IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v231y2021ics0925527320302759.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527320302759
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2020.107918?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guilherme Luz Tortorella & Diego Fettermann, 2018. "Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies," International Journal of Production Research, Taylor & Francis Journals, vol. 56(8), pages 2975-2987, April.
    2. Bortolotti, Thomas & Boscari, Stefania & Danese, Pamela, 2015. "Successful lean implementation: Organizational culture and soft lean practices," International Journal of Production Economics, Elsevier, vol. 160(C), pages 182-201.
    3. Armstrong, J. Scott & Overton, Terry S., 1977. "Estimating Nonresponse Bias in Mail Surveys," MPRA Paper 81694, University Library of Munich, Germany.
    4. Dennis Kolberg & Joshua Knobloch & Detlef Zühlke, 2017. "Towards a lean automation interface for workstations," International Journal of Production Research, Taylor & Francis Journals, vol. 55(10), pages 2845-2856, May.
    5. Hüttmeir, Andreas & de Treville, Suzanne & van Ackere, Ann & Monnier, Léonard & Prenninger, Johann, 2009. "Trading off between heijunka and just-in-sequence," International Journal of Production Economics, Elsevier, vol. 118(2), pages 501-507, April.
    6. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    7. Losonci, Dávid & Demeter, Krisztina & Jenei, István, 2011. "Factors influencing employee perceptions in lean transformations," International Journal of Production Economics, Elsevier, vol. 131(1), pages 30-43, May.
    8. Bortolotti, Thomas & Danese, Pamela & Flynn, Barbara B. & Romano, Pietro, 2015. "Leveraging fitness and lean bundles to build the cumulative performance sand cone model," International Journal of Production Economics, Elsevier, vol. 162(C), pages 227-241.
    9. Frank, Alejandro Germán & Dalenogare, Lucas Santos & Ayala, Néstor Fabián, 2019. "Industry 4.0 technologies: Implementation patterns in manufacturing companies," International Journal of Production Economics, Elsevier, vol. 210(C), pages 15-26.
    10. Frank, Alejandro G. & Mendes, Glauco H.S. & Ayala, Néstor F. & Ghezzi, Antonio, 2019. "Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 341-351.
    11. Jagdeep Singh & Harwinder Singh, 2014. "Performance enhancement of a manufacturing industry by using continuous improvement strategies - a case study," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 14(1), pages 36-65.
    12. Tortorella, Guilherme Luz & Cawley Vergara, Alejandro Mac & Garza-Reyes, Jose Arturo & Sawhney, Rapinder, 2020. "Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers," International Journal of Production Economics, Elsevier, vol. 219(C), pages 284-294.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tortorella, Guilherme Luz & Saurin, Tarcísio Abreu & Filho, Moacir Godinho & Samson, Daniel & Kumar, Maneesh, 2021. "Bundles of Lean Automation practices and principles and their impact on operational performance," International Journal of Production Economics, Elsevier, vol. 235(C).
    2. Oliveira-Dias, Diéssica de & Maqueira-Marin, Juan Manuel & Moyano-Fuentes, José & Carvalho, Helena, 2023. "Implications of using Industry 4.0 base technologies for lean and agile supply chains and performance," International Journal of Production Economics, Elsevier, vol. 262(C).
    3. Tortorella, Guilherme Luz & Cawley Vergara, Alejandro Mac & Garza-Reyes, Jose Arturo & Sawhney, Rapinder, 2020. "Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers," International Journal of Production Economics, Elsevier, vol. 219(C), pages 284-294.
    4. Bokhorst, Jos A.C. & Knol, Wilfred & Slomp, Jannes & Bortolotti, Thomas, 2022. "Assessing to what extent smart manufacturing builds on lean principles," International Journal of Production Economics, Elsevier, vol. 253(C).
    5. Sunder M, Vijaya & Prashar, Anupama, 2024. "The interplay of lean practices and digitalization on organizational learning systems and operational performance," International Journal of Production Economics, Elsevier, vol. 270(C).
    6. Gillani, Fatima & Chatha, Kamran Ali & Sadiq Jajja, Muhammad Shakeel & Farooq, Sami, 2020. "Implementation of digital manufacturing technologies: Antecedents and consequences," International Journal of Production Economics, Elsevier, vol. 229(C).
    7. Bag, Surajit & Gupta, Shivam & Kumar, Sameer, 2021. "Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development," International Journal of Production Economics, Elsevier, vol. 231(C).
    8. Narayanamurthy, Gopalakrishnan & Tortorella, Guilherme, 2021. "Impact of COVID-19 outbreak on employee performance – Moderating role of industry 4.0 base technologies," International Journal of Production Economics, Elsevier, vol. 234(C).
    9. Culot, Giovanna & Orzes, Guido & Sartor, Marco & Nassimbeni, Guido, 2020. "The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    10. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    11. Tortorella, Guilherme Luz & Fogliatto, Flavio S. & Cauchick-Miguel, Paulo A. & Kurnia, Sherah & Jurburg, Daniel, 2021. "Integration of Industry 4.0 technologies into Total Productive Maintenance practices," International Journal of Production Economics, Elsevier, vol. 240(C).
    12. Virmani, Naveen & Sharma, Shikha & Kumar, Anil & Luthra, Sunil, 2023. "Adoption of industry 4.0 evidence in emerging economy: Behavioral reasoning theory perspective," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    13. 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).
    14. Gastaldi, Luca & Lessanibahri, Sina & Tedaldi, Gianluca & Miragliotta, Giovanni, 2022. "Companies’ adoption of Smart Technologies to achieve structural ambidexterity: an analysis with SEM," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    15. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    16. Xi, Mengjie & Liu, Yang & Fang, Wei & Feng, Taiwen, 2024. "Intelligent manufacturing for strengthening operational resilience during the COVID-19 pandemic: A dynamic capability theory perspective," International Journal of Production Economics, Elsevier, vol. 267(C).
    17. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: a research agenda for industrial maintenance management," International Journal of Production Economics, Elsevier, vol. 224(C).
    18. Henrik Saabye & Thomas Borup Kristensen & Brian Vejrum Wæhrens, 2020. "Real-Time Data Utilization Barriers to Improving Production Performance: An In-depth Case Study Linking Lean Management and Industry 4.0 from a Learning Organization Perspective," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
    19. Calış Duman, Meral & Akdemir, Bunyamin, 2021. "A study to determine the effects of industry 4.0 technology components on organizational performance," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    20. Shet, Sateesh V. & Pereira, Vijay, 2021. "Proposed managerial competencies for Industry 4.0 – Implications for social sustainability," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:231:y:2021:i:c:s0925527320302759. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.