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Predicting customer demand for remanufactured products: A data-mining approach

Predicting customer demand for remanufactured products: A data-mining approach

Van Nguyen, Truong, Zhou, Li ORCID: 0000-0001-7132-5935, Chong, Alain Yee, Li, Boying and Pu, Xiaodie (2019) Predicting customer demand for remanufactured products: A data-mining approach. European Journal of Operational Research. ISSN 0377-2217 (doi:https://doi.org/10.1016/j.ejor.2019.08.015)

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Abstract

Remanufacturing has received increasing attention from researchers over the last decade. While many associated operational issues have been extensively studied, research into the prediction customer demand for, and the market development of, remanufactured products is still in its infancy. The majority of the existing research into remanufactured product demand is largely based on conventional statistical models that fail to capture the non-linear behaviour of customer demand and market factors in real-world business environments, in particular e-marketplaces. Therefore, this paper aims to develop a comprehensible data-mining prediction approach, in order to achieve two objectives: (1) to provide a highly accurate and robust demand prediction model of remanufactured products; and (2) to shed light on the non-linear effect of online market factors as predictors of customer demand. Based on the real-world Amazon dataset, the results suggest that predicting remanufactured product demand is a complex, non-linear problem, and that, by using advanced machine-learning techniques, our proposed approach can predict the product demand with high accuracy. In terms of practical implications, the importance of market factors is ranked according to their predictive powers of demand, while their effects on demand are analysed through their partial dependence plots. Several insights for management are revealed by a thorough comparison of the sales impact of these market factors on remanufactured and new products.

Item Type: Article
Uncontrolled Keywords: Data mining; Remanufactured products; Machine learning; Regression trees
Subjects: H Social Sciences > HB Economic Theory
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Faculty of Business > Networks and Urban Systems Centre (NUSC)
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group
Last Modified: 30 Sep 2019 16:00
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/25075

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