Forecasting demand for fashion goods: A hierarchical bayesian approach

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.