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Customer demand prediction of service-oriented manufacturing incorporating customer satisfaction

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  • Jin Cao
  • Zhibin Jiang
  • Kangzhou Wang

Abstract

With the emergence of individualised and personalised customer demands, the interaction of service and product has come into the sight of manufacturers and thus promoted the arising of service-oriented manufacturing (SOM), a new business mode that combines manufacturing and service. Be similar to the conventional manufacturing, the customer demand prediction (CDP) of SOM is very important since it is the foundation of the following manufacturing stages. As there are always tight and frequent interactions between service providers and customers in SOM, the customer satisfaction would significantly influence the customer demand of the following purchasing periods. To cope with this issue, a novel CDP approach for SOM incorporating customer satisfaction is proposed. Firstly, the structural relationships among customer satisfaction index and the influence factors are quantitatively modelled by using the structural equation model. Secondly, to reduce the adverse effect of multiple structural input data and small sample size, the least square support vector mechanism is employed to predict customer demand. Finally, the CDP of the air conditioner compressor which is a typical SOM product is implemented as the real-case example, and the effectiveness and validity of the proposed approach is elaborated from the prediction results analysis and comparison.

Suggested Citation

  • Jin Cao & Zhibin Jiang & Kangzhou Wang, 2016. "Customer demand prediction of service-oriented manufacturing incorporating customer satisfaction," International Journal of Production Research, Taylor & Francis Journals, vol. 54(5), pages 1303-1321, March.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:5:p:1303-1321
    DOI: 10.1080/00207543.2015.1067377
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    Cited by:

    1. Kankam-Kwarteng, Collins & Sarpong, Appiah & Amofah, Ofosu & Acheampong, Stephen, 2021. "Marketing performance of service firms: Recognizing market sensing capability and customer interaction orientation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7, pages 8-48.
    2. Collins Kankam-Kwarteng & Appiah Sarpong & Ofosu Amofah & Stephen Acheampong, 2021. "Marketing performance of service firms: Recognizing market sensing capability and customer interaction orientation," Post-Print hal-03376959, HAL.
    3. Zihayat, Morteza & Ayanso, Anteneh & Davoudi, Heidar & Kargar, Mehdi & Mengesha, Nigussie, 2021. "Leveraging non-respondent data in customer satisfaction modeling," Journal of Business Research, Elsevier, vol. 135(C), pages 112-126.
    4. Kejia Chen & Jian Jin & Zheng Zhao & Ping Ji, 2022. "Understanding customer regional differences from online opinions: a hierarchical Bayesian approach," Electronic Commerce Research, Springer, vol. 22(2), pages 377-403, June.

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