demand for the hog processing plant is shown in Table 1. This table was derived from the case study plant's energy equations, and it was assumed that 3,300 hogs would be processed in one eight-hour day. 58 TABLE 1. DAILY ENERGY DEMANDS FOR SELECTED HOG PROCESSING ACTIVITIES Hog Processing Activity Daily Energy Consumption (1,000 B.T.U.) Scalding 22,617 Dehairing 57,500 Inedible Rendering 139,087 Edible Rendering 77,530 Blood Drying 77,186 Clean-Up 58,710 Refrigeration and Freezing 171,600 Total energy use within the hog processing plant was estimated. Energy demands for specific energy sources and major production activities were also estimated. Energy estimated by activity and source will be simulated as production varies over the time horizon. The simulation model will be primarily concerned with the change in total energy demand to meet energy conservation goals, the fea- sibility of utilizing specific production alternatives as prices of energy sources change and as energy supplies are restricted. CHAPTER V REVENUE AND COST ESTIMATION This research is primarily concerned with the com- bined effect of a total energy constraint, a natural gas (supply constraint, and rising energy prices on the earnings of a hog processing plant. Revenue and costs are estimated to determine the combined effect of the energy and price changes on the plant's earnings before taxes. All cost components are related to output level changes but not all are specifically delineated. Energy supplies, energy prices, hog supplies, hog prices, product prices, and labor prices are estimated separately from other plant inputs. Total revenue is defined as the price (Pq) per unit of output times the number of units sold (0), i.e. R = PqQ. The total revenue derived from a hog processing plant, however, is dependent upon several products, by- products, and intermediate products. Each of these have associated prices that vary according to the demand and supply relationships prevailing at any particular point in time. Consequently, total revenue (R) would, of necessity, be defined as: 59 60 where J = number of different products sold Q j = 2 q i=1 3 Revenue Estimation Prices of the several products sold by a hog pro- cessing plant vary as the demand-supply relationships re- Spond to individual market conditions. Each product's price has its own particular substitutes, complements, and price changes. The hog slaughter market does, however, in- fluence all of the various hog product supply situations. The combined impact of these several product prices is reported monthly by the U.S.D.A.39 This data series reports the wholesale value of the carcass and by- products per 100 pounds of liveweight and the average price per 100 pounds for various slaughter hog categories. Revenue for the plant was estimated by utilizing the U.S.D.A. hog price and quantity data series. Total re- venue was not estimated. Margin revenue was estimated and is defined as the difference in wholesale value and slaugh- ter value per 100 pounds of liveweight. Linear regression was utilized to estimate the value of live hogs and the 39U.S. Department of Agriculture, Livestock and Meat Statistics, "Pork: Live Animals and WholesaTe Prices Wholesale and Retail Values", Table 174, Statistical Bulletin No. 522, ERS, SRS, AMS, (1974), p. 283. 61 wholesale value per 100 pounds of liveweight as the slaugh- ter hog market conditions change over time. Wholesale Value of Hogs Research on price spreads have been directed in several directions. Learn"o concluded that merchants tend to maintain constant percentage markups of livestock pro- ducts and, consequently as production rises, the absolute difference in prices will decrease as livestock prices fall. Hayenga‘+1 utilized linear regression to determine the correlation between the value of hogs and their whole- sale value as the liveweight of the hog varied. Combining their research conclusions, the total quantity of hogs available for slaughter appears to have more effect on the margin between liveweight value and wholesale value than the weight Of the hog. -The wholesale value of hogs has been observed by industry members to fluctuate as the liveweight value of hogs adjuSts to market conditions. This relationship was quantified by linear regression to estimate the expected margin revenue as the quantity of slaughter hogs changed over time. The ratio of wholesale value to liveweight ”OLearn, Elmer W., "Estimating Demand for Live- stock Products at The Farm Level", Journal of Farm Economics, (1956), Vol. 38, pp. 1483-1491. FlHayenga, Marvin, An Evaluation of Hgg Pricing and Grading Methods, Agricultural Economics Report 192, Michigan State University, Department of Agricultural ECOnomics, (May 1971). 