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Identifying pathways to a high-performing lean automation implementation: An empirical study in the manufacturing industry

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  • Tortorella, Guilherme Luz
  • Narayanamurthy, Gopalakrishnan
  • Thurer, Matthias

Abstract

This paper examines pathways to implement a high-performing Lean Automation (LA). We asked 61 manufacturers from Brazil and India that are undergoing a lean implementation together with the adoption of disruptive digital technologies from Industry 4.0 (I4.0) to indicate their implementation sequence. We then used multivariate data techniques to analyze the collected data. Our findings suggested three sets of lean practices and I4.0 technologies; namely: start-up, in-transition and advanced. Further, companies that presented a higher performance improvement have more extensively implemented start-up and in-transition practices/technologies. However, no significant difference was found for the adoption level of advanced practices/technologies between low- and high-performer companies. Since the integration of I4.0 technologies into Lean Manufacturing (LM) is a relatively recent phenomenon, our study provides guidelines related to a preferential implementation sequence within this portfolio of practices and technologies.

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  • Tortorella, Guilherme Luz & Narayanamurthy, Gopalakrishnan & Thurer, Matthias, 2021. "Identifying pathways to a high-performing lean automation implementation: An empirical study in the manufacturing industry," International Journal of Production Economics, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:proeco:v:231:y:2021:i:c:s0925527320302759
    DOI: 10.1016/j.ijpe.2020.107918
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    1. Tortorella, Guilherme Luz & Saurin, Tarcisio A. & Hines, Peter & Antony, Jiju & Samson, Daniel, 2023. "Myths and facts of industry 4.0," International Journal of Production Economics, Elsevier, vol. 255(C).
    2. Cifone, Fabiana Dafne & Hoberg, Kai & Holweg, Matthias & Staudacher, Alberto Portioli, 2021. "‘Lean 4.0’: How can digital technologies support lean practices?," International Journal of Production Economics, Elsevier, vol. 241(C).
    3. Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.

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