Bayesian Process Monitoring, Control and Optimization

Abstract

The frequentist Shewhart charts have proved valuable for the first stage of quality improvement in many manufacturing settings. However, their statistical foundation is on a model with exactly known process parameters and independent identically distributed process readings. One or more aspects of this foundation are often lacking in real problems. A Bayesian framework allowing an escape from the independence and the known-parameter assumptions provides a conceptually sounder and more effective approach for process control when one moves away from this first idealization of a process.

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