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A production planning model to reduce risk and improve operations management

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  • Wang, X.
  • Li, D.
  • O'brien, C.
  • Li, Y.

Abstract

Traceability has become an essential business function to consistently supply quality and safe food products to consumers. However, it has been not rare that the efforts in traceability are separately made from routine operations management decisions. In this paper, an integrated optimisation model is developed in which the product safety related traceability factor is incorporated with operations factors to develop an optimal production plan. The model aims to improve traceability and manufacturing performance by simultaneously optimising the production batch size and batch dispersion with risk factors. Two industrial cases are used to support numerical analyses to investigate the benefit under various business situations and product features.

Suggested Citation

  • Wang, X. & Li, D. & O'brien, C. & Li, Y., 2010. "A production planning model to reduce risk and improve operations management," International Journal of Production Economics, Elsevier, vol. 124(2), pages 463-474, April.
    • Handle: RePEc:eee:proeco:v:124:y:2010:i:2:p:463-474
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    1. Trkman, Peter & McCormack, Kevin, 2009. "Supply chain risk in turbulent environments--A conceptual model for managing supply chain network risk," International Journal of Production Economics, Elsevier, vol. 119(2), pages 247-258, June.
    2. Steven Nahmias, 1982. "Perishable Inventory Theory: A Review," Operations Research, INFORMS, vol. 30(4), pages 680-708, August.
    3. Goyal, S. K. & Giri, B. C., 2001. "Recent trends in modeling of deteriorating inventory," European Journal of Operational Research, Elsevier, vol. 134(1), pages 1-16, October.
    4. Bhavin J. Shah & Nita H. Shah & Y. K. Shah, 2005. "Eoq Model For Time-Dependent Deterioration Rate With A Temporary Price Discount," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 479-485.
    5. Chun, Young H., 2003. "Optimal pricing and ordering policies for perishable commodities," European Journal of Operational Research, Elsevier, vol. 144(1), pages 68-82, January.
    6. Wen Zhao & Yu-Sheng Zheng, 2000. "Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous Demand," Management Science, INFORMS, vol. 46(3), pages 375-388, March.
    7. Dorit Hochbaum, 2007. "Complexity and algorithms for nonlinear optimization problems," Annals of Operations Research, Springer, vol. 153(1), pages 257-296, September.
    8. Bogaschewsky, Ronald W. & Buscher, Udo D. & Lindner, Gerd, 2001. "Optimizing multi-stage production with constant lot size and varying number of unequal sized batches," Omega, Elsevier, vol. 29(2), pages 183-191, April.
    9. Alfaro, Jos A. & Rbade, Luis A., 2009. "Traceability as a strategic tool to improve inventory management: A case study in the food industry," International Journal of Production Economics, Elsevier, vol. 118(1), pages 104-110, March.
    10. Hallikas, Jukka & Karvonen, Iris & Pulkkinen, Urho & Virolainen, Veli-Matti & Tuominen, Markku, 2004. "Risk management processes in supplier networks," International Journal of Production Economics, Elsevier, vol. 90(1), pages 47-58, July.
    11. Wu, Desheng & Olson, David L., 2008. "Supply chain risk, simulation, and vendor selection," International Journal of Production Economics, Elsevier, vol. 114(2), pages 646-655, August.
    12. Hui-Ming Wee & Yu, Jonas, 1997. "A deteriorating inventory model with a temporary price discount," International Journal of Production Economics, Elsevier, vol. 53(1), pages 81-90, November.
    13. Josef Kallrath, 2005. "Solving Planning and Design Problems in the Process Industry Using Mixed Integer and Global Optimization," Annals of Operations Research, Springer, vol. 140(1), pages 339-373, November.
    14. Souilah, Abdelghani, 1995. "Simulated annealing for manufacturing systems layout design," European Journal of Operational Research, Elsevier, vol. 82(3), pages 592-614, May.
