4.7 Article

Artificial intelligence in supply chain and operations management: a multiple case study research

Journal

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2023.2232050

Keywords

Artificial intelligence; operations management; supply chain management; SCOR; industry 4; 0; multiple-case study; >

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This study examines the application of artificial intelligence (AI) in operations and supply chain management (OSCM). Through a multiple case study, it identifies the benefits and barriers of implementing AI in OSCM processes. The results show that AI can enhance competitiveness by reducing costs and lead times, improving service levels, quality, safety, and sustainability. However, obstacles such as data quality assurance, lack of specific skills, high investments, unclear economic benefits, and limited experience in cost analysis for AI projects were also identified. The study offers guidance for practitioners and provides a foundation for future research.
Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies' competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research.

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