Reconfigurable manufacturing systems can rapidly change machine and system configuration in response to market dynamics. However, given machine performance, differently configured systems result in different system performance. Systematic methodologies are required to predict the reliability of reconfigurable machines, and productivity of reconfigurable systems, including the impact of process parameter selection. The objective of this research is to develop general methodologies to analyze non-traditional system configurations for reliability and productivity, and gain insight into trends in their performance.; This research extends the understanding of configuration on system performance, both for machines and the full system. In reliability, a methodology is developed to determine machine reliability by using manufacturing process parameters and physics-of failure concepts. The parameters selected for a given machining process at a given speed influences the reliability of the machine and the system productivity. Physics-of-failure models connect process parameters to reliabilities, and the process plan connects those reliabilities to the system configuration. It is shown that the highest system productivity can occur at less than maximum production rate, and process plan aggressiveness determines the best configuration.; In productivity, Boolean algebra is applied to model arbitrary manufacturing system configurations and predict their productivity, but its use is limited due to high computational overhead. Combinatorial and Markov chain models are developed for the productivity of two classes of system configurations, parallel-serial and reserve capacity. The parallel-serial configuration class is serial lines set in parallel with and without crossover, and hybrid lines combining segments with both. Synergistic productivity improvements result from partial production states not in pure serial lines, and is dependent on machine availability. Adding parallel lines produces diminishing returns while reducing overall productivity variability. The reserve capacity configuration class is serial lines with standby machines that can perform any operation in the main production line should a machine fail. Standby machines add significantly to productivity, but again at a diminishing return. An optimal number of standby machines occur for a given main line length. Parallel-serial configurations derive higher productivity from each standby machine due to their bottleneck mitigation.
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