Cost to Serve

This project introduces a refined approach to cost allocation developed for our sponsor company, a manufacturer and commercializer of chemical solutions for the construction industry. Faced with the complexities of serving thousands of clients across North America, the company seeks to improve its volume-based allocation method to accurately identify profitable clients and enhance business decision-making. Utilizing the Shapley method from cooperative game theory, this study proposes a new model that incorporates geographic distances, shipment volumes, and other logistical factors into the cost allocation process. We employ Shapley values to assign costs based on the marginal contributions of each client to the overall transportation costs, a significant advancement over the volume-based proportional method. Initial results demonstrate the model's effectiveness in providing a fair and transparent cost distribution, supported by several methods of analysis which quantifies the improvement over the existing allocation policy. Furthermore, the implementation of this model was augmented through machine learning techniques, which enables predictive insights for cost allocation. This enhancement promises improved operational efficiency and strategic planning capabilities. The project not only addresses the immediate needs of our sponsor company but also sets a precedent for “cost-to-serve” applications in logistics-intensive industries.