Easily Implementable Inventory Control Policies
This work was initiated and supported by a manufacturer of mail processing equipment, which stocks 30,000 distinct parts in a distribution center to support field maintenance of their equipment. To find an effective stocking policy for this system we formulate a constrained optimization model with the objective of minimizing overall inventory investment at the distribution center subject to constraints on customer service and order frequency. Because size, integrality, and nonconvexity make this problem intractable to exact analysis, we develop three heuristic algorithms based on simplified representations of the inventory and service expressions. These lead to what we call easily implementable inventory policies, in which the control parameters for a newly introduced part can be computed in closed form without reoptimizing the rest of the system. Numerical comparisons against a lower bound on the cost function show that even our simplest heuristic works well when a high service level is required. However, we show that a more sophisticated heuristic is more robustly accurate. We also compare our heuristics to methods previously in use by the firm whose system motivated this research and show that they are more efficient in the sense of attaining the same customer service level with a 20–25% smaller inventory investment. Finally, we discuss implementation issues related to the specific needs of the client firm, such as how to handle parts with no or low recent usage and dynamically changing demand for parts.