Modelling and managing systemic risks in supply chains

Kanogkan Leerojanaprapa; Lesley Walls; Robert van der Meer

Modelling and managing systemic risks in supply chains

Kanogkan Leerojanaprapa, Lesley Walls, Robert van der Meer

Management Science

Research output: Contribution to conference › Paper › peer-review

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Abstract

A structured review of the supply chain and risk management literature supports an analysis of the sources and types of risks anticipated in supply chains and networks. We discuss alternative modelling approaches, such as Bayesian Belief Nets (BBN), System Dynamics, Fault and Event Trees, which are evaluated against the criteria characterizing systemic risks that emerge from the literature review. Finally, we briefly present an empirical pilot case study is conducted with a public sector organization in charge of a pharmaceutical distribution network to explore the feasibility of a BBN modelling approach.

Original language	English
Publication status	Published - 4 Jul 2011
Event	18th International Conference of the European Operations Management Association, 3-6 July 2011, University of Cambridge - Cambridge, United Kingdom Duration: 3 Jul 2011 → 6 Jul 2011

Conference

Conference	18th International Conference of the European Operations Management Association, 3-6 July 2011, University of Cambridge
Country/Territory	United Kingdom
City	Cambridge
Period	3/07/11 → 6/07/11

Keywords

supply chains
Bayesian belief nets
risk management

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Leerojanaprapa-etal-EurOMA-2011-Modelling-and-managing-systemic-risks-in-supply-chainsAccepted author manuscript, 286 KB

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title = "Modelling and managing systemic risks in supply chains",

abstract = "A structured review of the supply chain and risk management literature supports an analysis of the sources and types of risks anticipated in supply chains and networks. We discuss alternative modelling approaches, such as Bayesian Belief Nets (BBN), System Dynamics, Fault and Event Trees, which are evaluated against the criteria characterizing systemic risks that emerge from the literature review. Finally, we briefly present an empirical pilot case study is conducted with a public sector organization in charge of a pharmaceutical distribution network to explore the feasibility of a BBN modelling approach.",

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T1 - Modelling and managing systemic risks in supply chains

AU - Leerojanaprapa, Kanogkan

AU - Walls, Lesley

AU - van der Meer, Robert

PY - 2011/7/4

Y1 - 2011/7/4

N2 - A structured review of the supply chain and risk management literature supports an analysis of the sources and types of risks anticipated in supply chains and networks. We discuss alternative modelling approaches, such as Bayesian Belief Nets (BBN), System Dynamics, Fault and Event Trees, which are evaluated against the criteria characterizing systemic risks that emerge from the literature review. Finally, we briefly present an empirical pilot case study is conducted with a public sector organization in charge of a pharmaceutical distribution network to explore the feasibility of a BBN modelling approach.

AB - A structured review of the supply chain and risk management literature supports an analysis of the sources and types of risks anticipated in supply chains and networks. We discuss alternative modelling approaches, such as Bayesian Belief Nets (BBN), System Dynamics, Fault and Event Trees, which are evaluated against the criteria characterizing systemic risks that emerge from the literature review. Finally, we briefly present an empirical pilot case study is conducted with a public sector organization in charge of a pharmaceutical distribution network to explore the feasibility of a BBN modelling approach.

KW - supply chains

KW - Bayesian belief nets

KW - risk management

UR - http://www.euroma2011.org/

M3 - Paper

T2 - 18th International Conference of the European Operations Management Association, 3-6 July 2011, University of Cambridge

Y2 - 3 July 2011 through 6 July 2011

ER -

Modelling and managing systemic risks in supply chains

Abstract

Conference

Keywords

Access to Document

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A bayesian network model with epistemic uncertainty: analysis of a medicine supply chain risk

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