62 value (W/S) was selected as the dependent variable, and the independent variable was the quantity of commercially slaughtered hogs (H). The monthly data (1968-1973) was found to be serially correlated and the Cochrane-Orcutt method for estimating regression equations with auto- regressive disturbances was utilized to obtain the re- gression coefficients.“2 The estimated relationship was: 2 W/S = .0425 + .0013H R = .85 (.004) (.00001) where W/S = ratio of monthly wholesale value per 100 pounds of liveweight to monthly liveweight value per 100 pounds H = thousands of monthly U.S. commercially slaughtered hogs Durbin Watson (D.W.) = 1.43 (XX) = standard error of coefficient. The wholesale value of hogs per 100 pounds of live- weight was predicted with a relatively high degree of con- fidence whenever the slaughter price per hundred pounds and the quantity of hogs available for commercial slaughter were know. l'ZKmenta, Jan, Elements Of Econometrics, (Mac- Millan Company, New York, 1971). p. 287. 63 _1veweight Value of Hggs The liveweight value or slaughter hog market price and pork prices have been estimated by economists with widely varying approaches and results. Part, but not all, of this diversity stems from the variance in research ob- jectives. Estimates of hog demand elasticities have been made on an annual basis, monthly, weekly, and by day of the week. These estimates range from -O.46 to -5.8.'*3 Annual estimates of the demand elasticity of hogs range from -O.46““ to -2.75.'*5 One monthly estimate of price flex- ibility was -l.6“5 (at the means) while the weekly and weekday estimates were below -2.5. Hayenga and Hacklander estimated the monthly price of live hogs.“7 Their linear regression model ac- counted for approximately 97 percent of the monthly varia- tion in hog price. Hog price was estimated as a function “3Shepherd, Geoffrey S., Agricultural Price Analy- sis, (Iowa State Univ., Ames, IA, 1964), 5th ed., pp. 63-64. ““Brandow, G. E., ”Interrelations Among Demands for Farm Products and Implications for Control of Market Supply", (Pennsylvania State University, Agricultural Ex- periment Station , Bulletin 680, 1961). ”SOean, Gerald W., and Heady, Earl 0., "Changes in Supply Response and Elasticity for Hogs", Journal of Farm Economics, Vol. 40, (1958), p. 539. “SHayenga, Marvin L., and Hacklander, Duane, "Monthly Supply-Demand Relationships for Federal Cattle and Hogs", American Journal of Agricultural Economics, (Nov.. 1970), Vol. 52, No. 4, p. 539. l'7Ibid. 64 of hog slaughter, beef slaughter, stored hog supplies, changes in pork supplies, per capita income, and monthly binary variables. The hog and beef slaughter variables are based upon average slaughter per day to eliminate monthly variations as days per month differ. The Hayenga and Hacklander model was re-estimated over a different and more current four-year time horizon. The resulting equation explained 95 percent of the price variation. .The expected negative sign on the beef variable was substantiated. Many of the coefficients were nearly identical to the earlier model. . The disadvantage of using the Hayenga-Hacklander model is that five variables would have to be predicted to estimate hog prices in future time periods. To reduce the number of variables which would need to be predicted, the pork storage variable and the change in pork storage vari- ables were eliminated from the Hayenga-Hacklander model. Hog prices were estimated by ordinary least squares with five independent variables, hog slaughter, beef slaughter, per capita income, and monthly binary variables. Approximately 90 percent of the monthly price variation could be accounted for over the 1969-1973 time period. This model loses little explanatory power and re- duces the number of variables. The daily beef slaughter variable has a mean of 133.6 million pounds and a standard deviation of only 7.15. Consequently, the monthly mean of daily beef slaughter