A cellular automata-based simulation study to optimize supply chain operations during sudden-onset disruption

Abstract

There are noticeable cases today that affect supply chain (SC) planning due to disasters. Such events, which occur without prior information, affect the overall decision-making in SC operations. The nature of such events can be mild and severe depending on the intensity of their characteristics. Moreover, recovering in such trying times becomes a primary objective in any business situation. The study proposes a simulation approach based on cellular automata that suggests an effective recovery strategy to minimize the impact of disruptions. The simulation tool analyzes the performance of firms that cooperate in a serial SC structure and exchange the items depending on ordering frequency. We consider two key performance indicators to gauge the overall sensitivity of the network against the disruption, namely, network strength and resource levels of the SC agents. Two disruption scenarios, namely, mild and severe, are considered, and the analysis highlights a gap of 10.94% in the network performance comparing the two situations simultaneously. A conceptual framework with algorithmic flowchart is presented in the paper to provide over-arching view of the study. The study observes the effectiveness of collaboration among the firms to overcome the disaster situation and identify the best recovery approach. The study quantifies the relationship between resource investment during such a difficult time versus the recovery phase. Though the simulation solution does not account for the implied uncertainty due to exogenous variables such as demand, the analysis provides substantial insights that are suitable to mitigate real-world SC decision-making problem due to disruptions.