Annals of Operations Research

, Volume 219, Issue 1, pp 5–23 | Cite as

New opportunities in operations research to improve pork supply chain efficiency

  • Sara V. Rodríguez
  • Lluis M. Plà
  • Javier Faulin
Article

Abstract

The structure of the pork sector in world economy is changing. In many countries the number of pig farms is being reduced, while the herd size of the remaining ones is increasing. Pig production process is partitioned into different phases with specialized farms devoted to piglet production, rearing or fattening pigs are common instead of old farrowing-to-fattening farms. Pig farms have tended to be integrated and coordinate their operations into pork supply chains by using tighter vertical coordination linkages. This paper presents a description of the pork supply chain, stressing the role of pig farming as one of the key issues to improve pork supply chain efficiency. A survey of literature to support the decision making on the pork sector has revealed that most papers had only considered individual farm operations, while the pork supply chain management involves the coordination of sets of farm units at different stages of production. Thus, our contribution emphasizes the importance and complexity of new decision-making tasks regarding the modern organization of the pork sector. All these elements make it possible to envisage new opportunities for operations research methods to be successfully applied to the pork supply chain. Likewise, we have identified some existing gaps in the literature that we believe should be addressed in the near future.

Keywords

Pork supply chain management Sow herd models Pork sector Supply chain management models 

Notes

Acknowledgements

The authors appreciate the financial help from the Working Community of the Pyrenees in the development of this research (code IIQ13172.RI1-CTP09-R2). Sara V. Rodriguez acknowledges the Mexican Mathematical Society-Sofia Kovalevskaia Foundation, the project NPTC founded by PROMEP PROMEAP/103.5/11/4330, and the AMC-FUMEC for the grant received during the development of this work. Lluis M. Plà wishes to acknowledge the financial support of the Spanish Research Program (MTM2005-09362-C03-02, AGL2009-12026 and MTM2009-14087-C04-01). Javier Faulin wants also to recognize the financial aid of the Spanish Ministry of Science with the project TRA2010-21644-C03-01 and the support of the Research Network “Sustainable TransMET” funded by the Government of Navarre (Spain) in the Program “Jerónimo de Ayanz”.

