IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitmx/v19y2022i07ns0219877022500237.html
   My bibliography  Save this article

Combined Technology Selection Model for Digital Transformation in Manufacturing: A Case Study From the Automotive Supplier Industry

Author

Listed:
  • Hasan Erbay

    (Bosch TR, Aydınevler Mahallesi I˙nönü Caddesi 20 Ofispark A, Küçükyalı, Istanbul 34854, Turkey)

  • Nıhan Yıldırım

    (Management Engineering Department, Istanbul Technical University, ITU Macka Campus, Istanbul 34367, Turkey)

Abstract

Among generic technology management activities, rapid technology identification and selection stand as the significant determinants of technology adoption success in the digital transformation era. Especially for manufacturing SMEs in developing countries, rapid digital technologies are critical since they struggle to protect their competitiveness in global value chains threatened by digitalization. Previous studies introduce various multi-criteria decision-making model-based approaches to identify and select appropriate manufacturing technologies. However, these approaches were relatively rigid and required an advanced understanding of the technology for criteria and alternative settings and evaluation. Decision-makers need more flexible and scalable contextual frameworks for technology selection in digitalization. Since digital technologies offer both benefits and challenges, the decision-making models should reflect this dialectic nature of Industry 4.0 adoption and contextually optimize their decisions by combining multiple quantitative methods for technology identification and selection. Besides, case studies on digital technology selection are rare in manufacturing SMEs from developing country context in the literature. In this context, this study proposes a technology selection framework that utilizes the three dimensions (industry 4.0 technologies, benefits, and challenges) and combines AHP with a QFD-inspired intervention matrix and an optimization model by Mixed Integer Programming (MIP). The proposed model is validated with a case study from the automotive supplier industry in Turkey with the data provided from interviews and a Delphi survey with 11 experts from the digitalization value chain of the selected industry. Case study results revealed that the highest benefits of industry 4.0 lie in process/quality efficiency improvement and reduced inventory. At the same time, data analytics and sensor technologies occurred as the most critical tools. Significant challenges of digital technology adoption are insufficient expert know-how and budget constraints.

Suggested Citation

  • Hasan Erbay & Nıhan Yıldırım, 2022. "Combined Technology Selection Model for Digital Transformation in Manufacturing: A Case Study From the Automotive Supplier Industry," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 19(07), pages 1-34, November.
    • Handle: RePEc:wsi:ijitmx:v:19:y:2022:i:07:n:s0219877022500237
    DOI: 10.1142/S0219877022500237
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219877022500237
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219877022500237?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.

    Citations

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


    Cited by:

    1. Zhaocheng Xu & Jingchuan Hou, 2024. "CEO overseas experience, dynamic capabilities and corporate digital transformation: An imprinting theory perspective," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.

    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:wsi:ijitmx:v:19:y:2022:i:07:n:s0219877022500237. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitm/ijitm.shtml .

    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.