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Smart product platforming powered by AI and generative AI: Personalization for the circular economy

Author

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  • Akhtar, Pervaiz
  • Ghouri, Arsalan Mujahid
  • Ashraf, Aniqa
  • Lim, Jia Jia
  • Khan, Naveed R
  • Ma, Shuang

Abstract

The interlocks between smart product platforming (SPP) powered by Artificial Intelligence (AI) and Generative AI, big data analytics, and machine learning are still in their infancy. Modern technology-driven SPP promotes personalized product design and manufacturing suited to support environmentally friendly products for the circular economy. In this study, we develop a framework pertaining to the interlinks between SPP, big data analytics, machine learning, and the circular economy. To test our framework, we apply structure equation modeling based on data collected from more than 200 automotive industry professionals operating in China. Our results demonstrate that SPP and big data analytics are the central determinants for manufacturing environmentally friendly products, ultimately promoting circular economy applications. SPP plays a pivotal role in innovative product design and in facilitating the relevant manufacturing procedures. Big data analytics significantly feed into SPP applications. Machine learning and flexibility in SPP perform moderating roles in strengthening environmentally friendly outcomes. The mediating role played by SPP between big data analytics and environmentally friendly products for the circular economy is partially encouraging. As SPP powered by AI and Generative AI is an emerging phenomenon, our study contributes to this new knowledge dimension. We conclude this paper by discussing the theoretical and practical implications of our study, its limitations, and directions for future research.

Suggested Citation

  • Akhtar, Pervaiz & Ghouri, Arsalan Mujahid & Ashraf, Aniqa & Lim, Jia Jia & Khan, Naveed R & Ma, Shuang, 2024. "Smart product platforming powered by AI and generative AI: Personalization for the circular economy," International Journal of Production Economics, Elsevier, vol. 273(C).
    • Handle: RePEc:eee:proeco:v:273:y:2024:i:c:s0925527324001403
    DOI: 10.1016/j.ijpe.2024.109283
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