Supply chain resilience in a post-pandemic era : a scenario-based simulation study

Abstract

This study investigates supply chain resilience (SCR) through a systematic literature review and a simulation-based case study. Through a systematic literature review, the study aims to understand the definiton of SCR and its evolution, the different metrics proposed to measure SCR and interventions identified or proposed to improve SCR. After identifying the potential metrics and interventions, a case study is used to simulate and compare the effectiveness of a range of interventions on the resilience of a supply chain under disruption. As part of the literature review, a total of 1480 articles were reviewed with 233 considered relevant to this study. The findings highlight a shift in SCR research from risk management and robustness to adaptive, technology-driven approaches, particularly post-COVID-19. Various resilience metrics exist, including recovery time, financial impact, and network-based indices, though no single standard metric prevails. Interventions were categorised into redundancy, flexibility, network design, supplier strategies, Industry 4.0 and 5.0 strategies, recovery, and other measures. The case study considered a global supply chain with component manufacturing and product assembly based in China, exporting five products to the rest of the world. Distribution centres (DC’s) in Australia, South Africa, Russia, the USA, Brasil and Belgium serve customers around the world. Ten interventions from the flexibility, recovery, redundancy, supplier and network intervention categories were tested across 30 disruption scenarios. The comparison of many different interventions make this study unique as other simulation studies reviewed in the literature often test only one intervention. After analysing the results, it was determined that Dual Sourcing, a supplier intervention, is the most effective in improving service levels and reducing recovery time after disruption. Despite these insights, the study’s high-level approach presents limitations, including favourable assumptions about supplier recovery and disruption probabilities, limited intervention testing, and the exclusion of detailed manufacturing processes. Future research should explore lower-level interventions, such as additive manufacturing, and incorporate more granular simulation models to enhance practical applicability.