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Supply Chain Disruptions Preparedness Measures Using a Dynamic Model

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Supply Chain Risk Management

Abstract

Supply chain risk management has recently seen extensive research efforts, but questions such as “How should a firm plan for each type of disruption?” and “What are the strategies and the total cost incurred by the firm if a disruption occurs?” continue to deserve attention. This chapter analyzes different disruption cases by considering the impacts of disruptions at a supplier, a firm’s warehouse, and at the firm’s production facility. The firm can prepare for each type of disruption by buying from an alternate supplier, holding more inventory, or holding inventory at a different warehouse. The Wagner-Whitin model is used to solve the optimal ordering strategy for each type of disruption. Since the type of disruption is uncertain, we assign probabilities for each disruption and use the Wagner-Whitin model to find the order policy that minimizes the firm’s expected cost.

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References

  • Adhyitya, A., Srinivasan, R., & Karimi, I. A. (2007). Heuristic rescheduling of crude oil operations to manage abnormal supply chain events. American Institute of Chemical Engineers Journal, 53(2), 397–422.

    Article  Google Scholar 

  • Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm’s resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33, 111–122.

    Article  Google Scholar 

  • Babich, V., Burnetas, A. N., & Ritchken, P. H. (2007). Competition and diversification effects in supply chains with supplier default risk. Manufacturing & Service Operations Management, 9(2), 123–146.

    Article  Google Scholar 

  • Bean, J. C., Birge, J. R., Mittenthal, J., & Noon, C. E. (1991). Matchup scheduling with multiple resources, release dates and disruptions. Operations Research, 39(3), 470–483.

    Article  Google Scholar 

  • Bode, C., & Wagner, S. M. (2015). Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of Operations Management, 36, 215–228.

    Article  Google Scholar 

  • Hendricks, K. B., Singhal, V. R., & Zhang, R. (2009). The effect of operational slack, diversification, and vertical relatedness on the stock market reaction to supply chain disruptions. Journal of Operations Management, 27(3), 233–246.

    Article  Google Scholar 

  • Hopp WJ, Iravani SMR, Liu Z (2009) Strategic risk from supply chain disruptions. Working paper. Department of Industrial Engineering and Management Sciences, Northwestern University. Retrieved from http://webuser.bus.umich.edu/whopp/working%20papers/Strategic%20Risk%20from%20Supply%20Chain%20Disruptions.pdf

  • Hopp, W. J., & Spearman, M. L. (2008). Factory physics (3rd ed.). Boston, MA: McGraw-Hill.

    Google Scholar 

  • Jeunet, J., & Jonard, N. (2000). Measuring the performance of lot-sizing techniques in uncertain environments. International Journal of Production Economics, 64(1), 197–208.

    Article  Google Scholar 

  • Kazan, O., Nagi, R., & Rump, C. M. (2000). New lot-sizing formulations for less nervous production schedules. Computers & Operations Research, 27(13), 1325–1345.

    Article  Google Scholar 

  • MacKenzie CA, Barker K, Grant FH (2012) Evaluating the consequences of an inland waterway port closure with a dynamic multiregional interdependence model. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 42(2), 359–370.

    Google Scholar 

  • MacKenzie, C. A., Barker, K., & Santos, J. R. (2014). Modeling a severe supply chain disruption and post-disaster decision making with application to the Japanese earthquake and tsunami. IIE Transactions, 46(12), 1243–1260.

    Article  Google Scholar 

  • Natarajarathinam, M., Capar, I., & Narayanan, A. (2009). Managing supply chains in times of crisis: A review of literature and insights. International Journal of Physical Distribution & Logistics Management, 39(7), 535–573.

    Article  Google Scholar 

  • Papadakis, I. S. (2006). Financial performance of supply chains after disruptions: An event study. Supply Chain Management: An International Journal, 11(1), 25–33.

    Article  Google Scholar 

  • Richter, K., & Weber, J. (2001). The reverse Wagner/Whitin model with variable manufacturing and remanufacturing cost. International Journal of Production Economics, 71, 447–456.

    Article  Google Scholar 

  • Richter, K., & Sombrutzki, M. (2000). Remanufacturing planning for the reverse Wagner/Whitin models. European Journal of Operational Research, 121(2), 304–315.

    Article  Google Scholar 

  • Sheffi, Y. (2005). The resilient enterprise: Overcoming vulnerability for competitive advantage. Cambridge: The MIT Press.

    Google Scholar 

  • Schmitt, A. J., & Tomlin, B. (2012). Sourcing strategies to manage supply disruptions. In H. Gurnani, A. Mehrotra, & S. Ray (Eds.), supply chain disruptions: Theory and practice of managing risk (pp. 51–72). London: Springer.

    Chapter  Google Scholar 

  • Snyder, L. V., Atan, Z., Peng, P., Rong, Y., Schmitt, A. J., & Sinsoysal, B. (2016). OR/MS models for supply chain disruptions: A review. IIE Transactions, 48(2), 89–109.

    Article  Google Scholar 

  • Snyder LV, Scaparra MP, Daskin MS, Church RL (2006) Planning for disruptions in supply chain networks. Tutorials in Operations Research, 234–257.

    Google Scholar 

  • Song, J.-S., & Zipkin, P. H. (1996). Inventory control with information about supply conditions. Management Science, 42(10), 1409–1419.

    Article  Google Scholar 

  • Song, J.-S., & Zipkin, P. (2009). Inventories with multiple supply sources and networks of queues with overflow bypasses. Management Science, 55(3), 362–372.

    Article  Google Scholar 

  • Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488.

    Article  Google Scholar 

  • Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, 52(5), 639–657.

    Article  Google Scholar 

  • Vakharia, A. J., & Yenipazarli, A. (2008). Managing supply chain disruptions. Foundations and Trends in Technology, Information and Operations Management, 2(4), 243–325.

    Article  Google Scholar 

  • Wagner, H. M., & Whitin, T. M. (1958). Dynamic version of the economic lot size model. Management Science, 9(1), 89–96.

    Article  Google Scholar 

  • Xia, Y., Yang, M.-H., Golany, B., Gilbert, S. M., & Yu, G. (2004). Real-time disruption management in a two-stage production and inventory system. IIE Transactions, 36, 111–125.

    Article  Google Scholar 

  • Xiao, T., & Qi, X. (2008). Price competition, cost and demand disruptions and coordination of a supply chain with one manufacturer and two competing retailers. Omega, 36(5), 741–753.

    Article  Google Scholar 

  • Xiao, T., & Yu, G. (2006). Supply chain disruption management and evolutionarily stable strategies of retailers in the quantity-setting duopoly situation with homogeneous goods. European Journal of Operational Research, 173(2), 648–668.

    Article  Google Scholar 

  • Xiao, T., Yu, G., Sheng, Z., & Xia, Y. (2005). Coordination of a supply chain with one-manufacturer and two-retailers under demand promotion and disruption management decisions. Annals of Operations Research, 135(1), 87–109.

    Article  Google Scholar 

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Correspondence to Amit Sonar .

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Sonar, A., Mackenzie, C.A. (2018). Supply Chain Disruptions Preparedness Measures Using a Dynamic Model. In: Khojasteh, Y. (eds) Supply Chain Risk Management. Springer, Singapore. https://doi.org/10.1007/978-981-10-4106-8_8

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