| Summary: | 碩士 === 輔仁大學 === 應用統計研究所 === 89 === Nowadays, the capability to rapidly produce products in large quantities and low costs are essential requirements for industries to complete in such a highly competitive and customer-oriented environment. And all industries are trying their best to seek better production methods in order to reduce their production costs. Among them, the multiple stream process is getting more and more attention since it can satisfy the above-mentioned requirements. Therefore the monitoring and detecting of assignable causes in a multiple stream process is an important issue for both the researcher and industry processes. This study applies the methods of Run Test and Shewhart control chart to monitor the multiple stream process. This study uses a set of simulations to compare the performance of these two methods when the multiple steam process is out of control. The research findings indicate that while the Run Test method is more sensitive when one individual filling head has gone wrong, the performance of a modified Shewhart control chart is more effective when all of the filling heads are out of control. But both of them are restricted and lack enough detecting capability in some situations. In order to solve the issue of the above-mentioned drawbacks, this research proposes an alternative approach in monitoring the multiple stream process. By the ability of modeling complicated system and its generalization capability, the artificial neural networks (NNs) is used to make up the inadequacy of the traditional Run Test and Shewhart control charts, A set of simulations are used to compare the performance of NNs, Run Test and Shewhart control chart when the Multiple stream process is out of control. The case considered in the simulations are 3 filling, 5 filling and 7 fillings heads of a multiple stream process. The research findings indicate that the proposed NNs are more sensitive in detecting the out-of-control signal in terms average run length (ARL) than the alternative approaches.
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