IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v281y2020i3p543-558.html
   My bibliography  Save this article

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

Author

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

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.

Suggested Citation

  • Van Nguyen, Truong & Zhou, Li & Chong, Alain Yee Loong & Li, Boying & Pu, Xiaodie, 2020. "Predicting customer demand for remanufactured products: A data-mining approach," European Journal of Operational Research, Elsevier, vol. 281(3), pages 543-558.
    • Handle: RePEc:eee:ejores:v:281:y:2020:i:3:p:543-558
    DOI: 10.1016/j.ejor.2019.08.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037722171930668X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2019.08.015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Ravi Subramanian & Ramanath Subramanyam, 2012. "Key Factors in the Market for Remanufactured Products," Manufacturing & Service Operations Management, INFORMS, vol. 14(2), pages 315-326, April.
    3. Yan, Wei & Xiong, Yu & Xiong, Zhongkai & Guo, Nian, 2015. "Bricks vs. clicks: Which is better for marketing remanufactured products?," European Journal of Operational Research, Elsevier, vol. 242(2), pages 434-444.
    4. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    5. Wang, Yacan & Hazen, Benjamin T., 2016. "Consumer product knowledge and intention to purchase remanufactured products," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 460-469.
    6. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    7. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    8. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.
    9. Xu, Xun & Zeng, Shuo & He, Yuanjie, 2017. "The influence of e-services on customer online purchasing behavior toward remanufactured products," International Journal of Production Economics, Elsevier, vol. 187(C), pages 113-125.
    10. Frota Neto, João Quariguasi & Bloemhof, Jacqueline & Corbett, Charles, 2016. "Market prices of remanufactured, used and new items: Evidence from eBay," International Journal of Production Economics, Elsevier, vol. 171(P3), pages 371-380.
    11. Sanjeev Swami & Pankaj Khairnar, 2006. "Optimal normative policies for marketing of products with limited availability," Annals of Operations Research, Springer, vol. 143(1), pages 107-121, March.
    12. Milgrom, Paul R & Weber, Robert J, 1982. "A Theory of Auctions and Competitive Bidding," Econometrica, Econometric Society, vol. 50(5), pages 1089-1122, September.
    13. Asil Oztekin, 0. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    14. Masci, Chiara & Johnes, Geraint & Agasisti, Tommaso, 2018. "Student and school performance across countries: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1072-1085.
    15. James D. Abbey & Margaret G. Meloy & V. Daniel R. Guide Jr. & Selin Atalay, 2015. "Remanufactured Products in Closed-Loop Supply Chains for Consumer Goods," Production and Operations Management, Production and Operations Management Society, vol. 24(3), pages 488-503, March.
    16. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    17. Kuan Siew Khor & Benjamin T. Hazen, 2017. "Remanufactured products purchase intentions and behaviour: Evidence from Malaysia," International Journal of Production Research, Taylor & Francis Journals, vol. 55(8), pages 2149-2162, April.
    18. Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
    19. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    20. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    21. Zhou, Li & Xie, Jiaping & Gu, Xiaoyu & Lin, Yong & Ieromonachou, Petros & Zhang, Xiaole, 2016. "Forecasting return of used products for remanufacturing using Graphical Evaluation and Review Technique (GERT)," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 315-324.
    22. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    23. George A. Akerlof, 1970. "The Market for "Lemons": Quality Uncertainty and the Market Mechanism," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 84(3), pages 488-500.
    24. Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
    25. Boulding, William & Kirmani, Amna, 1993. "A Consumer-Side Experimental Examination of Signaling Theory: Do Consumers Perceive Warranties as Signals of Quality?," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(1), pages 111-123, June.
    26. Asil Oztekin, 2018. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 20(2), pages 223-238, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Narendra Singh & Pushpa Singh & Mukul Gupta, 2020. "An inclusive survey on machine learning for CRM: a paradigm shift," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 447-457, December.
    2. Seoyoon Lee & Minjung Kwak, 2020. "Consumer Valuation of Remanufactured Products: A Comparative Study of Product Categories and Business Models," Sustainability, MDPI, vol. 12(18), pages 1-29, September.