References

  1. Allen, M. A., & Stewart, T. S. (1983). A simulation model for a swine breeding unit producing feeder pigs. Agricultural Systems, 10, 193–211. CrossRefGoogle Scholar
  2. Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: a review. European Journal of Operational Research, 196(1), 1–20. CrossRefGoogle Scholar
  3. Backus, G., & Dijkhuizen, A. A. (2002). Kernkamp lecture: the future of the European pork chain. In Allen D. Leman swine conference, Minnesota, USA (pp. 8–11). Google Scholar
  4. Bailleul, P. J. D., Bernier, J. F., van Milgen, J., Sauvant, D., & Pomar, C. (2000). The utilization of prediction models to optimize farm animal production systems: the case of a growing pig model. In J. P. McNamara, J. France, & D. Beever (Eds.), Modelling nutrient utilization in farm animals (pp. 379–392). Wallingford: CABI Publishing. CrossRefGoogle Scholar
  5. Balogh, P., Ertsey, I., Fenyves, V., & Nagy, L. (2009). Analysis and optimization regarding the activity of a Hungarian pig sales and purchased cooperation. Studies in Agricultural Economics, 109, 35–54. Google Scholar
  6. Bloemhof, J. M., Smeets, C. M., & van Nunen, J. A. E. E. (2004). Supply chain optimization in animal husbandry. Erasmus Research Institute of Management. In B. Fleischmann & A. Klose (Eds.), Lecture notes in econometrics and mathematical systems: Vol. 544. Distribution logistics; advanced solutions to practical problems (pp. 47–64). Berlin: Springer. Google Scholar
  7. Boland, M. A., Preckel, P. V., & Schinckel, A. P. (1993). Optimal hog slaughter weights under alternative pricing systems. Journal of Agriculture and Applied Economics, 25(2), 148–163. Google Scholar
  8. Boys, K. A., Li, N., Preckel, P. V., Schinckel, A. P., & Foster, K. A. (2007). Economic replacement of a heterogeneous herd. American Journal of Agricultural Economics, 89, 24–35. CrossRefGoogle Scholar
  9. Broek, J. V. D., Schütz, P., Stougie, L., & Tomasgard, A. (2006). Location of slaughterhouses under economies of scale. European Journal of Operational Research, 175(2), 740–750. CrossRefGoogle Scholar
  10. Broekmans, J. E. (1992). Influence of price fluctuations on delivery strategies for slaughter pigs. Dina Nota, 7, 1–24. Google Scholar
  11. Bookbinder, J. H., & Matuk, T. A. (2009). Logistics and Transportation in Global Supply Chains: Review, Critique, and Prospects. In Tutorials in Operations Research INFORMS (pp. 182–211). ISBN 978-1-877640-24-7. Google Scholar
  12. Cachon, G. (2003). Supply chain coordination with contracts. In S.G. deKok (Ed.), Handbooks in operations Research and Management Science: Supply Chain Management, Amsterdam: North-Holland. Chap. 11. Google Scholar
  13. Chavas, J. P., Kliebenstein, J., & Crenshaw, J. D. (1985). Modeling dynamical agricultural production response: the case of swine production. American Journal of Agricultural Economics, 67(3), 636–646. CrossRefGoogle Scholar
  14. Christopher, M. (1998). Logistics and supply chain management. strategies for reducing cost and improving service. London: Prentice Hall. Google Scholar
  15. Chopra, S., & Meindl, P. (2007). Supply chain management: strategic, planning and operation. Upper Saddle River: Prentice-Hall. Google Scholar
  16. Christou, I. T. (2012). Quantitative methods in supply chain management. models and algorithms. London: Springer. CrossRefGoogle Scholar
  17. Dijkhuizen, A. A., Morris, R. S., & Morrow, M. (1986). Economic optimization of culling strategies in swine breeding herds, using the “PORKCHOP computer program”. Preventive Veterinary Medicine, 4, 341–353. CrossRefGoogle Scholar
  18. Faostat (2012). Statistics of livestock primary in the section of Production. Downloadable from the webpage http://faostat.fao.org/site/573/default.aspx#ancor. Last access February 23th, 2012.
  19. Filho, K. E. (2004). Supply chain approach to sustainable beef production from a Brazilian perspective. Livestock Production Science, 90, 53–61. CrossRefGoogle Scholar
  20. Gribkovskaia, I., Gullberg, B. O., Hovden, K., & Wallance, S. W. (2006). Optimization model for a livestock collection problem. International Journal of Physical Distribution & Logistics Management, 36(2), 136–152. CrossRefGoogle Scholar
