An integrated dynamic flow model for supply chain risk analysis

Ke Sun, James T. Luxhøj

Research output: Contribution to conferencePaperpeer-review

Abstract

A supply chain is an integrated system with dynamic flows of capital, goods, information and people. With the occurrence of a disturbance and its subsequent uncertainty, risks in supply chains obstruct the proper functioning of each system flow. Various risk analysis models have been applied to supply chains to facilitate decision makers with understanding and mitigating these risks. A Bayesian Belief Network (BBN) is one of the modeling approaches that provide a systemic conditional probabilistic view on risk analysis through a directed acyclic graph. However, because the factor of time is one of the crucial elements in the supply chain domain, a BBN is limited in its application due to the lack of temporal factors in this model. A Dynamic Bayesian Network (DBN) offers a solution that enables time dependencies in probability models. Nevertheless, the study is still constrained with the separation of risk models and dynamic flows in a supply chain. System Dynamics (SD) is a simulation tool that specializes in modeling forward flows and feedbacks in a complex socio-technical system. By utilizing the essence of DBN and SD, this paper proposes a Dynamic Flow Bayesian Network (DFBN) to offer a comprehensive methodology for supply chain risk analysis.

Original languageAmerican English
Pages83-88
Number of pages6
StatePublished - 2020
Event2016 Industrial and Systems Engineering Research Conference, ISERC 2016 - Anaheim, United States
Duration: May 21 2016May 24 2016

Conference

Conference2016 Industrial and Systems Engineering Research Conference, ISERC 2016
Country/TerritoryUnited States
CityAnaheim
Period5/21/165/24/16

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Keywords

  • Dynamic Bayesian Network
  • Risk Analysis
  • Supply Chain
  • System Dynamics

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