    15. Fritz, Melanie & Schiefer, Gerhard, 2009. "Tracking, tracing, and business process interests in food commodities: A multi-level decision complexity," International Journal of Production Economics, Elsevier, vol. 117(2), pages 317-329, February.
    16. Bhattacharjee, Sudip & Ramesh, R., 2000. "A multi-period profit maximizing model for retail supply chain management: An integration of demand and supply-side mechanisms," European Journal of Operational Research, Elsevier, vol. 122(3), pages 584-601, May.
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    2. Soysal, Mehmet & Bloemhof-Ruwaard, Jacqueline.M. & Meuwissen, Miranda P.M. & van der Vorst, Jack G.A.J., 2012. "A Review on Quantitative Models for Sustainable Food Logistics Management," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 3(2), pages 1-20, December.
    3. Saikouk, Tarik & Badraoui, Ismail & Spalanzani, Alain, 2014. "The Forest Supply Chain Management: An Entropic Perspective," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Next Generation Supply Chains: Trends and Opportunities. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 18, volume 18, pages 487-513, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    4. Kong, Dongmin & Shi, Lu & Yang, Zhiqing, 2019. "Product recalls, corporate social responsibility, and firm value: Evidence from the Chinese food industry," Food Policy, Elsevier, vol. 83(C), pages 60-69.
    5. Sovan Mitra & Andreas Karathanasopoulos, 2019. "Firm Value and the Impact of Operational Management," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(1), pages 61-85, March.
    6. Montecchi, Matteo & Plangger, Kirk & West, Douglas C., 2021. "Supply chain transparency: A bibliometric review and research agenda," International Journal of Production Economics, Elsevier, vol. 238(C).
    7. Brofman Epelbaum, Freddy Moises & Garcia Martinez, Marian, 2014. "The technological evolution of food traceability systems and their impact on firm sustainable performance: A RBV approach," International Journal of Production Economics, Elsevier, vol. 150(C), pages 215-224.
    8. Auler, Daniel P. & Teiceira, Rafael & Nardi, Vinicius, 2016. "Food safety as a field in supply chain management studies: a systematic literature review," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 20(1), September.
    9. Hall, David C. & Johnson-Hall, Tracy D., 2021. "Recall effectiveness, strategy, and task complexity in the U.S. meat and poultry industry," International Journal of Production Economics, Elsevier, vol. 234(C).
    10. Soysal, M. & Bloemhof-Ruwaard, J.M. & van der Vorst, J.G.A.J., 2014. "Modelling food logistics networks with emission considerations: The case of an international beef supply chain," International Journal of Production Economics, Elsevier, vol. 152(C), pages 57-70.
    11. Saak, Alexander E., 2016. "Traceability and reputation in supply chains," International Journal of Production Economics, Elsevier, vol. 177(C), pages 149-162.
    12. Rodrigues, Daniel & Teixeira, Rafael & Shockley, Jeff, 2019. "Inspection agency monitoring of food safety in an emerging economy: A multilevel analysis of Brazil's beef production industry," International Journal of Production Economics, Elsevier, vol. 214(C), pages 1-16.
    13. Soysal, Mehmet & Bloemhof, Jacqueline M. & van der Vorst, Jack G.A.J., 2012. "A Review of Quantitative Models for Sustainable Food Logistics Management: Challenges and Issues," 2012 International European Forum, February 13-17, 2012, Innsbruck-Igls, Austria 144974, International European Forum on System Dynamics and Innovation in Food Networks.
    14. Xiongyong Zhou & Madeleine Pullman & Zhiduan Xu, 2022. "The impact of food supply chain traceability on sustainability performance," Operations Management Research, Springer, vol. 15(1), pages 93-115, June.
    15. Zhao, Xiande & Li, Yina & Flynn, Barbara B., 2013. "The financial impact of product recall announcements in China," International Journal of Production Economics, Elsevier, vol. 142(1), pages 115-123.
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    17. Aiello, Giuseppe & Enea, Mario & Muriana, Cinzia, 2015. "The expected value of the traceability information," European Journal of Operational Research, Elsevier, vol. 244(1), pages 176-186.

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