Finance

Supply chain

Physics

Psychological resilience

Pathology

Business

Supply chain risk management

Disease

Resilience (materials science)

Coronavirus disease 2019 (COVID-19)

Risk management

Infectious disease (medical specialty)

Psychotherapist

Pandemic

Optics

Data mining

Risk analysis (engineering)

Visibility

Marketing

Psychology

Process management

Data science

Business continuity

Visualization

Computer science

Big data

Computer security

Supply chain management

Service management

Medicine

Thermodynamics

Production Planning & Control

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.

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