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Unlocking the potential of quality as a core marketing strategy in remanufactured circular products: A machine learning enabled multi-theoretical perspective

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  • Govindan, Kannan

Abstract

Remanufacturing processes are inevitably associated with sustainable development. To unleash the potential of remanufacturing for circular economy transition, practitioners have introduced several strategies. Despite the important role of remanufacturing in circular economy, the final sales of remanufactured products are often less than anticipated targets. While several challenges may impact smaller sales, a primary challenge is a lack of focus on marketing strategies. Accordingly, only a small number of published studies explore marketing in the remanufacturing field. This study explores potential marketing opportunities in remanufacturing and focuses on improving warranty management; one approach is through increasing the reliability of quality by integrating smart technologies and, specifically, machine learning (ML). To achieve effective integration of machine learning in a new application, such as remanufacturing, more primary assessments are required. This study is the first to explore critical success factors (CSF) of machine learning with the integration of remanufacturing. A Danish case context has been chosen to explore the CSFs on machine learning integration in the quality process, especially with inspection of end-of-life (EoL) brake calipers. The study employs various theories, including CSF theory, Technology-Organization-Environment (ToE) theory, and stakeholder theory to analyze problem. 22 common CSFs were collected from existing studies, and they are validated and categorized based on ToE theory. The results show that 'expand the reach of algorithms' (T5), ‘scalability’ (O2), and ‘inspection policy’ (E2) are the most important success factors under these three dimensions, respectively. Several contributions were drawn from the results obtained that could directly help industrial leaders with the effective integration of ML in remanufacturing.

Suggested Citation

  • Govindan, Kannan, 2024. "Unlocking the potential of quality as a core marketing strategy in remanufactured circular products: A machine learning enabled multi-theoretical perspective," International Journal of Production Economics, Elsevier, vol. 269(C).
  • Handle: RePEc:eee:proeco:v:269:y:2024:i:c:s0925527323003559
    DOI: 10.1016/j.ijpe.2023.109123
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    Citations

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    Cited by:

    1. Niu, Baozhuang & Ruan, Yiyuan & Yu, Xinhu, 2024. "Purchasing new for remanufacturing: Sourcing co-opetition, tax-planning and data validation," International Journal of Production Economics, Elsevier, vol. 273(C).
    2. Bhattacharya, Sourabh & Govindan, Kannan & Ghosh Dastidar, Surajit & Sharma, Preeti, 2024. "Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    3. Sudhir Rana, 2024. "What, Why and How ‘Shared Goals’ Are Important in Academic Research? What Is There for Marketing Scholars?," FIIB Business Review, , vol. 13(3), pages 283-285, May.

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