Learning and Bayesian Updating in Long Cycle Made-To-Order (MTO) Production

We model production planning for made-to-order (MTO) manufacturing by choosing production rate to minimize expected discounted cost incurred up to a promised delivery date. Products that are MTO are often unique and customized. The associated learning curve slope and other production parameters cannot be precisely estimated before production starts. In this paper, a dynamic and adaptive approach to estimate the effects of learning and to optimize next period production is developed. This approach offers a closed-loop solution through stochastic dynamic programming. Monthly production data are used to update the joint probability distributions of production parameters via Bayesian methods. Our approach is illustrated using historical earned-value data from the Black Hawk Helicopter Program. Managerial insights are obtained and discussed.