Identification and evaluation of improvement proposals for Rusta AB's delivery schedule

Sammanfattning

Rusta AB is a company with a mission to offer everyday products at competitive prices. Operating in Sweden, Norway, Finland and Germany, the company has a total of 205 stores. In 2015, Rusta established a central warehouse outside the city of Norrköping, which includes both automated and traditional stacking warehouses, covering a total area of 128,000 square meters. One of the primary challenges encountered by Rusta's central warehouse is the uneven workload associated with outbound deliveries per hour. By determining new outbound delivery times, this report aims to even out the load at the distribution center. To achieve this, interviews and observations have been conducted to identify and analyze potential improvement measures. Additionally, Excel has been utilized to propose new outbound delivery schedules. These have been developed by studying graphs of the original delivery schedule and then shifting the warehouse loading times based on several criteria linked to the clustering in an attempt to even out the load over the week. The outcome of this report highlights the feasibility of achieving a more balanced workload at the central warehouse by introducing new outbound delivery times for the stores. Two improvement proposals for new delivery schedules have been developed, with improvement proposal 2 proving to be the most effective as it enabled a more even load. Even though both proposals show an improvement linked to the load, it is important to think about the demarcations that have been made. One such that has an impact on the result is the 44 Norwegian department stores that are not treated in this study. These still need to be considered, with the result that the proposals produced can be considered misleading. Furthermore, cluster segmentation should continue to be explored as there are additional opportunities for improvement. For future research in this area, a mathematical optimization model is recommended to determine optimal outbound delivery times, as such a model can consider more parameters than those considered in this study.