Absorbing Markov Chain Approach to Modelling Disruptions in Supply Chain Networks

Abstract

Recent developments in the area of network science has encouraged researchers to adopt a topological perspective in modelling Supply Chain Networks (SCNs). While topological models can provide macro level insights into the properties of SCN systems, the lack of specificity due to high level of abstraction in these models limit their real-world applicability, especially in relation to assessing the impact on SCNs arising due to individual firm or supply channel level disruptions. In particular, beyond the topological structure, a more comprehensive method should also incorporate the heterogeneity of various components (i.e. firms and inter-firm links) which together form the SCN. To fill the above gap, this work proposes using the idea of absorbing Markov chains to model disruption impacts on SCNs. Since this method does not require path enumeration to identify the number of supply chains which form the SCN, it is deemed more efficient compared to the other traditional methods. KEY WORDS:supply chain network; absorbing markov chain; supply network disruptions AUTHORS:

Energy Resilience Impact of Supply Chain Network Disruption to Military Microgrids

“…The four states represent varying levels of operational capacity-fully operational (π 0 ), semi-disrupted (π 1 ), heavily-disrupted (π 2 ), and fully disrupted (π 3 ). As a Markovian process, predictions can be made of its future states based solely on its current state (i.e., memoryless) [133], with the transition probabilities associated with state changes reflecting nodal reaction and response. In this case, the initial shock due to disruption (λ 1 , λ 2 , or λ 3 ) may result in a regression to one of three states depending on severity.…”