Interdependency modeling of supply chain risks incorporating game theoretic risks

Abroon Jamal Qazi, John Quigley, Alexander Dickson

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Abstract

Most of the current risk quantification techniques being applied in the field of Supply Chain Risk Management consider risk factors to be independent. This research considers risks as interdependent triggers, events and consequences. We propose a risk quantification framework based on Bayesian belief network modeling that is an effective method to capture the mentioned interaction between various risk factors. The conflicting incentives among stakeholders in a supply chain can jeopardize the success of a project and therefore, quantification of this category of risks named as 'Game theoretic risks' needs special consideration. We have assessed game theoretic risks in the development project of Boeing 787 aircraft. The game theoretic analysis captures uncertainty of the Tier-1 suppliers about the cost functions of each other and demonstrates that any uncertainty of information in a supply chain can adversely affect the intended outcome. Finally, we have designed a fair sharing partnership featuring continuous time domain and present value of money concept that aligns the conflicting incentives.
Original languageEnglish
Title of host publication10th Doctoral Workshop on Purchasing and Supply Management and IPSERA International Nodes Meeting
Subtitle of host publicationXXV Annual Scientific Meeting
Place of PublicationBologna
Number of pages15
Publication statusPublished - 15 Oct 2014
EventXXV Annual Scientific Meeting Associazione Italiana di Ingegneria Gestionale (RSA AiIG 2014) - Bologna, Italy
Duration: 16 Oct 201417 Oct 2014

Conference

ConferenceXXV Annual Scientific Meeting Associazione Italiana di Ingegneria Gestionale (RSA AiIG 2014)
Country/TerritoryItaly
CityBologna
Period16/10/1417/10/14

Keywords

  • supply chain risk management
  • game theoretic risk
  • game theory
  • Bayesian belief network

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