Markdown Optimization in Apparel Retail Sector

Abstract

Price discounts, known as markdowns, are important for fast fashion retailers to utilize inventory in a distribution channel using demand management. Estimating future demand for a given discount level requires the evaluation of historical sales data. In this evaluation recent observations might be more important than the older ones as majority of price discounts take place at the end of a selling season and that time period provides more accurate estimations. In this study, we consider a weighted least squares method for the parameter estimation of an empirical demand model used in a markdown optimization system. We suggest a heuristic procedure for the implementation of weighted least squares in a markdown optimization utilizing a generic weight function from the literature. We tested the suggested system using empirical data from a Turkish apparel retailer. Our results indicate that the weighted least squares method is more proper than the ordinary least squares for the fast fashion sales data as it captures price sensitivity of demand at the end of a selling season more accurately.