AI-enabled smart manufacturing boosts ecosystem value capture: the importance of servitization pathways within digital-intensive industries
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2024-11
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Elsevier B.V.
Resumen
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.
Palabras clave
AI-Enabled smart manufacturing
Ecosystem
Servitization
Value capture
Ecosystem
Servitization
Value capture
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Materias
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Bustinza, O. F., Molina, L. M., Vendrell-Herrero, F., & Opazo-Basaez, M. (2024). AI-enabled smart manufacturing boosts ecosystem value capture: The importance of servitization pathways within digital-intensive industries. International Journal of Production Economics, 277. https://doi.org/10.1016/J.IJPE.2024.109411