Network science indicators and their relationship with performance during disruptions: A case study

Interest in supply chain (SC) resilience has increased in the wake of the pandemic and other crises, including those related to political and environmental instability. The literature offers some contributions to proactive indicators to assess the resilience of a system before a disruption occurs. Other studies provide metrics to assess resilience from the reactive perspective after the onset or end of a disruption. This paper examines the application of some proactive indicators from network science to some post-disruption measure of resilience, especially how these measure evolves as a function of time. We examine this by testing different supply chain designs against disrupted scenarios and using data from a real-life industry. The focus is on service level as a performance metric. The tested indicators correlate well with performance loss but show a limited ability to correlate with metrics representing SC dynamics. The practical contribution of this paper is an approach to measure SC resilience as an inherent property of the system, which can aid in designing future SCs, rather than measuring resilience as a response to a disruptive event. The paper also provides theoretical contributions, including the further validation of certain indicators from the literature and the identification of research areas in need of new metrics.