Linking data science to lean production: a model to support lean practices

Pozzi, R; Cannas, V; Ciano, Maria

Abstract

The literature discusses data science (DS) as a very promising set of techniques and tools to support lean production (LP) practices. DS could aid manufacturing companies in transforming massive real-time data into meaningful knowledge, increasing process transparency and product quality information and supporting improvement activities through data-driven decision-making. However, no attempt has been made in the literature to formalise the links between DS and LP practices. Thus, this study aims to overcome this gap by clarifying the DS techniques and tools that can support LP practices and how to apply them. This study employs a quantitative bibliometric method—specifically, a keyword co-occurrence network analysis—on a set of papers extracted from Scopus. The results obtained allowed the researchers to identify a set of DS techniques and tools that can support LP practices and to develop a model to guide their implementation based on the typical improvement implementation stages of the plan-do-check-act cycle. The model shows how to use DS techniques and tools in LP for: identifying areas for improvement and subsequent implementation (plan); enabling a better knowledge and process management (do); identifying/predicting potential problems and employing statistical process control (check); providing remedial actions and effectively applying process improvement (act).

Citation

Pozzi, R., Cannas, V., & Ciano, M. (2021). Linking data science to lean production: a model to support lean practices. International Journal of Production Research, 1-22. https://doi.org/10.1080/00207543.2021.1946192

• Journal Article Type: Article
• Acceptance Date: Jun 13, 2021
• Online Publication Date: Jul 6, 2021
• Publication Date: Jul 6, 2021
• Deposit Date: Dec 3, 2021
• Publicly Available Date: Jul 7, 2022
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Electronic ISSN: 1366-588X
• Peer Reviewed: Peer Reviewed
• Pages: 1-22
• DOI: https://doi.org/10.1080/00207543.2021.1946192
• Keywords: Industrial and Manufacturing Engineering; Management Science and Operations Research; Strategy and Management
• Public URL: https://nottingham-repository.worktribe.com/output/6848617
• Publisher URL: https://www.tandfonline.com/doi/abs/10.1080/00207543.2021.1946192
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 6 July 2021, available online: https://www.tandfonline.com/doi/abs/10.1080/00207543.2021.1946192.