was used, and only per capita 65 income and daily hog slaughter estimates for future time periods were necessary. selected was: S = 35.10 - .66X + .16X - .11X The linear regression model - .09M 1 2 3 1 (17.73) (.11) (.021) (.10) (2.5) - .30M2 - 2.92M3 - 3.04M4 - 4.83M5 (2.70) (2.92) (2.60) (2.60) - 6.29M6 + 3.25M7 - 0.52M8 + 1.15M9 (2.60) (2.75) (2.57) (2.64) + 1.63M10 - 1.21Mll (2.63) (2.79) R2 = .89 X1 = million pounds of pork slaughtered per month divided by the number of work days per month"8 X2 = monthly U.S. per capita income X3 = million pounds of beef slaughtered per month divided by the number of work days per month M1 .M11 = monthly binary variable (Feb. = 1...Dec. =11) (xx) = standard error of coefficients “3Work days are computed as: = 1 day; week days holiday = holiday 1/3 daY- week day, no holiday 1/2 day; Saturday or Sunday 66 D.W. = .98 Per capita monthly income was estimated by linear regression over time. The resulting estimate was: 2 PCI = 445.7 + 3.05t R = .98 (15.8) (.37u) where PCI = monthly personal income divided by the U.S. monthly population"9 t = number of the months over the time horizon t = 1 on 1/1/73 standard error of coefficient (XX) D.W. = 2.48 Commercial Hgg Sgpply Estimation Hog supplies were estimated for future time periods by assuming that the repetitive nature of hog cycles would continue. Two separate studies concluded that a hog supply cycle existed and that the cycle is not solely dependent on corn production or prices. The applicability of the “9U.S. Department of Commerce, Survey of Current Business, 1969-1973. . 67 "Cobweb Theorem" was investigated by Dean and HeadySO in 1958. Shepherd also observed the hog cycle's occurrence even though corn prices were relatively stable.51 In many agricultural situations, producers adjust their outputs to price changes, but the change is not re- flected instanteously in the market place. Consequently, supply response will be lagged and the market price in future time periods will reflect past decisions. Dean and Heady investigated the lagged supply response Of hog pro- ducers and concluded: Three conditions are required for the Cob- .web Theorem to explain the functioning of a commodity market; (a) producers plan in period t for output in period t+1 on the basis of prices in period t; (b) production plans once made, cannot be changed until the following time period; (c) price must be determined by the quantity sold (i.e., by interaction of a conventional demand function and a vertical supply function). The production demand and supply structure for hogs approximate these conditions. With regard to condition (a), limited re- search evidence points to "extension of current prices" as a dominant expectation model used by producers. Condition (b) is approximately met since once sows are bred, relatively little can be done to increase or decrease future production. Condition (c) implies no simultaneity between price and quantity within the soDean, "Changes in Supply Response and Elasticity for Hogs", pp. 845-860. 51Shepherd, Agricultural Price Analysis, p. 40.- 68 marketing period, i.e. quantity is assumed to be predetermined.52 In addition to the observations of Dean and Heady, Shepherd reported that: Evidence in recent years, however, indicates that the four-year hog production and price cycles are inherent in the internal condi- tions of the hog industry and do not re- quire shocks from outside to keep them going. After 1952, the stabilization operations of the CCC were conducted on so large a scale that they almost completely damped down year to year variations in corn prices. Yet hog production and prices continue their four-year cyclic movement much the same as before.53 Larson5‘+ and Talpaz55 both attempted to quantify the hog cycle. Larson utilized trigonometric functions to approximate the hog-corn price ratio, pork productions, and sow farrowing. Talpaz combined the work of many re- searchers and "statistically tested and accepted the exis- tence of the combined series Of cycles operating 52Dean, "Changes in Supply Response and Elasticity for Hogs", p. 846. 53Shepherd, Agricultural Price Analysis, p. 40. 5“Larson, Arnold B., "The Hog Cycle As Harmonic Motion", Journal of Farm Economics, (1964), Vol. 46. 