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

By Dmitry A. Ivanov, Alexandre Dolgui. Production planning & control (Print), 2020

1,372 citations recorded by the source corpus.

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.

Subfields: Industrial and Manufacturing Engineering, Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/fd278d4f858188ea7563f07defdda80704349dbc

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Citations in the corpus

Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic (2020, methodology): “Ecological modelling can shed light on underlying behaviors of collective survivals (Demirel et al. 2019, Ivanov and Dolgui 2020b).”
Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic (2021, positive): “Traditional modeling techniques (e.g., Bayesian networks and Petri nets) can also be combined with ML to develop and understand a model’s structure more efficiently (Blos, da Silva, & Wee, 2018; Hosseini & Ivanov, 2020).”
Reconfigurable supply chain: the X-network (2020, positive): “Recent research theorized a notion of digital SC twins – computerized models that represents the network state for any given moment in real time (Ivanov and Dolgui 2020).”
Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study (2020, neutral): “One example is the COVID-19 pandemic, which has recently threatened global and inter-twined supply networks (Ivanov, 2020; Ivanov and Dolgui, 2020a).”
Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case (2020, positive): “The simulation models consider logical and randomness constraints, such as randomness in disruptions, inventory, production, sourcing, and shipment control policies, and gradual capacity degradation and recovery (Ivanov and Dolgui, 2020).”
A meta-analytical review of antecedents and outcomes of firm resilience (2021, background): “This is particularly relevant in the current COVID-19 context, when the entire world faced serious disruptions (Ivanov and Dolgui, 2020; Belhadi et al., 2021).”
Supply Chain Viability and the COVID-19 pandemic: a conceptual and formal generalisation of four major adaptation strategies (2021, background): “…for SC adaptation Technology adoption in SCs has reached an unprecedented scale, entailing inherent adaptability inside the SCs themselves (Blackhurst, Das, and Ivanov 2021; Dubey et al. 2021; Frazzon, Freitag, and Ivanov 2021; Ivanov and Dolgui 2020a; Ivanov, Dolgui, and Sokolov 2019).”
The Industry 5.0 framework: viability-based integration of the resilience, sustainability, and human-centricity perspectives (2022, background): “For example, it is concerned with intertwined supply networks ‘that encapsulate entireties of interconnected supply chains, which, in their integrity, secure the provision of society and markets with goods and services’ (Ivanov and Dolgui 2020).”
Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics (2020, background): “Thereby, the emerging technologies, such as cloud operations or industrial Artiﬁcial Intelligence, allow for new ﬂexible production systems (Calzavara et al. 2018; Dubey et al. 2018; Panetto et al. 2019; Wamba and Akter 2019; Ivanov and Dolgui 2020).”