Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.618878
Title: A Bayesian belief network modelling process for systemic supply chain risk
Author: Leerojanaprapa, Kanogkan
ISNI:       0000 0004 5355 607X
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2014
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Abstract:
To effectively manage risk in supply chains, it is important to understand the interrelationships between risk events that might affect the flow of material, products and information within the chain. Typical supply chain risk management tends to treat events as if they are independent and so fail to capture the systemic nature of supply chain risks. This thesis addresses this shortcoming by developing a quantitative modelling process to support systemic supply chain risk analysis. Bayesian Belief Network (BBN) models are able to capture both the aleatory and epistemic uncertainties associated with supply chains and to represent probabilistic dependency relationships. A visual modelling process, grounded in the theory of BBN and the decision context of supply chain risk management, is developed to capture the knowledge and probability judgements of relevant stakeholders. An experiment has been conducted to evaluate alternative approaches to structuring a BBN model for supply risk. It is found that building causal maps provides a good basis for translating stakeholder cause-effect knowledge about the supply chain risks into a formal graphical probability model, which underpins the BBN. The modelling process has been evaluated through a longitudinal case for the hospital medicine supply of NHS Greater Glasgow & Clyde. A BBN model has been developed in collaboration with relevant stakeholders who have expertise in all or part of the medicine supply chain. The perceptions of these stakeholders about the modelling process and results generated have been formally gathered and analysed. The BBN model of the medicine supply chain has provided insight into risks not captured by conventional risk management methods and supported deeper understanding of risk through exploration of modelling scenarios. Analysis of stakeholder evaluation of the modelling process provided valuable insights into the operationalization of BBN modelling for supply risk and has informed the final modelling process developed through this research.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.618878  DOI: Not available
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