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

Truong Van Nguyen, Li Zhou, Alain Yee Loong Chong, Boying Li, Xiaodie Pu

Research output: Journal PublicationArticlepeer-review

97 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)543-558
Number of pages16
JournalEuropean Journal of Operational Research
Issue number3
Publication statusPublished - 16 Mar 2020


  • Data mining
  • Machine learning
  • Regression trees
  • Remanufactured products

ASJC Scopus subject areas

  • General Computer Science
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management
  • Industrial and Manufacturing Engineering


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