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Forecasting Demand for Fashion Goods:A Hierarchical Bayesian Approach

In: Intelligent Fashion Forecasting Systems: Models and Applications

Author

Listed:
  • Phillip M. Yelland

    (Google Inc.)

  • Xiaojing Dong

    (Santa Clara University)

Abstract

A central feature of demand for products in the fashion apparel segment is a pronounced product life cycle—demand for a fashion product tends to rise and fall dramatically in accordance with the rate of public of adoption. Product demands that vary in such a manner can be difficult to forecast, especially in the critical early period of a product’s life, when observed demand can be a very unreliable yardstick of demand later on. This paper examines the applicability of a Bayesian forecasting model—based on one developed for use in the computer industry—to fashion products. To do so, we use an agent-based simulation to produce a collection of demand series consistent with commonly-accepted characteristics of fashion adoption. Using Markov chain Monte Carlo techniques to make predictions using the Bayesian model, we are able quantitatively to demonstrate its superior performance in this application.

Suggested Citation

  • Phillip M. Yelland & Xiaojing Dong, 2014. "Forecasting Demand for Fashion Goods:A Hierarchical Bayesian Approach," Springer Books, in: Tsan-Ming Choi & Chi-Leung Hui & Yong Yu (ed.), Intelligent Fashion Forecasting Systems: Models and Applications, edition 127, chapter 0, pages 71-94, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-39869-8_5
    DOI: 10.1007/978-3-642-39869-8_5
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    Citations

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    Cited by:

    1. Fallah Tehrani, Ali & Ahrens, Diane, 2016. "Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 131-138.
    2. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    3. Shuyun Ren & Hau-Ling Chan & Tana Siqin, 2020. "Demand forecasting in retail operations for fashionable products: methods, practices, and real case study," Annals of Operations Research, Springer, vol. 291(1), pages 761-777, August.
    4. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    5. Dazhou Lei & Hao Hu & Dongyang Geng & Jianshen Zhang & Yongzhi Qi & Sheng Liu & Zuo‐Jun Max Shen, 2023. "New product life cycle curve modeling and forecasting with product attributes and promotion: A Bayesian functional approach," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 655-673, February.

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