55Talpaz, Hovav, "Simulation, Decomposition, and Control of a Multi-Frequency Dynamic System: The United States Hog Production Cycle", (unpublished Ph.D. Disserta- tion, Michigan State University, 1973). 69 simultaneously".56 He found that not only did a four-year sow farrowing cycle exist, but that five smaller cycles also existed which were 2, 1.25, 1, .5, and .3-year cycles. Talpaz predicted the sow farrowing via regression analysis with 94 percent Of the variation in the dependent variable being explained. Based on the past research and particularly Talpaz's work, there appears to be little doubt that a hog cycle does exist and that it can be quantified. Other economic factors do play a role on the impact of producer decisions and consequently one could hypothesize that the cycle could be disturbed or changed by any one of these factors. The combined time span of these past cyclical studies, however, encompasses 1947 through 1971 and the hog cycle has continued to exist. The method employed by Talpaz was used to estimate the future monthly supply of U.S. slaughter hogs. A Fourier Series of the form: X(t) = E=O An Cos(nut) + bn Sin(nut) + e) where X(t) = monthly hog slaughter in month t T = the number of terms in the series 56Ibid., p. 64. 70 U = 2h/48; radian frequency for a 48 period cycle t = time period, 0-48 e = error term n = selected integer values between 1.0 and 18. was appropriate for the Talpaz sow farrowing estimation and was also used in this research to estimate the monthly hog slaughter. Utilizing the same Step-wise Delete Routine57 as Talpaz, the coefficients on the cosine and sine variables with an F-test, significance level of five percent was estimated. The resulting estimated equation was: Qt = 5.900 + 592.2x1 - 239.2x2 - 144.7x3 (120.0) (130.6) (69.7) (165.7) + 71.6X4 + 195.7X5 + 484.7X6 - 249X7 (131.u) (57.7) (167.3) (157.4) R2 = .64 D.W. = 2.08 (xx) = standard error of coefficients 57Ruble, W. L., "Improving the Computation of Si- multaneous Stockastic Linear Equations Estimates", Agri- cultural Economics Report No. 116 and Econometrics Special Report No. 1, Department of Agricultural Economics, Michigan State University, E. Lansing, MI, October 1968. 71 where Qt = quantity of commercially slaughtered hogs (in thousands) in month t X = Cos (ht/6) X = Cos (ht/3) X = Sin (ht/24) X = Sin (ht/6) X = Sin (2nt/3) X = Cos (ht/24) >< ll 7 C05 (wt/12) The explanatory power of the hog slaughter equa- tion is much lower than Talpaz's equation of sow farrowing. A reduction in explanatory capabilities was expected since producers have the ability to adjust hog marketings and slaughter via production practices. These estimates of hog slaughter and live hog prices were made inorder to estimate the wholesale value of hog products. Margin revenue for the plant was deter- mined by a set of linear regression equations which esti- mated monthly hog slaughter and monthly per capita income to predicted live hog prices which, in turn, was used to estimate wholesale hog values. The margin revenue was 72 defined as the price difference in liveweight value and wholesale value per hundredweight of live hog times the quantity of live hogs (hundredweight) slaughtered by the firm. Cost Estimation The cost associated with processing hogs depend upon the production level and the price of slaughter hogs. The particular production costs of a firm are generally re- garded as confidential and released only after certain safeguards have been met. Two potential sources of production cost data were investigated. These were the "Hog Cut-Out Values and Mar- gins" published by Madigan-Abraham Associates, Inc.58 and a "Cost Components" study conducted for the Economic Re- search Service, U.S.D.A. by Food Management, Inc.59 Both sources provided labor and overhead costs, but neither re- lated cost changes to output level fluctuations. The COOperating firm was approached to provide the necessary information which related output level changes to cost changes. Understandably, the firm did not want the actual costs of production made public. An acceptable 58Madigan-Abraham Associates, Inc., Hog Cut-Out Values and Margins, 1627 Whitfield Avenue, Sarasota, Florida 33580. 59Food Management, Inc., Cost Components-Cattle and Hog Slau hter Plants, ERS, USDA Contract No. 12-17-02-5-943 October 974. 