Inventory-Service Optimization in Configure-to-Order Systems

This study is motivated by a process-reengineering problem in PC manufacturing, i.e., to move from a build-to-stock operation that is centered around end-product (machine type model) inventory, towards a configure-to-order (CTO) operation that eliminates end-product inventory. CTO appears to be the ideal operational model that provides both mass customization and a quick response time
to order fulfillment. To quantify the inventory-service tradeoff in the CTO environment, we develop a nonlinear optimization model with multiple constraints, reflecting the service levels offered to different market segments. To solve the optimization problem, we develop an exact algorithm for the important case of demand in each market segment having (at least) one unique component, and a greedy heuristic for the non-unique component case. Furthermore, we show how to use sensitivity analysis, along with simulation, to fine-tune the solutions. The performance of the model and the solution approach is examined by extensive numerical studies on realistic problem data. We also demonstrate that the model can generate considerable new insights into the key benefits of the CTO
operation, in particular the impact of risk pooling and improved forecast accuracy.