Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience

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

307 citations recorded by the source corpus.

This study suggests a new approach to supply chain (SC) disruption risk management where SC behaviour is less dependent on the certainty of our knowledge about the environment and its changes. The unpredictability of the occurrence of disruption and its magnitude suggests that designing SCs with a low need for ‘certainty’ may be as important, if not more so, than predetermined disruption control strategies. In this setting, this study calls for the development of a new perspective in SC disruption management, i.e. low-certainty-need (LCN) SCs. A number of principles and concepts is derived in recent, relevant literature to structure the characteristics of the LCN framework and its management. Structural variety, process flexibility, and parametrical redundancy are identified as key LCN SC characteristics that ensure efficient disruption resistance as well as recovery resource allocation. Two efficiency capabilities of the LCN SC are shown, i.e. low need for uncertainty consideration in planning decisions and low need for recovery coordination efforts based on a combination of lean and resilient elements. The results allow the identification of an LCN SC framework, concepts and technologies for its implementation as well as missing themes and new research questions which contribute to a better understanding of SC disruption risks. Special focus is directed on the digital technology usage in the LCN framework implementation.

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Citations in the corpus

Reconfigurable supply chain: the X-network (2020, unknown): “Ivanov and Dolgui (2019) point to several research gaps in the SC resilience area as follows: “First, structural SC design patterns need to be identified that allow for both efficient robustness and recoverability.”
Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics (2020, background): “…page of the article 2013; Dubey et al. 2018; Ivanov et al. 2018a) and particularly Industry 4.0 has also been iden-tiﬁed as a major determinant in improving production ﬂexibility (Cavalcantea et al. 2019; Dubey et al. 2019; Frank et al. 2019; Ivanov et al. 2016, 2019a,b; Ivanov and Dolgui 2019).”
Supply Chain Viability and the COVID-19 pandemic: a conceptual and formal generalisation of four major adaptation strategies (2021, methodology): “With this combination of resilience and efficiency (in line with the lean resilience concept proposed in Ivanov and Dolgui (2019) and extended in Ivanov (2021b)), AGCO has been very agile in navigating the COVID-19 outbreak by substituting sourcing.”
Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting (2020, background): “…in recent years, a majority of developing economies have been either deliberately designed, or have evolved, to operate efficiently and effectively in routine environments characterised by stability and predictability (Gibson and Tarrant 2010; Zhang, Wang, and Zhu 2019; Ivanov and Dolgui 2019).”
Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic (2021, unknown): “As was discussed in the context of BDA, simulation techniques can also be combined with AI (Cavalcante, Frazzon, Forcellini, & Ivanov, 2019).”
Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain† (2019, background): “…and Holyst 2012; Ivanov, Sokolov, and Dolgui 2014a; Ivanov, Sokolov, and Pavlov 2014b; Lu, Ran, and Shen 2015; Ivanov et al. 2017, 2019; Ivanov and Dolgui 2018; Ojha et al. 2018) describes disruption propagation in the SC, the resulting SC structural dynamics, and the performance impact of this…”
Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic (2020, background): “, agile, lean, sustainable, resilient, and digital SC) (Altay et al. 2018; Bier et al. 2020; Blackhurst et al. 2011; Brandenburg and Rebs 2015; Choi et al. 2018; Das et al. 2006; Dolgui et al. 2018; Dubey et al. 2015; Govindan et al. 2016; DuHadway et al. 2019; Hosseini et al. 2019a; Ivanov 2018b; I”
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, unknown): “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).”
Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model (2023, background): “resources and absorb some of the disruptions when the risk occurs (Ali et al., 2022; Knemeyer et al., 2009; Dabhilkar et al., 2016; Ivanov and Dolgui, 2018; Sheffi and Rice, 2005).”
A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing (2019, background): “Ivanov and Dolgui [56] elaborate on the importance of increasing SC re-133 silience in eﬃcient ways, i.e., achieving SC resileanness (resilient + lean).”
Supply chain disruptions and resilience: a major review and future research agenda (2021, background): “International Journal of Production Research Ivanov and Dolgui (2019) It presents a new conceptual approach to SC design with a low need for certainty, less dependent on the unpredictability of disruptive changes 6 Ripple effect in the supply chain: an analysis and recent literature.”