73 compromise which combined the "Cost Components" data and the plant's production cost resulted. The "COst Components" report contained a survey of midwestern hog slaughtering plants which had an hourly- rated slaughter capacity between 390 and 480 head. The cost data for these four plants were combined by using a simple average of the average cost per head for the various cost components. These cost components are shown in Table 2 for a plant with a 400 hog, hourly-rated capacity, and a nine-hour production work day. The average processing cost per hog for the four firms was 4.77¢ per pound of live hog processed. The associated output level with an average production capacity of 445 hogs per hour is approximately 18 million pounds of liveweight per month. The case study plant provided twelve months of data which reflected the average cost of production over the production range of 11 to 22 million pounds per month. The data were plotted and visually inspected to ascertain an apprOpriate functional form. The data showed no evi- dence of a curvilineat relationship and linear regression was selected as an apprOpriate estimation method. The resulting equation Of the form *= - Y K0 OM IbS. explained 58 percent of the variation in Y* where 74 TABLE 2. DAILY HOG PROCESSING COSTS - SLAUGHTER, CUT, RENDER, AND CLEAN-UP - 1974 Variable Direct Labor $ 6,540 Supplies 4,032 Utilities 1,332 Sanitation Labor 1,172 Repair Labor 972 Other (transport buy, sell, etc.) 13,536 $30,584 Fixed Administration 3,100 Meat Inspection 72 Other 4,908 Depreciation 1,556 Interest 1,007 $10,643 TOTAL DAILY COST . $41,227 Average Cost Per Hog $11.45 Average Processing Cost Per Hog $ 9.91 (Average Cost Less Depreciation and Interest ($0.0477/lb. Liveweight) 75 M lbs. million pounds of hogs slaughtered K constants 0’“ Serial correlation was not present. The firm's average cost curve over the given pro- duction range was shifted vertically by adding an amount 6 to the constant K0 where 6 was allowed to be positive or negative. This adjustment of the intercept forced the average processing cost curve of the firm through the co- ordinates of the average processing cost of the surveyed midwestern hog processing plant. The resulting equation which depicts the average cost of processing hogs over the given production range is: Y = .0837 - .002M lbs. (.0076) (.0005) where Y = average cost per pound of liveweight slaughtered in dollars M lbs. = million'pounds Of hogs slaughtered per month (xx) = standard error of coefficient D.W. = 1.87 Adjustments in specific cost components were made to account for changes in resource use and expected price 76 increases. This adjustment procedure required monitoring- the use level of specific inputs in the production process. When price changes occurred, the new price was multiplied by the quantity of input used and added to the production cost equation while the Old price multiplied by the quantity of input used was removed from the cost equation. Labor usage, fuel oil, and natural gas were three inputs which were specifically monitored. The quantity of labor, fuel Oil, and natural gas were allowed to adjust with production level changes. Monthly labor costs were adjusted according to inflationary expectations of the firm and production hours worked. The labor union contract called for a guaranteed minimum weekly pay for 36 hours and time and a half for overtime. If the firm paid for more hours than were actually worked in a given month, due to the minimum wage provision, the firm could utilize those hours in the following month without any additional cost. These provisions of the labor union contract were utilized to adjust labor costs when necessary. Energy costs were adjusted according to production fluctuations and expectations of the firm regarding price increases and energy constraints. A five percent annual increase in energy prices were also considered to Obtain a measure of the simulation model's sensitivity to energy prices. These expectations are shown in Chapter VI, in the section entitled Expectation Component of The Simula- tion Model. 77 Margin revenue for the firm was estimated by pre- dicting the price difference between wholesale value and liveweight value and multiplying by the units of production. Production costs for the firm were estimated from firm data and adjusted to cost data provided from hog processing plants with similar characteristics to protect the confi- dentially of the firm's data. Net earnings to the firm are the difference between margin revenue and production costs which include processing costs plus fixed costs. 