  21. Higgins, A. J., & Laredo, L. A. (2006). Improving harvesting and transport planning within a sugar value chain. Journal of the Operational Research Society, 57(4), 367–376. CrossRefGoogle Scholar
  22. Higgins, A. J., Miller, C. J., Archer, A. A., Ton, T., Fletcher, C. S., & McAllister, R. R. J. (2009). Challenges of operations research practice in agricultural value chains. Journal of the Operational Research Society, 61, 964–973. CrossRefGoogle Scholar
  23. Hobbs, J. E., Kerr, W. A., & Klein, K. K. (1998). Creating international competitiveness through supply chain management: Danish pork. Supply Chain Management: An International Journal, 3(2), 68–78. CrossRefGoogle Scholar
  24. Hobbs, J. E., & Young, L. M. (2000). Closer vertical co-ordination in agri-food supply chains: a conceptual framework and some preliminary evidence. Supply chain Management: An International Journal, 5(3), 131–143. CrossRefGoogle Scholar
  25. Huirne, R. B., Dijkhuizen, A. A., Van Beek, P., & Hendriks, Th. H. B. (1993). Stochastic dynamic programming to support sow replacement decisions. European Journal of Operational Research, 67, 161–171. CrossRefGoogle Scholar
  26. Jalving, A. W., Dijkhuizen, A. A., & van Arendonk, J. A. M. (1992). Dynamic probabilistic modelling of reproduction and management in sow herds. General aspects and model description. Agricultural Systems, 39, 133–152. CrossRefGoogle Scholar
  27. Jørgensen, E. (1993). The influence of weighing precision on delivery decision in slaughter pig production. Acta Agriculture Scandinavica, Section A, Animal Science, 43, 181–189. CrossRefGoogle Scholar
  28. Khamjan, S., Piewthongngam, K., & Pathumnakul, S. (2013). Pig procurement plan considering pig growth and size distribution. Computers & Industrial Engineering, 64(4), 886–894. CrossRefGoogle Scholar
  29. Kamp, J. A. L. M. (1999). Knowledge based systems: from research to practical application: pitfalls and critical success factors. Computers & Electronics in Agriculture, 22(2–3), 243–250. CrossRefGoogle Scholar
  30. Kohls, R. L., & Uhl, J. N. (2002). Marketing of agricultural products (9th ed.). Upper Saddle River: Prentice Hall. Google Scholar
  31. Krieter, J. (2002). Evaluation of different pig production systems including economic, welfare and environmental aspects. Archiv für Tierzucht, 45(3), 223–235. Google Scholar
  32. Kristensen, A. R., Jørgensen, E., & Toft, N. (2012). Herd Management Science. II. Advanced topics http://www.prodstyr.ihh.kvl.dk/vp/2011/book.htm. Academic Books ISBN: 9788763461214.
  33. Kristensen, A. R., & Søllested, T. A. (2004a). A sow replacement model using Bayesian updating in a three-level hierarchic Markov process I. Biological model. Livestock Production Sciences, 87, 13–24. CrossRefGoogle Scholar
  34. Kristensen, A. R., & Søllested, T. A. (2004b). A sow replacement model using Bayesian updating in a three-level hierarchic Markov process II. Optimization model. Livestock Production Sciences, 87, 25–36. CrossRefGoogle Scholar
  35. Kristensen, A. R., Nielsen, L., & Hielsen, M. S. (2012). Optimal slaughter pig marketing with emphasis on information from on-line live weight assessment. Livestock Science, 145, 95–108. CrossRefGoogle Scholar
  36. Kure, H. (1997). Marketing Management support in slaughter pig production. Ph.D. dissertation, Department of Animal Science and Animal Health, The Royal Veterinary and Agricultural University, Copenhagen. Google Scholar
  37. Liang, J., Fabiosa, J. F., Jensen, H. H., & Miller, G. Y. (2010). Potential HPAI shocks and welfare implications of market power in the US broiler industry. Agricultural and Applied Economics Association. Joint Annual Meeting, Denver, Colorado. Google Scholar
  38. Manzini, R., & Gebennini, E. (2008). Optimization models for the dynamic facility location and allocation problem. International Journal of Production Research, 46(8), 2061–2086. CrossRefGoogle Scholar
  39. Marsh, W. E. (1986). Economic decision making on health and management livestock herds: examining complex problems through computer simulation. Ph.D. Dissertation, University of Minnesota, St. Paul. Google Scholar
  40. Martel, G., Dedieu, B., & Dourmad, J. Y. (2008). Simulation of sow herd dynamics with emphasis on performance and distribution of periodic task events. Journal of Agricultural Science, 146, 365–380. Google Scholar
