{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:39:14Z","timestamp":1782833954044,"version":"3.54.5"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Research in lean production has recently focused on linking lean production to Industry 4.0 by discussing the positive relationship between them. In the context of Industry 4.0, data science plays a fundamental role, and operations management research is dedicating particular attention to this field. However, the literature on the empirical implementation of data science to lean production is still under-investigated and details are lacking in most of the reported contributions. In this study, multiple case studies were conducted involving the Italian manufacturing sector to collect evidence of the application of data science to support lean production and to understand it. The results provide empirical proof of the link and examples of a variety of data science techniques and tools that can be combined to support lean production practices. The findings offer insights into the applications of the traditional lean plan\u2013do\u2013check\u2013act cycle, supporting feedback on performance metrics, total productive maintenance, total quality management, statistical process control, root cause analysis for problem-solving, visual management, and Kaizen.<\/jats:p>","DOI":"10.3390\/systems12030100","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T04:47:05Z","timestamp":1710478025000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Data Science Supporting Lean Production: Evidence from Manufacturing Companies"],"prefix":"10.3390","volume":"12","author":[{"given":"Rossella","family":"Pozzi","sequence":"first","affiliation":[{"name":"School of Industrial Engineering, Universit\u00e0 Carlo Cattaneo\u2014LIUC, 21053 Castellanza, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Violetta Giada","family":"Cannas","sequence":"additional","affiliation":[{"name":"School of Industrial Engineering, Universit\u00e0 Carlo Cattaneo\u2014LIUC, 21053 Castellanza, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tommaso","family":"Rossi","sequence":"additional","affiliation":[{"name":"School of Industrial Engineering, Universit\u00e0 Carlo Cattaneo\u2014LIUC, 21053 Castellanza, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"ref_1","first-page":"140","article-title":"Beyond Toyota: How to root out waste and pursue perfection","volume":"74","author":"Womack","year":"1996","journal-title":"Harv. Bus. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pe\u00e7as, P., Encarna\u00e7\u00e3o, J., Gamb\u00f4a, M., Sampayo, M., and Jorge, D. (2021). Pdca 4.0: A new conceptual approach for continuous improvement in the industry 4.0 paradigm. Appl. Sci., 11.","DOI":"10.3390\/app11167671"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"94","DOI":"10.24867\/IJIEM-2023-2-326","article-title":"How to accelerate digital transformation in companies with Lean Philosophy? Contributions based on a practical case","volume":"14","author":"Amorim","year":"2023","journal-title":"Int. J. Ind. Eng. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.procs.2022.01.238","article-title":"Lean and industry 4.0: A leading harmony","volume":"200","author":"Naciri","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1080\/00207543.2018.1444806","article-title":"Industry 4.0: State of the art and future trends","volume":"56","author":"Xu","year":"2018","journal-title":"Int. J. Prod. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1108\/JMTM-09-2018-0325","article-title":"Industry 4.0 as a data-driven paradigm: A systematic literature review on technologies","volume":"32","author":"Klingenberg","year":"2021","journal-title":"J. Manuf. Technol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1080\/00207543.2020.1798035","article-title":"Researchers\u2019 perspectives on Industry 4.0: Multi-disciplinary analysis and opportunities for operations management","volume":"59","author":"Ivanov","year":"2021","journal-title":"Int. J. Prod. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1177\/1063293X20987911","article-title":"Bin Development of IoT\u2014Enabled data analytics enhance decision support system for lean manufacturing process improvement","volume":"29","author":"Mohamad","year":"2021","journal-title":"Concurr. Eng."},{"key":"ref_9","first-page":"851","article-title":"Lean 4.0: A new holistic approach for the integration of lean manufacturing tools and digital technologies","volume":"5","author":"Valamede","year":"2020","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1108\/JMTM-01-2019-0032","article-title":"Towards the proposition of a lean automation framework: Integrating industry 4.0 into lean production","volume":"32","author":"Tortorella","year":"2020","journal-title":"J. Manuf. Technol. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103464","DOI":"10.1016\/j.compind.2021.103464","article-title":"Lean manufacturing and internet of things\u2014A synergetic or antagonist relationship?","volume":"129","author":"Anosike","year":"2021","journal-title":"Comput. Ind."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1108\/EJIM-02-2018-0030","article-title":"Organizational and managerial challenges in the path toward Industry 4.0","volume":"22","author":"Agostini","year":"2019","journal-title":"Eur. J. Innov. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1080\/00207543.2020.1832274","article-title":"Industry 4.0 triggered by Lean Thinking: Insights from a systematic literature review","volume":"59","author":"Bittencourt","year":"2021","journal-title":"Int. J. Prod. