Supply chain risk management and artificial intelligence: state of the art and future research directions

By George Baryannis, Sahar Validi, Samir Dani, Grigoris Antoniou. International Journal of Production Research, 2018

802 citations recorded by the source corpus.

Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.

Subfields: Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/b1c263c0ad29e0102972bb451d46d80755d4449b

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Citations in the corpus

Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review (2022, background): “Other studies have focused on identifying and classifying the different AI techniques used for risk management (Baryannis et al., 2019; Hamdi et al., 2018), and on evaluating different such techniques as part of supply chain resilience (Belhadi, Kamble, Fosso Wamba, et al.”
Information and digital technologies of Industry 4.0 and Lean supply chain management: a systematic literature review (2020, background): “0 enable the risk associated with the supply chain to be managed and minimized (Ivanot et al. 2019; Baryannis et al. 2019).”
Artificial intelligence in operations management and supply chain management: an exploratory case study (2021, methodology): “SCM strategies often depend on rapid and adaptive decision-making based on potentially significant, multidimensional data sources (Baryannis Validi, et al. 2019).”
Blockchain applications in supply chains, transport and logistics: a systematic review of the literature (2019, background): “The application of advanced analytics (Baryannis et al. 2018) to the data provided by blockchains can lead to better predictions of the impending risks in the supply chain and transport networks, and the ability to devise better customised plans to manage potential disruptions to these networks.”
Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations (2020, background): “The future of supply chain digitization will be driven by big data predictive analytics capability, powered by artificial intelligence (O’Leary, 2013; Loebbecke and Picot, 2015; Zhong et al., 2016; Kusiak, 2018; Ivanov et al., 2016, 2019b; Baryannis et al., 2019; Tortorella et al. 2020).”
Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation (2024, positive): “Due to the need for rapid and adaptive decision-making based on vast and complex data sources, supply chain risk management is a promising application area for AI (Baryannis et al.2019).”
Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic (2021, neutral): “Some scholars consider BDA part of AI and vice versa (Baryannis et al., 2019; Brintrup et al., 2020; Choi et al., 2018).”