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Boris Sokolov
Researcher at Russian Academy of Sciences
Publications - 184
Citations - 6505
Boris Sokolov is an academic researcher from Russian Academy of Sciences. The author has contributed to research in topics: Supply chain & Scheduling (production processes). The author has an hindex of 32, co-authored 178 publications receiving 4574 citations. Previous affiliations of Boris Sokolov include Riga Technical University & Saint Petersburg State University.
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The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics
TL;DR: This paper 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 and emerges with an SC risk analytics framework.
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Ripple effect in the supply chain: an analysis and recent literature
TL;DR: The reasons and mitigation strategies for the ripple effect in the supply chain are observed and a ripple effect control framework that includes redundancy, flexibility and resilience analysis is presented.
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The Ripple effect in supply chains: trade-off 'efficiency-flexibility-resilience' in disruption management
TL;DR: In this article, the authors present the Ripple effect in supply chains and summarise recent developments in the field of supply chain (SC) disruption management from a multi-disciplinary perspective, identifying gaps in current research and delineating future research avenues.
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A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0
TL;DR: In this paper, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented, which is based on a non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem.
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Literature review on disruption recovery in the supply chain
TL;DR: This study analyzes state-of-the-art research streams on supply chain design and planning with both disruptions and recovery considerations and classifies existing research streams and application areas of different quantitative methods subject to different disruption risks and recovery measures.