Stochastic Lateral Transshipment within the Fast Fashion Industry

Abstract: In an industry with highly variable demand and a fickle customer demanding a fast changing supply, quickly responding to the customer becomes crucial for the fast fashion retailer. Using lateral transshipment, one is able to reorganize supply within an echelon of the supply chain to quickly respond to the forecasted demand by looking at shorter more accurate forecasts and act accordingly. Due to the uncertainty of demand there is the question of if introducing stochasticity into the optimization model can improve the outcome. In this thesis a case study is conducted together with H&M Group to solve the transshipment problem with a Two-Stage Recourse Problem with Sample Average Approximation (SAA). The problem was solved for four warehouses within the same geographical region, 18 325 variants, and 5 sample sets in the SAA. A variant is defined as a product of a specific colour and size. The model was compared to the wait and see solution (WS) as well as the solution given by the expected value problem (EV). The recourse problem resulted in the mean and median service level of each warehouse increasing, no matter if they ship more items than they receive, and the mean service level over all variants increasing. It was then shown that despite a small aggregate value of the stochastic solution (VSS) of approximately 3.7%, using a stochastic model led to a 1.3 percentage point (pp) larger increase of mean service level for the entire system, as well as a roughly 20pp increase of the median ”mean service level” for items across all warehouses. So, despite a small decrease in cost the result on the service level is a great benefit in a setting where service levels is of high importance.