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 — in fact, CTO has made irrelevant the whole notion of pre-configured machine types — and focuses instead on maintaining the right amount of inventory at the components. Indeed, 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. Research undertaken while an academic visitor at IBM Research Division, T.J. Watson Research Center.