78 CHAPTER VI SIMULATION MODEL The simulation model was designed to investigate the decision strategies of,a hog processing plant over a six-year time horizon. The decision strategies available for consideration were limited by energy supply constraints which were to be implemented during specific time intervals. Consequently, decision strategies were also time related. Annual decisions were made in regards to changes in the firm's production functions and shifts in the function were allowed as the level of capital embodied in technology was adjusted. Monthly decisions determined the level of pro- duction for the plant. The objective of this research was to determine the effect of plant earnings and energy flows as various strategies were considered.1 Three basic strategies were considered: (1) change production levels, (2) adopt energy reducing technology, and (3) change output composition. Monthly production could be reduced to meet energy con- straints kept at the normal level or adjusted to maximize profit. Energy reductions due to technology changes oc- curred as alternatives with positive net present value were adopted. Energy consumption, earnings technology 79 adjustments, and total production were reported monthly over the six-year time horizon. The effect of the natural gas constraint and the energy conservation goal on earnings was measured by com- paring three modifications of the simulation model. These models differed in two respects, either the decision cri- teria was modified or the energy constraints were considered jointly and separately. Variation of the constraints and decision rules were separated into three groups, called Models A, B, or C. Model A simulated the energy and financial changes due to rising energy prices and standard plant management strat- egies. Model B differed from Model A by the addition of two energy constraints. A 12 percent energy conservation goal for 1982 and natural gas supply reductions for the period of 1976 to 1980 were imposed. Model B was considered the most likely estimate of the hog processing plant situa- tion. Model C eliminates the firm's strategy used in Model B and replaced it with a profit maximizing strategy. Model A was designed to show the impact of rising energy prices. Model B was designed to approximate the situation and re- action Of the hog processing plant. Model C was designed to provide a comparison of theoretically prescribed be- havior with the behavior of the firm. Table 3 illustrates the difference between these models. mapm> pcwmmgn pm: m>wppmoa m new: mm>mumcgmppm m we msmm .N cowuuauosn pumpmm .N umou page» Fm>mp pzapao mcwscmumv new wacm>wg page“ we pomsucou gonmp can msmsm mucmemmwwu Ezewxmz .H < mm mamm .H umxsmeioac magnum m.ngm .fi 6 eeo-u=;m 0mm“ an eeo-e=;m mac «inuaasgmucu .m mum mpnwunzecmucfi .e Nwmfi an Feom «mmfi an pmom cow» cowuoaums xagwcm Nu“ .N -uzuwg magmcm xmfi .m mmmu x203 « coo.me 6» nepeee_ 1 mac; ooo.om servosuosa z—gucoz .H < we 05mm .N v cowuoauoga a—zpcoz .N I I moo; ooo.~e < he esem .H x eoepezeoee apepeos .fi mez2mm augmcm mo msgmgh ” amou szcc< " u:mEpmm>=H H m>ppmcgmup< covuuzvogm Ammo: zomh<422Hm wznmmmuoma.wo: mxh zH cmamaumzou mm>mhommzu .m m4mog “aquao mucosa mcowuouuwaxm women xogmcm sued mammogucu woven augmcm Eacc< so; am eeeeeeeu eoeueNCEExe: “Steed owsmuwgu copmpumo Esp; .—F .o— ucwugoa om u:on< mucvcgom amomgucm macpcgmu ca «muogucu up mcwcgou ca mmog mocwcgmm :H mac; mmcpcguu =~ mmmmgucm ooo.oop mocpcgnm cu mmmmguc~ ppusm Eacc< Ema mucwcgmm cu cowpprz ~w «no; «r 00””. V /’ .m mmawnu 104 zpmaomcmupse_m umpnou< mm poz upzoz ucm m>wmzpuxu appmauaz mg< mm>wpmcgmup< o3» mmmgp « umm.- HoH.mw uoopm m—ocz Fpmm r mmo.~ mo~.mH m:_Fmawa mcwgmucmm mucosa NNH.¢K - oom.oH me_»eo neopm om=e_epeou , mmu.mm¢- mmo.mnmi Empmzm mcwsmucmm wezumsmqem» 3o; oee.efl ‘ mmo.me somepm mo: 1- gmmcmcuxm “mm: om~.omm www.0mm cowuaEm—omm , pmm:.swxoou maoacvucou omu.m » Nam.mm w smacwucou man» one FFmsm maven amemcu cw mummcocH poacc< ucmucma m>wd < chE=mm< cowumuowaxm Ours; zmsmcm m.se_u co ummmm m=~m> acmmmsa umz Uwswfiwmcou m>wHMCLmu ~< onmfl 11 >o040zzumh quuaomm >wmmzm mo m=4<> hzmmmmm hmz .m m4m< Hm cm as mu nu mum" u m moxmh mgommm mucwcsmm gwpcmzmpm no: cw mummgucu mmm2o>< Hm om ms mu an cum“ 1 m gmunmmopm mo: Foacc< cowuuaumm augmzm mmmgm>< an mum“ i ¢ umsamcouiwmecu we msgush Amcw~ve+xoe aveoenv u Ammo: Aosouuzo xpmxvp umosv m Juno: < Ammo: Amucpmgumcou masocm ocv Juno: onhmp cowpuznogn cw mmmcmgu op man use weep mm: maemcm cm acopm mucme 1m>oz .u use .m .