On October 4th, 2024, the research team of the RESILIENCE project was hosted at the University of Salerno, partner institution of the project carrying out research in the area of learning and forgetting models for training and upskilling.
Supply chain (SC) resilience takes network connectivity and performance persistence perspectives, which supplement each other. The extant literature has developed a large body of knowledge about SC resilience’s network and performance indicators. However, we are unaware of any published research combining these two perspectives in resilience assessment. Therefore, this study aims to advance our understanding of how network and performance indicators can mutually enhance each other when analysing SC resilience as both a system property (quality) and an outcome (quantity). The unique contribution of our study is a combined use of network science and discrete-event simulation allowing for mixed-method grounded integration of static and dynamic views of supply chain resilience. Using node degrees as network indicators and on-time delivery, fulfilment rate, and time-to-recovery as performance indicators, we examine reactions of these indicators to a disruption to the sourcing strategies of three different flexibility degrees. We observe that network science methods can be used to identify disruption existence while simulation methods allow quantifying performance impact. We show how and when the combined application of network and performance indicators can inform decision-makers about SC resilience, and propose a generalised guideline for a practical implementation of the developed approach. Our main conclusion is that SC resilience-assessment models can be mutually enhanced by including network characteristics and process dynamics through a combination of network analysis and simulation.
Read the full article here: https://www.sciencedirect.com/science/article/pii/S1478409225000238
