Artificial Intelligence in Supply Chain and Operations Management: A Multiple Case Study Research

Cannas, Violetta Giada; Ciano, Maria Pia; Saltalamacchia, Mattia; Secchi, Raffaele

Abstract

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.

Citation

Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2023). Artificial Intelligence in Supply Chain and Operations Management: A Multiple Case Study Research. International Journal of Production Research, https://doi.org/10.1080/00207543.2023.2232050

• Journal Article Type: Article
• Acceptance Date: Jun 22, 2023
• Online Publication Date: Jul 12, 2023
• Publication Date: Jul 12, 2023
• Deposit Date: Aug 21, 2023
• Publicly Available Date: Aug 25, 2023
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Electronic ISSN: 1366-588X
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1080/00207543.2023.2232050
• Keywords: artificial intelligence; operations management; supply chain management; SCOR; industry 40
• Public URL: https://nottingham-repository.worktribe.com/output/24574410
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/00207543.2023.2232050