    3. Duan, Sophia Xiaoxia & Tay, Richard & Molla, Alemayehu & Deng, Hepu, 2022. "Predicting Mobility as a Service (MaaS) use for different trip categories: An artificial neural network analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 135-149.
    4. Mahmoud Tarhini, 2022. "Consumption and Consumer Behaviour of Organic AGRI-FOOD Products," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 23(1), pages 136-149, March.
    5. Adel A. Alamri, 2023. "A Sustainable Closed-Loop Supply Chains Inventory Model Considering Optimal Number of Remanufacturing Times," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    6. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
    7. Muhammad Salman Pathan & Edana Richardson & Edgar Galvan & Peter Mooney, 2023. "The Role of Artificial Intelligence within Circular Economy Activities—A View from Ireland," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
    8. Huang, Shupeng & Potter, Andrew & Eyers, Daniel & Li, Qinyun, 2021. "The influence of online review adoption on the profitability of capacitated supply chains," Omega, Elsevier, vol. 105(C).
    9. Karina Cecilia Arredondo-Soto & Alejandro Jiménez-Zaragoza & Marco Augusto Miranda-Ackerman & Julio Blanco-Fernández & Alejandra García-Lechuga & Guadalupe Hernández-Escobedo & Jorge Luis García-Alcar, 2022. "Design and Repair Strategies Based on Product–Service System and Remanufacturing for Value Preservation," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
    10. Boram Choi & Jong Hwan Suh, 2020. "Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
    11. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    12. Xu, Xianhao & Shen, Yaohan & (Amanda) Chen, Wanying & Gong, Yeming & Wang, Hongwei, 2021. "Data-driven decision and analytics of collection and delivery point location problems for online retailers," Omega, Elsevier, vol. 100(C).
    13. Alyahya, Mansour & Agag, Gomaa & Aliedan, Meqbel & Abdelmoety, Ziad Hassan & Daher, Maya Mostafa, 2023. "A sustainable step forward: Understanding factors affecting customers’ behaviour to purchase remanufactured products," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    2. Phantratanamongkol, Supanan & Casalin, Fabrizio & Pang, Gu & Sanderson, Joseph, 2018. "The price-volume relationship for new and remanufactured smartphones," International Journal of Production Economics, Elsevier, vol. 199(C), pages 78-94.
    3. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    4. Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Wang, Yacan & Zhu, Quan & Krikke, Harold & Hazen, Benjamin, 2020. "How product and process knowledge enable consumer switching to remanufactured laptop computers in circular economy," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    6. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    7. Juana Camacho-Otero & Casper Boks & Ida Nilstad Pettersen, 2018. "Consumption in the Circular Economy: A Literature Review," Sustainability, MDPI, vol. 10(8), pages 1-25, August.
    8. Zhou, Yu & Xiong, Yu & Jin, Minyue, 2021. "Less is more: Consumer education in a closed-loop supply chain with remanufacturing," Omega, Elsevier, vol. 101(C).
    9. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
    10. Quariguasi Frota Neto, João & Dutordoir, Marie, 2020. "Mapping the market for remanufacturing: An application of “Big Data” analytics," International Journal of Production Economics, Elsevier, vol. 230(C).
    11. Seoyoon Lee & Minjung Kwak, 2020. "Consumer Valuation of Remanufactured Products: A Comparative Study of Product Categories and Business Models," Sustainability, MDPI, vol. 12(18), pages 1-29, September.
    12. Xu, Xun & Zeng, Shuo & He, Yuanjie, 2021. "The impact of information disclosure on consumer purchase behavior on sharing economy platform Airbnb," International Journal of Production Economics, Elsevier, vol. 231(C).
    13. Xu, Xun & Zeng, Shuo & He, Yuanjie, 2017. "The influence of e-services on customer online purchasing behavior toward remanufactured products," International Journal of Production Economics, Elsevier, vol. 187(C), pages 113-125.
    14. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    15. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    16. Gökçe Esenduran & James A. Hill & In Joon Noh, 2020. "Understanding the Choice of Online Resale Channel for Used Electronics," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1188-1211, May.
    17. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    18. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    19. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    20. Xiaojiao Qiao & Xiukun Zhao & Jinhui Zou, 2021. "Remanufacturing Marketing Decisions in the Presence of Retailing Platforms in the Carbon Neutrality Era," IJERPH, MDPI, vol. 19(1), pages 1-18, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:281:y:2020:i:3:p:543-558. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.