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

2020/05/21 by Dmitry Ivanov, Alexandre Dolgui · 1,343 citations
Business, Management and Accounting · Engineering · #Big data #Business #Business continuity #Computer science #Computer security #Coronavirus disease 2019 (COVID-19) #Data mining #Data science #Digital Transformation in Industry #Marketing #Pandemic #Process management #Psychological resilience #Quality and Supply Management #Resilience (materials science) #Risk analysis (engineering) #Risk management #Service management #Supply Chain Resilience and Risk Management #Supply chain #Supply chain management #Supply chain risk management #Visibility #Visualization

paper · doi:10.1080/09537287.2020.1768450

published in Production Planning & Control 32(9), 775-788 (Informa UK Limited)

crossref issued 2020/05/21 · crossref published 2020/05/21 · crossref published-online 2020/05/21 · openalex publication_date 2020/05/21 · crossref created 2020/05/21 · crossref deposited 2021/06/05 · crossref published-print 2021/07/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05 · crossref indexed 2026/08/08

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.

Citations

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