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A total sales forecasting method for a new short life-cycle product in the pre-market period based on an improved evidence theory: application to the film industry

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  • Zhongjun Tang
  • Shunpeng Dong

Abstract

It is challenging to forecast total sales of short life-cycle products due to a lack of historical sales data. Multi-source information combination methods make it possible to depict different kinds of characteristics and features, given a limited volume of samples. Evidence theory is a common approach used for multi-source combinations. This paper proposes a new method, named ‘Multi-Evidence Dynamic Weighted Combination Forecasting (MEDWCF)’, based on improvements in the application of Evidence theory. Two kinds of machine learning methods are used to solve the basic probability assignment generation problem pertaining to Evidence theory, so a dynamic update combination algorithm is proposed. These innovations improve the classical one-step static combination rules. Samples of 313 films launched within 2016 and 2017 proved that compared with other forecasting methods, MEDWCF has more effectiveness and better generalisation ability. Effective product sales forecast by MEDWCF may help managers make correct decisions in manufacturing and marketing before the product launched.

Suggested Citation

  • Zhongjun Tang & Shunpeng Dong, 2021. "A total sales forecasting method for a new short life-cycle product in the pre-market period based on an improved evidence theory: application to the film industry," International Journal of Production Research, Taylor & Francis Journals, vol. 59(22), pages 6776-6790, November.
    • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:22:p:6776-6790
    DOI: 10.1080/00207543.2020.1825861
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