An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic

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Rahman, Towfique
Taghikhah, Firouzeh
Paul, Sanjoy Kumar
Shukla, Nagesh
Agarwal, Renu
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2021
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Abstract

The current COVID-19 pandemic has hugely disrupted supply chains (SCs) in different sectors globally. The global demand for many essential items (e.g., facemasks, food products) has been phenomenal, resulting in supply failure. SCs could not keep up with the shortage of raw materials, and manufacturing firms could not ramp up their production capacity to meet these unparalleled demand levels. This study aimed to examine a set of congruent strategies and recovery plans to minimize the cost and maximize the availability of essential items to respond to global SC disruptions. We used facemask SCs as an example and simulated the current state of its supply and demand using the agent-based modeling method. We proposed two main recovery strategies relevant to building emergency supply and extra manufacturing capacity to mitigate SC disruptions. Our findings revealed that minimizing the risk response time and maximizing the production capacity helped essential item manufacturers meet consumers' skyrocketing demands and timely supply to consumers, reducing financial shocks to firms. Our study suggested that delayed implementation of the proposed recovery strategies could lead to supply, demand, and financial shocks for essential item manufacturers. This study scrutinized strategies to mitigate the demand-supply crisis of essential items. It further proposed congruent strategies and recovery plans to alleviate the problem in the exceptional disruptive event caused by COVID-19.

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Computers & Industrial Engineering

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158

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Subject

Business systems in context

Engineering

Information and computing sciences

Mathematical sciences

COVID-19 pandemic

Essential item

Recovery strategy

Risk and disruption

Supply chain resilience

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Rahman, T; Taghikhah, F; Paul, SK; Shukla, N; Agarwal, R, An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic, Computers & Industrial Engineering, 2021, 158, pp. 107401

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