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

Research output: Contribution to conferenceAbstract

Abstract

While there is growing interest in supply chain resilience, conceptualization of the main constructs is discussed at a high level in the literature. We aim to learn more about what supply chain resilience means through modelling, specifically using Dynamic Bayesian Networks (DBNs). DBNs are directed acyclic graphs for reasoning under uncertainty through time. A DBN is capable of using partial knowledge about one variable to update the uncertainty about other variables in the model. In principle, DBNs present a possible model class for analysing resilience because they can capture the dynamic uncertain behaviour of a supply chain due to the effects of potential hazards. Between 2013-15 we have conducted multiple cases for four distinct manufacturing and retail supply chains with focal companies based in the UK, Canada and Malaysia. We present the characteristics of each chain and the insights gained through application of our DBN modelling protocol. Our analysis has generated better understanding of the relative importance of resilience enablers reported in the literature and the propagation of uncertainties on predictions of resilience under different risk scenarios. Reflecting on our modelling methodology has generated insights into the benefits and limitations of DBNs for modelling supply chain resilience.

Conference

Conference27th European Conference on Operational Research (EURO XXVII)
CountryUnited Kingdom
CityGlasgow
Period12/07/1515/07/15

Fingerprint

Modeling
Supply chain
Bayesian networks
Resilience
Uncertainty
Conceptualization
Canada
Supply chain modeling
Propagation
Scenarios
Hazard
Directed acyclic graph
Prediction
Relative importance
Modeling methodology
Malaysia
Enablers
Retail
Manufacturing

Keywords

  • Bayesian network model
  • supply chain resilience
  • dynamic Bayesian network (DBN)
  • multiple case studies
  • dynamic supply chain behaviour

Cite this

Ali Agha, M. S., van der Meer, R., & Walls, L. (2015). Dynamic Bayesian network modelling of supply chain resilience: learning from multiple cases. 296-296. Abstract from 27th European Conference on Operational Research (EURO XXVII), Glasgow, United Kingdom.
Ali Agha, Mouhamad Shaker ; van der Meer, Robert ; Walls, Lesley. / Dynamic Bayesian network modelling of supply chain resilience : learning from multiple cases. Abstract from 27th European Conference on Operational Research (EURO XXVII), Glasgow, United Kingdom.1 p.
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keywords = "Bayesian network model, supply chain resilience, dynamic Bayesian network (DBN), multiple case studies, dynamic supply chain behaviour",
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note = "27th European Conference on Operational Research (EURO XXVII) ; Conference date: 12-07-2015 Through 15-07-2015",

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Ali Agha, MS, van der Meer, R & Walls, L 2015, 'Dynamic Bayesian network modelling of supply chain resilience: learning from multiple cases' 27th European Conference on Operational Research (EURO XXVII), Glasgow, United Kingdom, 12/07/15 - 15/07/15, pp. 296-296.

Dynamic Bayesian network modelling of supply chain resilience : learning from multiple cases. / Ali Agha, Mouhamad Shaker; van der Meer, Robert; Walls, Lesley.

2015. 296-296 Abstract from 27th European Conference on Operational Research (EURO XXVII), Glasgow, United Kingdom.

Research output: Contribution to conferenceAbstract

TY - CONF

T1 - Dynamic Bayesian network modelling of supply chain resilience

T2 - learning from multiple cases

AU - Ali Agha, Mouhamad Shaker

AU - van der Meer, Robert

AU - Walls, Lesley

PY - 2015/7/12

Y1 - 2015/7/12

N2 - While there is growing interest in supply chain resilience, conceptualization of the main constructs is discussed at a high level in the literature. We aim to learn more about what supply chain resilience means through modelling, specifically using Dynamic Bayesian Networks (DBNs). DBNs are directed acyclic graphs for reasoning under uncertainty through time. A DBN is capable of using partial knowledge about one variable to update the uncertainty about other variables in the model. In principle, DBNs present a possible model class for analysing resilience because they can capture the dynamic uncertain behaviour of a supply chain due to the effects of potential hazards. Between 2013-15 we have conducted multiple cases for four distinct manufacturing and retail supply chains with focal companies based in the UK, Canada and Malaysia. We present the characteristics of each chain and the insights gained through application of our DBN modelling protocol. Our analysis has generated better understanding of the relative importance of resilience enablers reported in the literature and the propagation of uncertainties on predictions of resilience under different risk scenarios. Reflecting on our modelling methodology has generated insights into the benefits and limitations of DBNs for modelling supply chain resilience.

AB - While there is growing interest in supply chain resilience, conceptualization of the main constructs is discussed at a high level in the literature. We aim to learn more about what supply chain resilience means through modelling, specifically using Dynamic Bayesian Networks (DBNs). DBNs are directed acyclic graphs for reasoning under uncertainty through time. A DBN is capable of using partial knowledge about one variable to update the uncertainty about other variables in the model. In principle, DBNs present a possible model class for analysing resilience because they can capture the dynamic uncertain behaviour of a supply chain due to the effects of potential hazards. Between 2013-15 we have conducted multiple cases for four distinct manufacturing and retail supply chains with focal companies based in the UK, Canada and Malaysia. We present the characteristics of each chain and the insights gained through application of our DBN modelling protocol. Our analysis has generated better understanding of the relative importance of resilience enablers reported in the literature and the propagation of uncertainties on predictions of resilience under different risk scenarios. Reflecting on our modelling methodology has generated insights into the benefits and limitations of DBNs for modelling supply chain resilience.

KW - Bayesian network model

KW - supply chain resilience

KW - dynamic Bayesian network (DBN)

KW - multiple case studies

KW - dynamic supply chain behaviour

UR - https://euro2015.euro-online.org/

M3 - Abstract

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ER -

Ali Agha MS, van der Meer R, Walls L. Dynamic Bayesian network modelling of supply chain resilience: learning from multiple cases. 2015. Abstract from 27th European Conference on Operational Research (EURO XXVII), Glasgow, United Kingdom.