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

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:

• supply chain dynamics
• supply chain risk management
• supply chain resilience
• supply chain design
• supply chain engineering
• Industry 4.0
• additive manufacturing
• blockchain
• big data analytics
• ripple effect

Acknowledgements

The authors thank associate editor and two anonymous reviewers for their valuable comments and improvement suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Dmitry Ivanov http://orcid.org/0000-0003-4932-9627

Alexandre Dolgui http://orcid.org/0000-0003-0527-4716

Additional information

Funding

This research was partially supported by RFBS grants (No. 17-29-07073-ofi-m, section 2), by Russian Science Foundation (project No. 16-19-00199, section 5), state assignments of the Ministry of education and science No. 2.3135.2017/4.6 (section 1), within the budget theme #0073–2018–0003 (sections 3, 4, 6).