IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v13y2022i4d10.1007_s13132-021-00832-8.html
   My bibliography  Save this article

Bringing Clarity to Issues with Adoption of Digital Manufacturing Capabilities: an Analysis of Multiple Independent Studies

Author

Listed:
  • Gregory A. Harris

    (Auburn University, 3312 Shelby Center)

  • Daniel Abernathy

    (Auburn University, 3312 Shelby Center)

  • Lin Lu

    (Fairfield University)

  • Anna Hyre

    (Auburn University, 3312 Shelby Center)

  • Alexander Vinel

    (Auburn University, 3312 Shelby Center)

Abstract

With access to a set of previously unpublished data focusing on implementation of digital manufacturing capabilities (Industry 4.0, Smart Manufacturing, and digital manufacturing), we attempt to identify recurring themes inhibiting their adoption, particularly focusing on small- and medium-sized manufacturers (SMMs). The data from webinar surveys and industry interviews revealed specific insights into a lack of digital readiness of the US industrial base. While larger manufacturers can be well-positioned to take advantage of new digital capabilities due to their size and resource availability, their small- and medium-sized suppliers often lag. This phenomenon can be a significant roadblock to the adoption of Industry 4.0 capabilities and its promised benefits. Our research indicates that most SMMs are not in a position to adopt these advanced manufacturing technologies and lack awareness and understanding of what “digital manufacturing” means. The investigation echoes similar issues documented and described by others in Europe, Australia, and Asia in addition to other research findings within the USA.

Suggested Citation

  • Gregory A. Harris & Daniel Abernathy & Lin Lu & Anna Hyre & Alexander Vinel, 2022. "Bringing Clarity to Issues with Adoption of Digital Manufacturing Capabilities: an Analysis of Multiple Independent Studies," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(4), pages 2868-2889, December.
    • Handle: RePEc:spr:jknowl:v:13:y:2022:i:4:d:10.1007_s13132-021-00832-8
    DOI: 10.1007/s13132-021-00832-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-021-00832-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13132-021-00832-8?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. Alexandre Moeuf & Robert Pellerin & Samir Lamouri & Simon Tamayo-Giraldo & Rodolphe Barbaray, 2018. "The industrial management of SMEs in the era of Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 56(3), pages 1118-1136, February.
    2. Berry, Michael W. & Browne, Murray & Langville, Amy N. & Pauca, V. Paul & Plemmons, Robert J., 2007. "Algorithms and applications for approximate nonnegative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 155-173, September.
    3. Martin Prause, 2019. "Challenges of Industry 4.0 Technology Adoption for SMEs: The Case of Japan," Sustainability, MDPI, vol. 11(20), pages 1-13, October.
    Full references (including those not matched with items on IDEAS)

    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. Sony, Michael & Naik, Subhash, 2020. "Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model," Technology in Society, Elsevier, vol. 61(C).
    2. M. Moghadam & K. Aminian & M. Asghari & M. Parnianpour, 2013. "How well do the muscular synergies extracted via non-negative matrix factorisation explain the variation of torque at shoulder joint?," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 16(3), pages 291-301.
    3. Zahoor, Nadia & Zopiatis, Anastasios & Adomako, Samuel & Lamprinakos, Grigorios, 2023. "The micro-foundations of digitally transforming SMEs: How digital literacy and technology interact with managerial attributes," Journal of Business Research, Elsevier, vol. 159(C).
    4. Srivastava, Deepak Kumar & Kumar, Vikas & Ekren, Banu Yetkin & Upadhyay, Arvind & Tyagi, Mrinal & Kumari, Archana, 2022. "Adopting Industry 4.0 by leveraging organisational factors," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    5. Houyem Zrelli & Abdullah H. Alsharif & Iskander Tlili, 2020. "Malmquist Indexes of Productivity Change in Tunisian Manufacturing Industries," Sustainability, MDPI, vol. 12(4), pages 1-20, February.
    6. Anhang Chen & Huiqin Zhang & Yuxiang Zhang & Junwei Zhao, 2024. "Manufacturers’ digital transformation under carbon cap-and-trade policy: investment strategy and environmental impact," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    7. Jianfei Cao & Han Yang & Jianshu Lv & Quanyuan Wu & Baolei Zhang, 2023. "Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    8. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
    9. Takehiro Sano & Tsuyoshi Migita & Norikazu Takahashi, 2022. "A novel update rule of HALS algorithm for nonnegative matrix factorization and Zangwill’s global convergence," Journal of Global Optimization, Springer, vol. 84(3), pages 755-781, November.
    10. Andrej Čopar & Blaž Zupan & Marinka Zitnik, 2019. "Fast optimization of non-negative matrix tri-factorization," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
    11. Duy Khuong Nguyen & Tu Bao Ho, 2017. "Accelerated parallel and distributed algorithm using limited internal memory for nonnegative matrix factorization," Journal of Global Optimization, Springer, vol. 68(2), pages 307-328, June.
    12. Bianco, Débora & Bueno, Adauto & Godinho Filho, Moacir & Latan, Hengky & Miller Devós Ganga, Gilberto & Frank, Alejandro G. & Chiappetta Jabbour, Charbel Jose, 2023. "The role of Industry 4.0 in developing resilience for manufacturing companies during COVID-19," International Journal of Production Economics, Elsevier, vol. 256(C).
    13. Yüksel, Hilmi, 2020. "An empirical evaluation of industry 4.0 applications of companies in Turkey: The case of a developing country," Technology in Society, Elsevier, vol. 63(C).
    14. GILLIS, Nicolas & GLINEUR, François, 2011. "Accelerated multiplicative updates and hierarchical als algorithms for nonnegative matrix factorization," LIDAM Discussion Papers CORE 2011030, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Eryarsoy, Enes & Kilic, Huseyin Selcuk & Zaim, Selim & Doszhanova, Marzhan, 2022. "Assessing IoT challenges in supply chain: A comparative study before and during- COVID-19 using interval valued neutrosophic analytical hierarchy process," Journal of Business Research, Elsevier, vol. 147(C), pages 108-123.
    16. Shanika L Wickramasuriya & Berwin A Turlach & Rob J Hyndman, 2019. "Optimal Non-negative Forecast Reconciliation," Monash Econometrics and Business Statistics Working Papers 15/19, Monash University, Department of Econometrics and Business Statistics.
    17. Vitkauskaitė, Elena & Varaniūtė, Viktorija & Bouwman, Harry, 2019. "Evaluating SMEs Readiness to Transform to IoT-Based Business Models," 30th European Regional ITS Conference, Helsinki 2019 205220, International Telecommunications Society (ITS).
    18. Szymon Cyfert & Waldemar Glabiszewski & Maciej Zastempowski, 2021. "Impact of Management Tools Supporting Industry 4.0 on the Importance of CSR during COVID-19. Generation Z," Energies, MDPI, vol. 14(6), pages 1-13, March.
    19. FUJIWARA Yoshi & INOUE Hiroyasu & YAMAGUCHI Takayuki & AOYAMA Hideaki & TANAKA Takuma & KIKUCHI Kentaro, 2021. "Money Flow Network Among Firms' Accounts in a Regional Bank of Japan," Discussion papers 21005, Research Institute of Economy, Trade and Industry (RIETI).
    20. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.

    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:spr:jknowl:v:13:y:2022:i:4:d:10.1007_s13132-021-00832-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.