Planning Production Systems Resilience by Linking Supply Chain Operational Factors

Kanchan Das
East Carolina University, USA

R.S. Lashkari
University of Windsor, Canada

A mathematical model is proposed to plan production system resilience in a supply chain to overcome production related disruptions using appropriate operational factors at optimum cost. The research considers internally generated disruption risks due to supply, quality management, and plant reliability failures; and externally generated disruption risks from natural calamities. The production system of a supply chain has several options to utilize controllable operational factors to inhibit or mitigate the risks it faces. The operational factors are planned to mitigate the natural calamity disruptions and to inhibit the internally generated risks to create resilience. Since supply chain outcomes may be considered to be the net effect of complex interactions among several operational factors and resources, appropriate linkage of the operational factors is the key to select the right option(s) to create system resilience to contain risks and disruptions. Each operational factor can influence more than one supply chain outcome or relevant risk; on the other hand, containment of each risk may need contributions from several operational factors. Selection of the suitable controllable operational factors would be the most viable option for resilience creation. The analysis of the model outcomes establishes the effectiveness of the proposed model based procedure in creating production system resilience within an optimum cost using controllable operational factors. The operational factors used are: supplier flexibility; plant capacity flexibility; designating suppliers by a quality metric based evaluation procedure to have ensured quality inputs; quality metrics based plant capability determination and allocating production to capable plants to have ensured quality products. A numerical example illustrates the applicability of the model.

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This paper has been downloaded 1062 times since published. The persistent DOI of this paper is DOI:10.31387/oscm0270184.