Modeling supply network reliability and resilience using dynamic Bayesian networks

Mouhamad Shaker Ali Agha; Robert van der Meer; Lesley Walls

Modeling supply network reliability and resilience using dynamic Bayesian networks

Mouhamad Shaker Ali Agha, Robert van der Meer, Lesley Walls

Management Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

Abstract

The resilience of supply networks to catastrophic events has received increased prominence following the effects on global supply of intentional and natural hazards, such as 9/11 and the Japanese tsunami respectively. We examine how supply network resilience relates to, for example, reliability, robustness and risk, in order to consider how we might construct models to support assessment of supply network resilience. We explore the use of Dynamic Bayesian Networks to model uncertainties in events affecting supply and to examine how these uncertainties propagate through supply networks over time. Using simple examples, we examine how such a modeling approach can be used to assess the impact of different strategies intended to improve supply networks resilience.

Original language	English
Title of host publication	Pioneering Supply Chain Design
Subtitle of host publication	A Comprehensive Insight into Emerging Trends, Technologies and Applications
Editors	Thorsten Blecker, Wolfgang Kersten, Christian M. Ringle
Place of Publication	Lohmar, Germany
Pages	249-264
Number of pages	16
Publication status	Published - 1 Aug 2012

Keywords

dynamic bayesian network (DBN)
supply network
supply network reliability

Cite this

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title = "Modeling supply network reliability and resilience using dynamic Bayesian networks",

abstract = "The resilience of supply networks to catastrophic events has received increased prominence following the effects on global supply of intentional and natural hazards, such as 9/11 and the Japanese tsunami respectively. We examine how supply network resilience relates to, for example, reliability, robustness and risk, in order to consider how we might construct models to support assessment of supply network resilience. We explore the use of Dynamic Bayesian Networks to model uncertainties in events affecting supply and to examine how these uncertainties propagate through supply networks over time. Using simple examples, we examine how such a modeling approach can be used to assess the impact of different strategies intended to improve supply networks resilience. ",

keywords = "dynamic bayesian network (DBN), supply network, supply network reliability",

author = "{Ali Agha}, {Mouhamad Shaker} and {van der Meer}, Robert and Lesley Walls",

year = "2012",

month = aug,

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language = "English",

isbn = "9783844101812",

pages = "249--264",

editor = "Thorsten Blecker and Wolfgang Kersten and Ringle, {Christian M.}",

booktitle = "Pioneering Supply Chain Design",

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Modeling supply network reliability and resilience using dynamic Bayesian networks. / Ali Agha, Mouhamad Shaker ; van der Meer, Robert ; Walls, Lesley.

Pioneering Supply Chain Design: A Comprehensive Insight into Emerging Trends, Technologies and Applications. ed. / Thorsten Blecker; Wolfgang Kersten; Christian M. Ringle. Lohmar, Germany, 2012. p. 249-264.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Modeling supply network reliability and resilience using dynamic Bayesian networks

AU - Ali Agha, Mouhamad Shaker

AU - van der Meer, Robert

AU - Walls, Lesley

PY - 2012/8/1

Y1 - 2012/8/1

N2 - The resilience of supply networks to catastrophic events has received increased prominence following the effects on global supply of intentional and natural hazards, such as 9/11 and the Japanese tsunami respectively. We examine how supply network resilience relates to, for example, reliability, robustness and risk, in order to consider how we might construct models to support assessment of supply network resilience. We explore the use of Dynamic Bayesian Networks to model uncertainties in events affecting supply and to examine how these uncertainties propagate through supply networks over time. Using simple examples, we examine how such a modeling approach can be used to assess the impact of different strategies intended to improve supply networks resilience.

AB - The resilience of supply networks to catastrophic events has received increased prominence following the effects on global supply of intentional and natural hazards, such as 9/11 and the Japanese tsunami respectively. We examine how supply network resilience relates to, for example, reliability, robustness and risk, in order to consider how we might construct models to support assessment of supply network resilience. We explore the use of Dynamic Bayesian Networks to model uncertainties in events affecting supply and to examine how these uncertainties propagate through supply networks over time. Using simple examples, we examine how such a modeling approach can be used to assess the impact of different strategies intended to improve supply networks resilience.

KW - dynamic bayesian network (DBN)

KW - supply network

KW - supply network reliability

UR - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4162852

M3 - Conference contribution book

SN - 9783844101812

SP - 249

EP - 264

BT - Pioneering Supply Chain Design

A2 - Blecker, Thorsten

A2 - Kersten, Wolfgang

A2 - Ringle, Christian M.

CY - Lohmar, Germany

ER -

Modeling supply network reliability and resilience using dynamic Bayesian networks

Abstract

Keywords

Other files and links

Fingerprint

Dynamic Bayesian network modelling of supply chain resilience: learning from multiple cases

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