Probabilistic analysis of supply chains resilience based on their characteristics using dynamic Bayesian networks

Student thesis: Doctoral Thesis

Abstract

There is an increasing interest in the resilience of supply chains given the growingawareness of their vulnerabilities to natural and man-made hazards. Contemporaryacademic literature considers, for example, so-called resilience enablers and strategies,such as improving the nature of collaboration and flexibility within the supply chain.Efforts to analyse resilience tend to view the supply chain as a complex system. Thepresent research adopts a distinctive approach to the analysis of supply resilience bybuilding formal models from the perspective of the responsible manager. DynamicBayesian Networks (DBNs) are selected as the modelling method since they are capableof representing the temporal evolution of uncertainties affecting supply. They alsosupport probabilistic analysis to estimate the impact of potentially hazardous eventsthrough time. In this way, the recovery rate of the supply chain under mitigation actionscenarios and an understanding of resilience can be obtained.The research is grounded in multiple case studies of manufacturing and retail supplychains, involving focal companies in the UK, Canada and Malaysia, respectively. Eachcase involves building models to estimate the resilience of the supply chain givenuncertainties about, for example, business continuity, lumpy spare parts demand andoperations of critical infrastructure. DBNs have been developed by using relevant datafrom historical empirical records and subjective judgement. Through the modellingpractice, It has been found that some SC characteristics (i.e. level of integration,structure, SC operating system) play a vital role in shaping and quantifying DBNs andreduce their elicitation burden. Similarly, It has been found that the static and dynamicdiscretization methods of continuous variables affect the DBNs building process.I also studied the effect of level of integration, visibility, structure and SC operatingsystem on the resilience level of SCs through the analysis of DBNs outputs. I found thatthe influence of the integration intensity on supply chain resilience can be revealedthrough understanding the dependency level of the focal firm on SC members resources. Ihave also noticed the relationship between the span of integration and the level ofvisibility to SC members. This visibility affects the capability of SC managers in the focalfirm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SCoperating system such as the inventory strategy could influence the intermediate ability ofSC to react to adverse events. By interpreting my case data in the light of the existingacademic literature, I can formulate some specific propositions.
Date of Award26 Oct 2016
Original languageEnglish
Awarding Institution
  • University Of Strathclyde

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