Forecasting fashion apparel (Part 2)


Have you noticed the annoying stock art they put on The BFD blog header? All I can think of is "If those idiots only used SAS Forecast Server, they wouldn't have to draw graphs all over their window panes just to do forecasting." It must really p.o. the housekeeping staff at that company.

Forecasting Fashion Colors

Last week's The BFD ended with the cliffhanging question of whether we can accurately forecast fashion colors. One way to interpret this question is whether we can, in general, forecast new products.  (While the underlying item itself (like a blouse, or jacket, or refrigerator) may not be new, it is coming out in a new "fashion" color that may only be available for a limited time.)

New product forecasting methods are generally fair at best, and often quite poor. For something in a fashion color, we at least have historical information on the "basic" color versions of the product, and previously released fashion color versions.

Using a structured analogy approach (as described in this whitepaper "New Product Forecasting Using Structured Analogies"), you can visualize the range of demand for analogous new product introductions (see below), giving yourself a ballpark idea of what forecast may be "reasonable."  The structured analogy approach is probably most valuable, however, as a way to detect the unreasonableness in new product forecasts generated by other means.

(Image shows the first 16 weeks of sales for DVDs with attributes: Rating=R, Genre=Horror)

Rather than bet the company on a new product forecast that is probably going to be wrong (and wrong in a big way), we are better off focusing on flexibility and responsiveness of the supply chain. Or at least, in our financial analysis / justification for the new product, accounting for the fact that we really don't know how well it is going to sell. I've given up hope for a miracle new product forecasting algorithm.

In the next installment we'll examine a new theory: The Wavelength Time-Series Approach to forecasting fashion colors.


About Author

Mike Gilliland

Product Marketing Manager

Michael Gilliland is a longtime business forecasting practitioner and formerly a Product Marketing Manager for SAS Forecasting. He is on the Board of Directors of the International Institute of Forecasters, and is Associate Editor of their practitioner journal Foresight: The International Journal of Applied Forecasting. Mike is author of The Business Forecasting Deal (Wiley, 2010) and former editor of the free e-book Forecasting with SAS: Special Collection (SAS Press, 2020). He is principal editor of Business Forecasting: Practical Problems and Solutions (Wiley, 2015) and Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning (Wiley, 2021). In 2017 Mike received the Institute of Business Forecasting's Lifetime Achievement Award. In 2021 his paper "FVA: A Reality Check on Forecasting Practices" was inducted into the Foresight Hall of Fame. Mike initiated The Business Forecasting Deal blog in 2009 to help expose the seamy underbelly of forecasting practice, and to provide practical solutions to its most vexing problems.

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