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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

再制造 背景(考古学) 质量(理念) 担保 计算机科学 过程管理 营销 利益相关者 循环经济 业务 知识管理 制造工程 经济 工程类 古生物学 生态学 哲学 管理 认识论 政治学 法学 生物
作者
Kannan Govindan
出处
期刊:International Journal of Production Economics [Elsevier]
卷期号:269: 109123-109123 被引量:3
标识
DOI:10.1016/j.ijpe.2023.109123
摘要

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.
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