Concurrent Design for Optimal Quality and Cycle

Product and manufacturing system design are the core issues in product development and dominate the profitability of a company. In order to assess and optimize the product and manufacturing system design, an objective evaluation framework is needed. Despite the many existing tools for product and manufacturing system design, there is a missing link between the product design and the production performances under system variability. The goal of the thesis is to explore and understand the interactions among part design and tolerancing, processes and system variability, and system control decision, then provide an integrated model to assess the total cost in a system. This model will be used to aid part design, tolerancing, batching, as well as strategy analysis in process improvement. A two-stage modeling approach is used to tackle the problem: quality prediction and production prediction. The quality prediction model projects the process variations into the output quality variations at each manufacturing stage, then predict the yield rate from the stochastic behavior of the variations and the tolerance. The production prediction model projects the demand rate and variability, processing times and variability, yield rates and batch-sizes into the manufacturing cycle time and inventories. After the performances are predicted through the previous two models, concurrent optimization of part design, tolerance, and batch-sizes are achieved by varying them to find the minimum cost. A case study at Boeing Tube shop is used to illustrate this approach. The result shows that the costless decisions in part design, tolerancing, and batchsizes can significantly improve the system performance. In addition, conducting them separately or without using the system performance as the evaluation criteria may only lead to the local optima. Thesis Committee: Prof. Anna Thornton (Chairperson) Prof. Mary Boyce Dr. Stanley Gershwin Prof. David Hardt