Abstract
A Markov chain model to analyze quality in flexible manufacturing systems with batch productions is developed in this article. The cycles when good quality and defective parts are produced are defined as the good and defective states, respectively, and transition probabilities are introduced to characterize the changes between these states. The product quality is presented as a function of these transition probabilities, and the transition that has the largest impact on quality is referred to as the quality bottleneck transition (BN-t). Analytical expressions to quantify the sensitivity of quality with respect to transition probabilities are derived, and indicators to identify the BN-t based on data collected on the factory floor are developed. Through extensive numerical experiments, it is shown that such indicators have a high accuracy in identifying the correct bottlenecks and can be used as an effective tool in quality improvement efforts. Finally, a case study at an automotive paint shop is presented to illustrate the applicability of the method.