The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

By Dmitry A. Ivanov, Alexandre Dolgui, Boris V. Sokolov. International Journal of Production Research, 2018

1,653 citations recorded by the source corpus.

The impact of digitalisation and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two isolated areas, i.e. the impact of digitalisation on SC management (SCM) and the impact of SCM on the ripple effect control. To the best of our knowledge, this is the first study that connects business, information, engineering and analytics perspectives on digitalisation and SC risks. This paper does not pretend to be encyclopedic, but rather analyses recent literature and case-studies seeking to bring the discussion further with the help of a conceptual framework for researching the relationships between digitalisation and SC disruptions risks. In addition, it emerges with an SC risk analytics framework. It analyses perspectives and future transformations that can be expected in transition towards cyber-physical SCs. With these two frameworks, this study contributes to the literature by answering the questions of (1) what relations exist between big data analytics, Industry 4.0, additive manufacturing, advanced trace & tracking systems and SC disruption risks; (2) how digitalisation can contribute to enhancing ripple effect control; and (3) what digital technology-based extensions can trigger the developments towards SC risk analytics.

Subfields: Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/3827c32ad0e5cec099543c68b63c8a6b09a6ac91

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Citations in the corpus

Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic (2021, positive): “As was discussed in the context of BDA, simulation techniques can also be combined with AI (Cavalcante, Frazzon, Forcellini, & Ivanov, 2019).”
Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting (2020, background): “2014), and the role of emerging technologies like big data and predictive analytics (Dubey et al. 2019c; Singh and Singh, 2019; Ivanov et al. 2019) & blockchain technology (Min, 2019).”
Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model (2023, background): “Through digital technologies such as big data analytics and artificial intelligence, the supply chain can integrate operational and environmental data to quickly generate effective, visual decisions in response to disruptions (Ivanov et al., 2019).”
Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy (2020, background): “…radically transforming many business models (Y. Chen 2018; Dolgui et al. 2020), thus impacting not only production systems and supply chains (SCs) (Ivanov, Dolgui, and Sokolov 2018; Pournader et al. 2020; Queiroz, Fosso Wamba, et al. 2020), but also the society as a whole (Aste, Tasca, and Di…”
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): “We build our simulation model according to the above analysis and following the previously developed and validated simulation models for SC risk analysis (Ivanov, 2017a, 2017b, 2018a, 2019, 2020).”
Blockchain applications in supply chains, transport and logistics: a systematic review of the literature (2019, methodology): “• Technology : Supply chain, logistics and transport operations across all industries are experiencing a rapid transformation in their use of disruptive technologies (Ivanov, Dolgui, and Sokolov 2018).”
Supply chain risk management and artificial intelligence: state of the art and future research directions (2018, positive): “The untapped potential of Big Data Analytics and AI with regard to SCRM has also been identiﬁed by Ivanov, Dolgui, and Sokolov (2018), who present a digitalisation framework that envisions a cyber supply chain sitting on top of traditional supply chain analytics.”
Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain (2019, methodology): “Instead, different physical ﬁrms will offer services in supply, manufacturing, logistics, and sales which will result in the dynamic allocation of processes and dynamic SC structures forming a cyber-physical SC (Ivanov, Dolgui, and Sokolov 2019).”
Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management (2020, positive): “Recent works (Ivanov et al. 2019, Tang and Veelenturf, 2019, Olsen and Tomlin, 2020; Calzavara et al., 2020) showed that, within the I4.”
Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics (2020, background): “The majority of works in this ﬁeld of research deal with scheduling to determine the best possible strategies for robot movement (Kats and Levner 2009; Ivanov et al. 2016, 2018b; Sethi et al. 1992) .”
A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing (2019, background): “Ivanov et al. [9] show that data analytics can be used at the planning stage to identify supplier risk exposure and can help at the reactive stage to monitor and identify disruptions.”