Applying a model for lateral transshipments in fast-fashion retail

Abstract: The highly variable demand of fast fashion causes retailers in the industry to face large uncertainties when allocating initial inventory batches across multiple locations. Further into the selling season, inventory imbalances might arise as an effect of deviations from expected demand. To mitigate these imbalances, retailers can make use of lateral transshipments of inventory from locations with excess stock to those facing the risk of a stockout. Such transshipments require models for determining what and how much to ship as well as when to ship it. This thesis investigates how such a model can be applied on replenishment warehouse level in a fast-fashion retail setting. The research was conducted through a quantitative case study at Hennes & Mauritz (H&M), one of the largest fast-fashion retailers in the world. An appropriate existing transshipment model based on the concept of service level was identified and adjusted to suit the characteristics of H&M. In contrast to the vast majority of models in literature, empirical probability distributions were used for dynamically modelling short-term demand. The proposed model was evaluated and found to suggest transshipments yielding significant revenue increase driven by lowered price reductions. This thesis differs from existing literature by providing a unique case-study of how a transshipment model can be applied in practice and how it performs on empirical data from one of the largest fast-fashion retailers in the world.