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Predicting and Managing Supply Chain Risks

In: Supply Chain Risk

Author

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  • Samir Dani

    (Loughborough University)

Abstract

Since the start of the new century the world at large has experienced escalating uncertainty as a result of climate changes, epidemics, terrorist threats and an increasing amount of economic upheaval. These uncertainties create risks for the proper functioning of supply chains. This chapter provides an insight into developing a proactive approach to predict risks and manage uncertainties that may potentially disrupt the supply chain. The aim of the chapter is to present a holistic perspective regarding supply chain risk management and incorporate a methodology to manage supply chain risks proactively. When discussing supply chain risk issues with industry personnel it was noticed that post 9/11, the issue of supply disruption had gained importance within the industry. But the focus on managing these disruptions and sources of these disruptions has been primarily reactive. Supply chain personnel in some instances have remarked that they have in the past researched and presented to their top management proactive risk management solutions which had been subsequently rejected and no investment provided. There is now, however, an increasing interest regarding proactive tools and hence this chapter seeks to present a framework for implementing proactive risk management. The chapter also suggests some tools which may prove useful in predicting supply chain risks.

Suggested Citation

  • Samir Dani, 2009. "Predicting and Managing Supply Chain Risks," International Series in Operations Research & Management Science, in: George A. Zsidisin & Bob Ritchie (ed.), Supply Chain Risk, chapter 4, pages 53-66, Springer.
    • Handle: RePEc:spr:isochp:978-0-387-79934-6_4
    DOI: 10.1007/978-0-387-79934-6_4
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    Cited by:

    1. da Cunha, Richard Alex & Rangel, Luís Alberto Duncan & Rudolf, Christian A. & Santos, Luiza dos, 2022. "A decision support approach employing the PROMETHEE method and risk factors for critical supply assessment in large-scale projects," Operations Research Perspectives, Elsevier, vol. 9(C).
    2. Guertler, Benjamin & Spinler, Stefan, 2015. "Supply risk interrelationships and the derivation of key supply risk indicators," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 224-236.
    3. Bertrand Baud-Lavigne & Samuel Bassetto & Bruno Agard, 2016. "A method for a robust optimization of joint product and supply chain design," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 741-749, August.
    4. Meike Schroeder & Sebastian Lodemann, 2021. "A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management," Logistics, MDPI, vol. 5(3), pages 1-17, September.
    5. Vlajic, Jelena V. & van der Vorst, Jack G.A.J. & Haijema, René, 2012. "A framework for designing robust food supply chains," International Journal of Production Economics, Elsevier, vol. 137(1), pages 176-189.

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