< mpmuoz mo mmcwp mm: zmgwcw on» acopm umocmgem appmwpcmzcmm go: men mmcwcgmm szccm .a—ucmacmmcou mo“ weaned oucm umumsoasouce no: mo: copmcmswc we?» ugh « onhazamzou >ommzm oz< moz~zm~hc2mcm RNH 011115\\\\n m. lim.~ or e < Foes: Arc .m appmscc< umm: maemch cowpp_z 108 any of the six time periods considered. The centrifuge blood dryer had a positive net present value but the mutually exclusive alternative of selling whole blood was a more attractive option. The first energy reducing tech- nology to be adopted was the continuous cooker heat reclam- ation. Two levels of expected energy prices were con- sidered. The case study plant expected a 300 percent in- crease in the price of interruptible natural gas and a 100 percent increase in the price of fuel oil. An alter- native set of energy price expectations per annum for both energy sources was also considered. The energy reducing technologies which would be adopted under both sets of price expectations were identical in regards to technolo- gies selected and time periods adopted. Table 9 reports the investment and energy savings of the adopted energy reducing technologies. Changes in energy consumption occurred when the interruptible natural gas supply constraints and the energy conservation goals were imposed. Rising inter- ruptible natural gas prices, alone, did not cause a shift to fuel oil. The case study plant relied upon natural gas to provide nearly 60 percent of its energy require- ments over the six year time period when energy constraints were applied, natural gas consumption decreased to only l5 percent of the plant's energy demand and fuel oil con- sumption rose from 30 percent to 75 percent of the plant's 109 ~.NH 0.0H 0.00 N.mH ~.NH 0.0H 000.0 m mcwpmawa mcwemucmm mmcmcu 0.00 0.00 0.00 0.00 0.0m 0.N0 000.0H 0 200000 00: com cmmcmcuxu pom: 0.Nn 0.0K 0.H0 0.05 0.NN 0.0m 00H.0m a Lomcmucoo mask 0:0 __m:m “.00 H.N0 0.00 fi.n0 n.00 H.N0 000.0N 0 mcwsmvcmm wpnwumcH sock cowumsmpumm 0.HOH 0.HHH 0.0HH 0.00H 0.HOH 0.HHH 000.00H0 000220 000.0 mmzmwgucmo H.00 0.00 0.00 H.Hm H.00 .0.0 000.0 0 000—0 0.002 Fpmm “002000 eo.mELm:h 000.Hv " mgmppoo H taxmuwmcou ammfi . 000a 050a wmmfi ~50“ " pcmsumm>cH " Amopoczumk 0:0 mucmeumznn< .mmmfi hz< >00402:0m# 20mm mwzH>wmmzm ouh m i 4 cowumszmcou Pro szu m m 1 4 cowpqe=mcou mac Possumz AacPNPEmeE amwosav u Ammo: Amsouuzo zpwxwp umoev m Amooz Amucwmgpmcou >0emcm 000 < Juno: " >wmmzm no mzmmzh H00H10N0H .hz<40 oszmmuomm we: >00hm mm II 10 - 11 7 12 - l3 - 14 - pounds of pounds Of pounds of pounds Of process pounds of pounds of process pounds of rendering pounds Of rendering pounds of pounds of pounds of pounds of pounds of pounds of raw blood dried blood hog carcass to cut matter available for the edible rendering prime steam lard matter available for the inedible rendering animal feed derived from the inedible process white grease derived from the inedible process loins produced hams produced butts produced picnic hams produced pork bellies produced pork ribs produced pounds pounds pounds number 134 of other pork products produced of house grease produced of live hog slaughtered in time period t of hogs slaughtered in time period t APPENDIX B ENERGY DEMAND COMPONENT OF THE SIMULATION MODEL TS = 113.919 + 15.1317X2 T0 = 71.875X2‘ TB = .00080606X5 TE = 129.217X3 TI = 0.0129375X4 T6 = 30X2 TC = 605.8x6 TH = 21.27X6 TR = 1450 + 0.4632X7 + 147.75X8X6 + 14.39 (X6 + 4) + X9 TT = 104,190 + 0.6975X1 where TS = therms Of energy required to scald hog carcasses in time period t T0 = therms Of energy required by the dehairing activity T8 = therms of energy required to dry whole blood 135 TE TI TG TC TH TR TT therms of activity .therms of activity therms therms period of of t 136 energy required energy required energy required energy required therms of energy required time period t therms of energy required period t for the edible rendering for the inedible rendering to singe hogs to clean up plant in time to process house grease in for refrigeration in time total