A novel framework for quantification of supply chain risks

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Qazi, Abroon and Quigley, John and Dickson, Alexander; Del Granado, Pedro Crespo and Joyce-Moniz, Martim and Ravizza, Stefan, eds. (2014) A novel framework for quantification of supply chain risks. In: 4th Student Conference on Operational Research. Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, GBR, pp. 1-15. ISBN 9783939897675 (https://doi.org/10.4230/OASIcs.SCOR.2014.1)

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Abstract

Supply chain risk management is an active area of research and there is a research gap of exploring established risk quantification techniques in other fields for application in the context of supply chain management. We have developed a novel framework for quantification of supply chain risks that integrates two techniques of Bayesian belief network and Game theory. Bayesian belief network can capture interdependency between risk factors and Game theory can assess risks associated with conflicting incentives of stakeholders within a supply network. We introduce a new node termed 'Game theoretic risks' in Bayesian network that gets its qualitative and quantitative structure from the Game theory based analysis of the existing policies and partnerships within a supply network. We have applied our proposed risk modeling framework on the development project of Boeing 787 aircraft. Two different Bayesian networks have been modeled; one representing the Boeing's perceived supply chain risks and the other depicting real time supply chain risks faced by the company. The qualitative structures of both the models were developed through cognitive maps that were constructed from the facts outlined in a case study. The quantitative parts were populated based on intuition and subsequently updated with the facts. The Bayesian network model incorporating quantification of game theoretic risks provides all the reasons for the delays and financial loss of the project. Furthermore, the proactive strategies identified in various case studies were verified through our model. Such an integrated application of two different quantification techniques in the realm of supply chain risk management bridges the mentioned research gap. Successful application of the framework justifies its potential for further testing in other supply chain risk quantification scenarios.

ORCID iDs

Qazi, Abroon ORCID logoORCID: https://orcid.org/0000-0003-4609-8712, Quigley, John ORCID logoORCID: https://orcid.org/0000-0002-7253-8470 and Dickson, Alexander ORCID logoORCID: https://orcid.org/0000-0001-9386-9036; Del Granado, Pedro Crespo, Joyce-Moniz, Martim and Ravizza, Stefan
CORE (COnnecting REpositories)