Performance Monitoring Through In-Process Product Quality Measurement
A production process that appears to be in statistical control can exhibit significant change in its actual performance. This is particularly true when performance must be inferred through attribute sampling, i.e. binary measurement of in-process product quality. This paper addresses the problem of inference of process performance based on such regular quality inspections. The partially observed Markov Chain model, proposed in , and elsewhere in connection with a closely related problem (continuous inspection sampling), is suggested as an appropriate framework. Conditions for successful quality monitoring using this model are investigated, such as choice of model order end parameter estimation. These issues are studied via simulation, incorporating maximum likelihood estimation techniques developed in . A numerical procedure that accelerates the convergence of this algorithm is introduced. Application of the methodology to production date is briefly discussed.
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