therms of energy demanded by the plant 100 pounds of products sold by the plant hours of production time in time period considered number pounds pounds number number of of of Of of edible cooking vats required matter processed = A6 in Appendix A whole blood processed days plant is operated in time period t hogs slaughtered in time period t hours of cooling time required which is the hours of production plus four hours 137 437 + 2.1x2 if time period is 1, 2, 3 (January, February, March) 688.8 + 7.9x2 if time period is 7, 8, 9 (July, August, September) 546 + 5.0x2 if time period is 4, 5, 6, 10, 11, 12 138 000.00 000.0 0H0.0 000.0 00H.0 000.0 000.0 000.0 000.0 000.0 000.0 000.0 H00.0 NO0H 000.00 000.0 000.0 000.0 000.0 H00.0 000.0 000.0 000.0 000.0 000.0 00H.0 000.0 H000 000.00 000.0 000.0 000.0 000.0 000.0 000.0 0H0.0 000.0 00H.0 0H0.0 0H0.0 000.0 000a 000.00 000.0 H00.0 000.0 000.0 000.0 0H0.0 000.0 0H0.0 000.0 000.0 000.0 000.0 000“ 000.00 000.0 0H0.0 000.0 00H.0 000.0 000.0 000.0 000.0 000.0 000.0 000.0 H00.0 000a 000.00 000.0 000.0 000.0 000.0 H00.0 000.0 000.0 000.0 000.0 000.0 000.0 000.0 0000 000.00 000.0 000.0 000.0 000.0 000.0 000.0 0H0.0 000.0 00H.0 0H0.0 0H0.0 000.0 000a N00H1000H .000:0=<40 00: 4<00002200 .0.0 0000H0000 0 x~02000< .0.0 4<000 Emnemum0 smnem>oz Lmnouuo gmnsmuqmm um000< 00:0 00:0 00: 7:2 saga: 0Lmagnmu 0Lm==m0 xhzoz 139 00.00 00.00 0H.00 00.00 00.00 00.00 00.00 00.00 0H.00 00.00 00.00 00.00 HO0H 00.00 00.00 00.00 00.00 00.00 00.00 00.00 00.H0 00.00 00.00 00.00 H0.00 000H ~H.fie mfi.e¢ mH.me «a.mm mm.ee Hm.oe 0N.~e 0m.oe me.mm N0.oe 00.mm mo.mm 000a 00.00 fi0.H0 00.00 00.00 00.00 00.00 00.00 00.00 00.00 00.00 H0.00 H0.00 000H 00.~0 00.00 00.00 00.H0 00.00 HN.00 00.00 00.H0 00.00 00.00 0H.0N 00.00 000H 00.00 00.00 00.00 00.00 00.00 00.00 00.00 00.H0 00.00 00.H0 00.00 00.00 000H Awe: “>00 to 010003000020: awe m=0<>0 0 x~0zmmm< 000H1000H .0000; 00: 0oz gwnouuo gmnewpqmm um00=< 00:0 0:00 002 00000 00002 agmagnmm 0:00:00 .1020: APPENDIX E OPERATING PROCEDURE 0F SIMULATION MODEL I. Exogenous Inputs 1. U.S. Commercial Hog Supply - 72 Months 2. Price Expectations a. Labor b. Fuel Oil c. Natural Gas 3. Energy Supply Expectations a. Fuel Oil Supply b. Natural Gas Supply c. 12% Energy Reduction Goal by 1982 II. Simulation Model 1. Select Energy Reduction Alternatives Prior to Start of Production Year a. Estimate Production for the next 15 Years - Market Share of U.S. Hogs b. Compute N.P.V. of Alternatives Given Produc- tion Level and Expected Energy Prices c. Long-Run Decision - AdOpt Technology if N.P.V. > 0 d. Estimate Energy Demand for First Year e. Check Energy Supply with Expected Energy De- mands; if S > 0, Continue; if S < D, AdOpt Next Best Technology 2. Set Production Level for Month I, I = 1 . . . . 12 a. Firm Decision Criteria 1. Production in Month I is a Function of Market Share and Prepaid Labor Estimate Live and Wholesale Hog Value for U.S. Hog Supply Adjust Live Hog Values if Production is Not Equal to Market Price Compute Monthly Costs and Revenues for Production Level I Compute Monthly Products Processed Within Plant Ul-hWN 140 3. CDN 0000101014:me Write Annual Reports When I 141 Compute Monthly Energy Demands for Pro- duct Processed and Reduce Demands if Pro- duction Alternatives were AdOpted . Compute Monthly Financial Position . Write Reports--Production, Financial, Energy . Change Month I = I + 1 and Repeat Steps (2a1-2a8) until I = 13; if I = 13, re- turn to 1 Profit Maximizing Decision Criteria Production in Month I is Determined by the Level of Profits Estimate Live and Wholesale Hog Values for U.S. Hog Supply . Adjust Live Hog Values for Possible Pro- duction Range . Compute Monthly Costs and Revenues Over Production Range . Search Over Profit Function to Select Maximum Profit Production in Month I Determined by Pro- fit Maximization Compute All Costs and Revenues for Monthly Production Level Compute Monthly Products Processed . Compute Monthly Energy Demands for Pro- ducts Processed and Reduce Demands if Pro- duction Alternatives were AdOpted . Compute Monthly Financial Report 11. 12. Write Reports--Production, Financial, Energy Change Month I = I + 1 and Repeat Steps (2b1-2b11) Until I 13; If I = 13, Re- turn to 1 ' 72 nICHIan STRTE UNIV. LIBRARIES IIIII IIIIII III IIII III IIIIIIIIII IIII IIII II IIIIIIII IIIIIIII IIIIIII 31293101532863