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AI-enabled smart manufacturing boosts ecosystem value capture: The importance of servitization pathways within digital-intensive industries

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  • Bustinza, Oscar F.
  • Molina, Luis M.
  • Vendrell-Herrero, Ferran
  • Opazo-Basaez, Marco

Abstract

Understanding successful pathways for manufacturers to capture value within the service ecosystem framework is a recent and still nascent area of research that requires further investigation and growth. Within industrial settings, artificial intelligence (AI) constitutes an enabling technology that can be integrated across a network of products and systems, driving the transformation of these service ecosystems. From this perspective, this study proposes that the symbiotic convergence between AI-enabled smart manufacturing, which facilitates process and product enhancements, and servitization, which enables product availability and customization, contributes to a higher level of ecosystem value capture. To address this issue, a research model employing Smart Partial Least Squares was developed to examine the interplay between these constructs. By using survey data from a purposively selected sample of servitized manufacturing firms, the findings reveal the synergistic effects of integrating AI-enabled smart manufacturing and servitization. Furthermore, the results indicate variances across industrial sectors, and highlight that in digitally-intensive industries, service business models have undergone more substantial transformations, fostering accelerated ecosystem development streamlined by customization. Conversely, in digitally-augmented industries, where inputs are digital but products are predominantly analog, digital capabilities are primarily confined to production processes.

Suggested Citation

  • Bustinza, Oscar F. & Molina, Luis M. & Vendrell-Herrero, Ferran & Opazo-Basaez, Marco, 2024. "AI-enabled smart manufacturing boosts ecosystem value capture: The importance of servitization pathways within digital-intensive industries," International Journal of Production Economics, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:proeco:v:277:y:2024:i:c:s0925527324002688
    DOI: 10.1016/j.ijpe.2024.109411
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