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1108\/TQM-11-2021-0318","article-title":"Lean in industry 4.0 is accelerating manufacturing excellence\u2014A DEMATEL analysis","volume":"35","author":"Ojha","year":"2023","journal-title":"TQM J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108258","DOI":"10.1016\/j.ijpe.2021.108258","article-title":"\u2018Lean 4.0\u2019: How can digital technologies support lean practices?","volume":"241","author":"Cifone","year":"2021","journal-title":"Int. J. Prod. Econ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6866","DOI":"10.1080\/00207543.2021.1946192","article-title":"Linking data science to lean production: A model to support lean practices","volume":"60","author":"Pozzi","year":"2021","journal-title":"Int. J. Prod. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1080\/14783363.2022.2141107","article-title":"The integration of Industry 4.0 and Lean Management: A systematic review and constituting elements perspective","volume":"34","author":"Komkowski","year":"2022","journal-title":"Total Qual. Manag. Bus. Excell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1080\/00207543.2018.1442945","article-title":"The link between Industry 4.0 and lean manufacturing: Mapping current research and establishing a research agenda","volume":"56","author":"Buer","year":"2018","journal-title":"Int. J. Prod. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1080\/00207543.2019.1672902","article-title":"Impacts of Industry 4.0 technologies on Lean principles","volume":"58","author":"Rosin","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1976","DOI":"10.1080\/00207543.2020.1790684","article-title":"The complementary effect of lean manufacturing and digitalisation on operational performance","volume":"59","author":"Buer","year":"2021","journal-title":"Int. J. Prod. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1108\/01443570210414329","article-title":"Case research in operations management","volume":"22","author":"Voss","year":"2002","journal-title":"Int. J. Oper. Prod. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1080\/00207543.2018.1498986","article-title":"Investing in lean manufacturing practices: An environmental and operational perspective","volume":"57","author":"Bai","year":"2019","journal-title":"Int. J. Prod. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1108\/20401461011033176","article-title":"Design for six sigma: Caveat emptor","volume":"1","author":"Watson","year":"2010","journal-title":"Int. J. Lean Six Sigma"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"157","DOI":"10.24867\/IJIEM-2020-3-261","article-title":"Development of Lean Manufacturing Implementation Framework in Machinery and Equipment SMEs","volume":"11","author":"Yuik","year":"2020","journal-title":"Int. J. Ind. Eng. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"25","DOI":"10.5465\/amj.2007.24160888","article-title":"Theory Building from Cases: Opportunities and Challenges Linked references are available on JSTOR for this article: Theory Building from Cases: Opportunities and Challenges","volume":"50","author":"Eisenhardt","year":"2007","journal-title":"Acad. Manag. J."},{"key":"ref_26","unstructured":"Yin, R.K. (2018). Case study Research and Applications, Sage Publication, Inc.. Des. Methods."},{"key":"ref_27","unstructured":"(2024, March 07). Classification Ateco (Classification of Economic Activity). Available online: https:\/\/www.istat.it\/en\/archivio\/17959."},{"key":"ref_28","unstructured":"(2024, March 07). Aida Analisi Informatizzata Delle Aziende Italiane. Available online: https:\/\/login.bvdinfo.com\/R0\/AidaNeo."},{"key":"ref_29","unstructured":"(2024, March 07). What Is an SME?. Available online: https:\/\/web.archive.org\/web\/20150208090338\/http:\/ec.europa.eu\/enterprise\/policies\/sme\/facts-figures-analysis\/sme-definition\/index_en.htm."},{"key":"ref_30","unstructured":"de Marrais, K., and Lapan, S.D. (2004). Foundations for Research: Methods of Inquiry in Education and the Social Sciences, Lawrence Erlbaum Associates, Inc."},{"key":"ref_31","first-page":"1","article-title":"Data and knowledge mining with big data towards smart production","volume":"9","author":"Cheng","year":"2018","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0272-6963(98)00042-4","article-title":"Strategic consensus in operations strategy","volume":"17","author":"Boyer","year":"1999","journal-title":"J. Oper. Manag."},{"key":"ref_33","unstructured":"Voss, C., Johnson, M., and Godsell, J. (2016). Research Methods for Operations Management, Routledge."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Stojanovic, L., Dinic, M., Stojanovic, N., and Stojadinovic, A. (2016, January 5\u20138). Big-data-driven anomaly detection in industry (4.0): An approach and a case study. Proceedings of the 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA.","DOI":"10.1109\/BigData.2016.7840777"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2927","DOI":"10.1007\/s00170-020-05124-0","article-title":"Integration of Lean Practices and Industry 4.0 Tech- nologies: Smart Manufacturing for Next-Generation Enter-prises","volume":"107","author":"Shahin","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/12\/3\/100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:14:06Z","timestamp":1760105646000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/12\/3\/100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,15]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["systems12030100"],"URL":"https:\/\/doi.org\/10.3390\/systems12030100","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,15]]}}}