The (α,β)-Precision Theory for Production System Monitoring and Improvement
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In the field of production system engineering, machine parameters, such as Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), machine quality parameter (q), and machine cycle time (τ), are widely used in quantitative methods for production system performance analysis, continuous improvement, and design. Unfortunately, the literature offers no methods for evaluating the smallest number of measurements necessary and sufficient to calculate reliable estimates of these parameters and the induced estimates of system performance metrics, such as machine efficiency (e), throughput (TP), quality parts throughput (TPq), production lead time (LT), and work-in-process (WIP).
This dissertation provides such a method. The approach is based on the concept of (α,β)-precise estimates, where α characterizes the estimate’s accuracy and β its probability. Using this concept, the smallest number of measurements necessary and sufficient to ensure (α,β)-precise estimates of machine parameters or system performance metrics is calculated, and a probabilistic upper bound of the observation time required to collect these measurements is derived.
In addition, the dissertation develops methods for performance analysis and improvement of production systems with cycle overrun and applies the (α,β)-Precision Theory for evaluating the overrun parameters.
The results obtained are illustrated by a case study based on an automotive transmission machining line.
Chair: Professor Semyon M. Meerkov
Remote Link: https://umich.zoom.us/j/92181765148