When preemptive risk mitigation is insufficient: The effectiveness of continuity and resilience techniques during COVID-19

• 成果类型: Article
• 署名作者: Dohmen, Anne E.; Merrick, Jason R. W.; Saunders, Lance W.; Stank, Theodore P.; Goldsby, Thomas J.
• 署名单位: University of Tennessee System; University of Tennessee Knoxville; Virginia Commonwealth University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13677
• 发表日期: 2023
• 页码: 1529-1549
• 关键词: covid-19; resilience; Supply Chain Disruptions; supply chain planning; supply chain risk

摘要

The extreme demand volatility caused by corona virus 2019 (COVID-19) overwhelmed most preemptive measures enacted by firms to mitigate disruptions in their supply chains. This led to the rapid implementation of reactive measures to provide business continuity. The operations and supply chain management literature has grouped these reactive techniques into those that involve the reconfiguration of firm resources and those that involve changes to its decision-making processes. Unfortunately, little of this work has empirically assessed the efficacy of individual reactive techniques on business continuity after the onset of a disruption such as COVID-19. The research reported in this paper addresses this gap through an experimental design and discrete-event simulation to empirically test the impact on firm outcomes of resource reconfiguration techniques versus those related to adaptive decision-making processes. Data from a canned foods manufacturer is used to populate the simulation and isolate the impact of these reactive changes implemented in March 2020 at the beginning of the COVID-19 disruption on the attainment of business continuity in the 3 months following the COVID-19 disruption. The results show that for this company, decision-making changes-specifically changes to planning process cadence and time horizon-had more impact on business continuity than those focused on resource reconfiguration (increasing capacity through stock keeping unit (SKU) prioritization and increasing the number of shifts). These results, in addition to qualitative data collected from company executives to provide context for the modeling results, are used to provide insights that are generalizable to many firms.