Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility

Dmitry Ivanov (), Alexandre Dolgui (), Ajay Das () and Boris Sokolov ()
Additional contact information
Dmitry Ivanov: Berlin School of Economics and Law
Alexandre Dolgui: IMT Atlantique, LS2N, CNRS
Ajay Das: Zicklin School of Business, CUNY-Baruch
Boris Sokolov: Saint Petersburg Institute for Informatics and Automation of the RAS (SPIIRAS)

A chapter in Handbook of Ripple Effects in the Supply Chain, 2019, pp 309-332 from Springer

Abstract: Abstract The quality of model-based decision-making support strongly depends on the data, its completeness, fullness, validity, consistency, and timely availability. These requirements on data are of a special importance in supply chain (SC) risk management for predicting disruptions and reacting to them. Digital technology, Industry 4.0, Blockchain, and real-time data analytics have a potential to achieve a new quality in decision-making support when managing severe disruptions, resilience, and the Ripple effect. A combination of simulation, optimization, and data analytics constitutes a digital twin: a new data-driven vision of managing the disruption risks in SC. A digital SC twin is a model that can represent the network state for any given moment in time and allow for complete end-to-end SC visibility to improve resilience and test contingency plans. This chapter proposes an SC risk analytics framework and explains the concept of digital SC twins. It analyses perspectives and future transformations to be expected in transition toward cyber-physical SCs. It demonstrates a vision of how digital technologies and smart operations can help integrate resilience and lean thinking into a resileanness framework “Low-Certainty-Need” (LCN) SC.

Keywords: Supply chain dynamics; Supply chain risk management; Supply chain resilience; Industry 4.0; Additive manufacturing; Blockchain; Big data analytics; Ripple effect; Digital twin (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (22)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-14302-2_15

Ordering information: This item can be ordered from
http://www.springer.com/9783030143022

DOI: 10.1007/978-3-030-14302-2_15

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().