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Forecasting Demand for Fashion Goods:A Hierarchical Bayesian Approach

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Intelligent Fashion Forecasting Systems: Models and Applications

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.

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Notes

  1. 1.

    A full summary of the notation in the paper, and a description of the probability distributions used is provided in the appendices.

  2. 2.

    For concreteness’s sake, we will frequently regard one period as 1 day, though we use the term “period” throughout to emphasize the generality of the framework.

  3. 3.

    For technical convenience, we take Purch 0 to be the empty set.

  4. 4.

    Throughout this section, events are assumed (conditionally) independent unless the contrary is noted.

  5. 5.

    i.e. \(i \in \mathtt{Market}\) and \(i\notin \mathtt{Purch}_{t-1}\).

  6. 6.

    Since some of the T j may be zero, we adopt the convention that for any expression •, \(\prod _{j=1}^{0}\mathrm{\bullet } = 1\).:

  7. 7.

    For a comprehensive survey of analytical models of product life cycles, see Mahajan et al. [42].

  8. 8.

    We use the difference between the 20th and 95th percentiles rather than the 95th percentile itself because α j and δ j might reasonably be considered a priori independent, making for easier specification of the model prior.

  9. 9.

    Strictly speaking, truncation on the left should be at a point slightly greater than 0, but the technical elision is of no practical consequence.

  10. 10.

    In this respect, we depart from the precept set out in the introduction requiring us to disassociate the simulation and forecasting model. However, since we do not regard seasonal effects as a defining characteristic of demand for fashion goods (to which life cycle effects are most pivotal), and since alternative approaches to seasonal modeling would be unnecessarily cumbersome here, we consider such a lapse justified.

  11. 11.

    “Boxes” delimit the interquartile range of the distributions, and “whiskers” extend 1.5 times the interquartile range from the ends of the boxes—see Tukey [62] for further details.

  12. 12.

    This is a modification of the more common mean absolute percentage error (MAPE) metric; since the values in the forecast series vary significantly over lifecycle of the corresponding product, the MAPE is apt to distort performance assessment by unduly inflating small absolute errors at either end of the lifecycle.

  13. 13.

    Note that the comparatively poor performance of the ets function here should not be construed as general criticism, but rather as affirmation of the contention that the proposed model is far better suited to the type of series encountered in this context than is a general-purpose forecasting tool.

  14. 14.

    In this respect, the situation here is the obverse of that in [67], where efficacy for a particular manufacturer’s demand series was demonstrated, but more general applicability remained something of an open question.

  15. 15.

    Hines and Bruce [32] point out that “fast fashion” retailers such as Zara and Primark publish revised forecasts on a weekly basis.

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Authors and Affiliations

  1. Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA

    Phillip M. Yelland

  2. Santa Clara University, 500 El Camino Real, Santa Clara, CA, 95053, USA

    Xiaojing Dong

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  1. Phillip M. Yelland
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Correspondence to Phillip M. Yelland .

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Editors and Affiliations

  1. Business Division Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hongkong, People's Republic of China

    Tsan-Ming Choi

  2. Business Division Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hongkong, People's Republic of China

    Chi-Leung Hui

  3. Business Division Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hongkong, People's Republic of China

    Yong Yu

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Yelland, P.M., Dong, X. (2014). Forecasting Demand for Fashion Goods:A Hierarchical Bayesian Approach. In: Choi, TM., Hui, CL., Yu, Y. (eds) Intelligent Fashion Forecasting Systems: Models and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39869-8_5

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