A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0

TLDR

The COVID‑19 pandemic and subsequent recoveries have highlighted the urgent need for digital twins to map supply networks and ensure visibility. The study proposes and investigates a digital supply chain twin that models real‑time network states to manage disruption risks. The authors develop a hybrid model‑based and data‑driven digital twin that captures risk data, disruption modeling, and performance assessment to manage supply chain disruptions. The study demonstrates that the digital twin improves predictive and reactive decision‑making, enabling end‑to‑end visibility and business continuity for global companies.

Abstract

We theorize a notion of a digital supply chain (SC) twin – a computerized model that represents network states for any given moment in real time. We explore the conditions surrounding the design and implementation of the digital twins when managing disruption risks in SCs. The combination of model-based and data-driven approaches allows uncovering the interrelations of risk data, disruption modeling, and performance assessment. The SC shocks and adaptations amid the COVID-19 pandemic along with post-pandemic recoveries provide indisputable evidences for the urgent needs of digital twins for mapping supply networks and ensuring visibility. The results of this study contribute to the research and practice of SC risk management by enhancing predictive and reactive decisions to utilize the advantages of SC visualization, historical disruption data analysis, and real-time disruption data and ensure end-to-end visibility and business continuity in global companies.