Abstract

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.

Keywords

AnalyticsSupply chainControl (management)Conceptual frameworkKnowledge managementBusinessAudit riskRippleSupply chain managementData scienceComputer scienceRisk analysis (engineering)EngineeringMarketingAccountingSociology

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Publication Info

Year
2018
Type
article
Volume
57
Issue
3
Pages
829-846
Citations
1672
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1672
OpenAlex
105
Influential

Cite This

Dmitry Ivanov, Alexandre Dolgui, Boris Sokolov (2018). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research , 57 (3) , 829-846. https://doi.org/10.1080/00207543.2018.1488086

Identifiers

DOI
10.1080/00207543.2018.1488086

Data Quality

Data completeness: 81%