Abstract
In recent years, numerous events have shown the extent to which companies, and subsequently their supply chains, are vulnerable to uncertain events. We have witnessed many supply chain malfunctions (with substantial consequences) due to supply and demand disruptions: affected companies reported, on average, a 14% increase in inventories, an 11% increase in cost, and a 7% decrease in sales in the year following the disruption (Hendricks and Singhal 2005). Component shortages, labor strikes, natural and manmade disasters, human errors, changes in customer taste, technological failures, malicious activities, and financially distressed and, in extreme cases, bankrupt partners, among many others, can cause disruptions in supply chains:
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Notes
- 1.
In the rest of the chapter, we call “scenario” what in reality is a class of scenarios, meaning that we do not try to capture all possible details of a chain of events. We do this because an excessive level of details of individual scenarios could lead to unmanageable analytical complexities in practice.
- 2.
For example, Deleris et al. (2004) define the supply chain performance at the level of a firm as the number of products manufactured within a given timeframe, a scenario as a set of events that disrupts the operations of some of the production plants within a network, and then use a product-mix model to measure the effect of the scenario on the value of the process to the firm.
- 3.
Others have followed the same path. For instance, Hicks (1999) describes a four-step method based on both simulation and optimization aimed at supply chain strategic planning. In his method, simulation is used to describe the dynamic behavior of a given supply chain structure and to assess the benefits of supply chain policies, such as inventory policies. Ingalls (1999) describes a simulation-based tool for supply chain analysis implemented at Compaq, which incorporates demand forecast errors.
- 4.
- 5.
In the base case, the maximum leadtime occurs as a result of mediocre management faced with a significant shortage of a complex component at the time of engineering changes on the subassembly site. In the disasters scenario, the maximum leadtime is achieved due to some engineering changes on the lines and a large labor strike.
- 6.
Risk perception is quite critical, especially when reliance on expert opinion is necessary due to lack of data to estimate the event probabilities. For example, a recent cover story of Time magazine explores Americans’ faulty risk perceptions (Time, November 26, 2006). Although we have not discussed it in this chapter, there is a prolific literature on risk perception. For a quick reference, we refer the reader to Slovic (1987).
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Acknowledgements
This study was funded in part by NSF grant #NSF/CAREER-0547021. We gratefully acknowledge partial support from GM/SU Collaborative Lab and the company in the case study. To protect the company, all data and information provided here are either publicly available or have been sufficiently disguised without removing the essence of the situation and the results. We thank all parties at the company for allowing us to use this information. We also acknowledge the constructive comments provided by Debra Elkins (GM) and Professor Elisabeth Paté-Cornell (Department of Management Science and Engineering at Stanford University). Last, but not least, we are indebted to four anonymous referees whose suggestions improved the chapter considerably.
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Deleris, L.A., Erhun, F. (2011). Quantitative Risk Assessment in Supply Chains: A Case Study Based on Engineering Risk Analysis Concepts. In: Kempf, K., Keskinocak, P., Uzsoy, R. (eds) Planning Production and Inventories in the Extended Enterprise. International Series in Operations Research & Management Science, vol 152. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8191-2_5
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