This Grant Opportunity for Academic Liaison with Industry (GOALI) award provides funding for the establishment of an analytical framework to investigate the impact of flexibility on product quality in flexible manufacturing systems and application of the results to production systems at General Motors. Specifically, the work includes deriving a method to address the analytical modeling for quality performance in flexible manufacturing systems, developing an approach to Identify the machine and the product type that impede the quality performance in the strongest manner, i.e., bottleneck machine and product, investigating the appropriate batch size or production sequence so that both the quality requirements and customer orders are satisfied, and finally, implementing the techniques to the painting and machining operations at General Motors manufacturing plants. The approach of the research would be based on analytical investigation of Markov processes that describe the flexible systems at hand. Quality performance will be modeled as a function of transitions among multiple states characterized by product types and sequences, and propagation of quality variations among the machines. The challenge of this research lies in discovering an appropriate mathematical description of such function and its arguments.
If successful, the results of this research will establish a novel analytical method to study the interaction between flexibility and quality, and to provide insights for production system design from the point of view of quality. Such a method will enable us to evaluate quality performance, identify bottlenecks, and design scheduling policies (sequencing and batching) to achieve the best quality and reliable demand satisfaction. The successful completion of this research will open up a new direction in manufacturing systems research and build a solid foundation for integrated study of cost, productivity, quality and flexibility. Furthermore, it will provide production engineers with quantitative tools for analysis, improvement, and design of flexible manufacturing systems with respect to quality. Finally, the results obtained will be applicable not only to automotive but also to other manufacturing industries (e.g., semiconductor, appliance, etc.) as well.