  41. Martinez, S. W., & Reed, A. (1996). From farmers to consumers. Vertical coordination in the food Industry. Economic Research Service. Report, No. 720, USDA, Washington. Google Scholar
  42. McCown, R. L. (2002). Locating agricultural decision support systems in the troubled past and sociotechnical compexity of ‘models for management’. Agricultural Systems, 74(1), 11–25. CrossRefGoogle Scholar
  43. Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management—a review. European Journal of Operational Research, 119, 14–34. Google Scholar
  44. Min, H., & Zhou, G. (2002). Supply chain modelling: past, present and future. Computers & Industrial Engineering, 43, 231–249. CrossRefGoogle Scholar
  45. Nadal, E., & Plà, L. M. (2013). Optimal planning of pig transfers along a pig supply chain by a mixed integer linear programming model. Submitted to the Journal of the Operational Research Society. Google Scholar
  46. Niemi, J. K. (2006). A dynamic programming model for optimizing feeding and slaughter decisions regarding fattening pigs. Agricultural and Food Science, 15, 6–121. Google Scholar
  47. Ohlmann, J. W., & Jones, P. C. (2011). An integer programming model for optimal pork marketing. Annals of Operations Research, 190(1), 271–287. CrossRefGoogle Scholar
  48. Oppen, J., & Løkketangen, A. (2008). A tabu search approach for the livestock collection problem. Computers & Operations Research, 35(10), 3213–3229. CrossRefGoogle Scholar
  49. Oppen, J., Løkketangen, A., & Desrosiers, J. (2010). Solving a rich vehicle routing and inventory using column generation. Computers & Operations Research, 37(7), 1308–1317. CrossRefGoogle Scholar
  50. Ouden, M., Dijkhuizen, A. A., Huirne, R. B. M., & Zuurbier, P. J. P. (1996). Vertical cooperation in agricultural production marketing chains, with special reference to product differentiation in pork. Agribusiness, 12, 277–290. CrossRefGoogle Scholar
  51. Ouden, M., Huirne, R. B. M., Dijkhuizen, A. A., & van Beek, P. (1997a). Economic optimization of pork production-marketing chains: II. Modelling outcome. Livestock Production Science, 48, 39–50. CrossRefGoogle Scholar
  52. Ouden, M., Nijsing, J. T., Dijkhuizen, A. A., & Huirne, R. B. M. (1997b). Economic optimization of pork production-marketing chains: I. Model input on animal welfare and cost. Livestock Production Science, 48, 23–37. CrossRefGoogle Scholar
  53. Perez, C., de Castro, R., & Font i Furnols, M. (2009). The pork industry: a supply chain perspective. British Food Journal, 111(3), 257–274. CrossRefGoogle Scholar
  54. Perez, C., de Castro, R., Simons, D., & Gimenez, G. (2010). Development of lean supply chains: A case study of the Catalan pork sector. Supply Chain Management, 11:271–280. Google Scholar
  55. Pettigrew, J. E., Cornelius, S. G., Eidman, V. R., & Moser, R. L. (1986). Integration of factors affecting sow efficiency: a modelling approach. Journal of Animal Science, 63, 1314–1321. Google Scholar
  56. Plà-Aragonés, L. M., Rodríguez-Sánchez, S. V., & Rebillas-Loredo, V. (2013, accepted). A mixed integer linear programming model for optimal delivery of fattened pigs to the abattoir. Journal of Applied Operations Research. Google Scholar
  57. Plà, L. M. (2006). Tactical supply chain model of pig production. Proceedings of the Second Meeting of the EURO Working group on Operational Research (OR) in Agriculture and Forest Management. Journal of Agricultural Science, 144(5), 467–472. CrossRefGoogle Scholar
  58. Plà, L. M. (2007). Review of mathematical models for sow herd management. Livestock Science, 106, 107–119. CrossRefGoogle Scholar
  59. Plà, L. M., Conde, J., & Pomar, J. (1998). Sow model for decision aid at farm level. In F. J. Giron (Ed.), Applied decision analysis, Boston: Kluwer Academic. Google Scholar
  60. Plà, L. M., Pomar, C., & Pomar, J. (2003). A Markov decision sow model representing the productive lifespan of sows. Agricultural Systems, 76, 253–272. CrossRefGoogle Scholar
  61. Plà, L. M., Faulin, J., & Rodriguez, S. V. (2009). A linear programming formulation of a semi-Markov model to design pig facilities. Journal of the Operational Research Society, 60(5), 619–625. CrossRefGoogle Scholar
