Graph-enabled decision support system for alternative supplier selection in multi-disruption scenarios

Many enterprises today grapple with supply disruptions in a tumultuous post-COVID-19 environment caused by black swan events, resulting in lost sales and hindering growth. Supply networks, previously optimized for cost and time efficiencies, are overstretched, and are unable to cope with these dynamic events. To overcome these challenges and boost supply chain resilience, this study proposes a graph-enabled decision support system for alternative supplier selection to mitigate supply-related disruptions. Semantic modeling of complex transportation networks and custody chains are included within supplier network domain knowledge, disruption mitigation workflows, and others to support stakeholders in identifying the best supplier alternatives with multi-aspect considerations such as production and distribution factors. An industrial case study of fast-moving consumer goods (FMCG) is used to illustrate this system, which can reduce business impacts and provide faster response times toward multi-disruption mitigation.