GOALI: Inventory Management in Assemble-to-Order Systems: Analysis, Policies, and Asymptotic Optimality

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The objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) award is to develop an optimization framework for inventory management in Assemble-to-Order manufacturing systems with general structures and parameter values. The basic approach is to employ a stochastic program to transform an intractable control problem into a much simpler solvable optimization problem. The optimal solution of the stochastic program, which sets a provable lower bound on the long-run average expected total inventory cost, will be used to inspire a family of inventory control policies for managing dynamic systems. These policies will be tested by the criterion of asymptotic optimality on the diffusion scale. In other words, when component lead times increase, thus making the system more costly, the proposed policies will drive the percentage difference between the inventory cost and its lower bound to zero. Performance of these policies in various parameter regions will also be evaluated empirically by numerical experiments, simulations, and case studies, including comparisons with other approaches and stress-tests under the least favorable conditions. Algorithms involving approximations will be developed to allow the approach to be implementable on an industrial scale.If successful, the results of this research will help manufacturing companies to achieve significant savings in inventory cost, thus improving the efficiency of the widely-adopted Assemble-to-Order manufacturing strategy. Because the policies are particularly effective for systems with long lead times, they will mitigate negative impacts on the inventory system of long transportation delays between manufacturers and their oversea suppliers and thus lead to changes in the cost-benefit calculation that favor insourcing, which will contribute to the US manufacturing sector's revitalization. The combined use of stochastic programs and asymptotic analysis also points to a promising new approach for inventory management optimization, which involves many notoriously hard problems.