  62. Plà, L. M., Sandars, D., & Higgins, A. (2013, accepted). A perspective on Operational Research prospects for agriculture. Journal of Operational Research Society. doi: 10.1057/jors.2913.45
  63. Pibernik, R., & Sucky, E. (2007). An approach to inter-domain master planning in supply chains. International Journal of Production Economics, 108, 200–212. CrossRefGoogle Scholar
  64. Pomar, C., Harris, D. L., & Minvielle, F. (1991). Computer simulation model of swine production systems: III. A dynamic herd simulation model including production. Journal of Animal Science, 69, 2822–2836. Google Scholar
  65. Rodriguez, S. V., Albornoz, V., & Plà, L. M. (2009). A two stage stochastic programming model for scheduling replacements in sow farms. TOP, 17(1), 171–179. CrossRefGoogle Scholar
  66. Rodriguez, S. V. (2010). Models under uncertainty to support sow herd management in the context of the Pork Supply Chain Supply. Ph.D. Dissertation, Department of Mathematics. University of Lleida, Spain. Google Scholar
  67. Rodriguez, S. V., Jensen, T., Plà, L. M., & Kristensen, A. (2011). Optimal replacement policies and economic value of clinical observations in sow herd. Livestock Science, 138, 207–219. CrossRefGoogle Scholar
  68. Roo, G. (1987). A stochastic model to study breeding schemes in a small pig population. Agricultural Systems, 25, 1–25. CrossRefGoogle Scholar
  69. Singh, D. (1986). Simulation of swine herd population dynamics. Agricultural Systems, 22, 157–183. CrossRefGoogle Scholar
  70. Stadler, H. (2005). Supply chain management and advanced planning-basics, overview and challenges. European Journal of Operational Research, 163, 575–588. CrossRefGoogle Scholar
  71. Schütz, P., Stougie, L., & Tomasgard, A. (2008). Stochastic facility location with general long-run costs and convex short-run costs. Computers & Operations Research, 35(9), 2988–3000. CrossRefGoogle Scholar
  72. Schütz, P., Tomasgard, A., & Ahmed, S. (2009). Supply chain design under uncertainty using sample average approximation and dual decomposition. European Journal of Operational Research, 199(2), 409–419. CrossRefGoogle Scholar
  73. Taylor, D. H. (2006). Strategic consideration in the development of lean agri-food supply chains: a case study of the UK pork sector. Supply Chain Management: An International Journal, 11(3), 271–280. CrossRefGoogle Scholar
  74. Tess, M. W., Bennett, G. L., & Dickerson, G. E. (1983). Simulation of genetic changes in life cycle efficiency of pork production. I. A bioeconomic model. Journal of Animal Science, 56, 336–353. Google Scholar
  75. Toft, N. (1998). The dynamic aspect of the reproductive performance in the sow herd. Dina Notat No. 70. Google Scholar
  76. Trienekens, J., Petersen, B., Wognum, N., & Brinkmann, D. (2009). European pork chains. In Diversity and quality challenges in consumer-oriented production and distribution, Wageningen Academic: Wageningen. ISBN 978-90-8686-103-3. Google Scholar
  77. Upton, M. (1989). Livestock productivity assessment and herd growth models. Agricultural Systems, 29, 149–164. CrossRefGoogle Scholar
  78. van der Gaag, M., Vos, F., Saatkamp, H. W., van Boven, M., van Beek, P., & Huirne, R. B. M. (2004). A state-transition simulation model for the spread of salmonella in the pork supply chain. European Journal of Operational Research, 156, 782–798. CrossRefGoogle Scholar
  79. van der Vorst, J. G. A. J., da Silva C. A, & Trienekens, J. H. (2007). Agro-industrial supply chain Management: Concepts and applications. Food and Agriculture Organization of the United Nations, Rome. Agricultural Management, Marketing and Finance, 17. Google Scholar
  80. Whittemore, C. T., & Kyriazakis, I. (2006). Whittemore’s science and practice of pig production. London: Blackwell. Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Sara V. Rodríguez
    • 1
    • 2
  • Lluis M. Plà
    • 2
  • Javier Faulin
    • 3
  1. 1.Graduate Program in System Engineering 111-FUniversidad Autonoma de Nuevo LeonCiudad Universitaria San Nicolas de los GarzaMexico
  2. 2.Department of MathematicsUniversity of LleidaLleidaSpain
  3. 3.Department of Statistics and Operations ResearchPublic University of NavarrePamplonaSpain

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