Bayesian Process Control for Attributes

We consider a process control procedure with fixed sample sizes and sampling intervals, where the fraction defective is the quality variable of interest, a standard attributes control chart methodology. We show that relatively standard cost assumptions lead to formulation of the process control problem as a partially observed Markov decision process, where the posterior probability of a process shift is a sufficient statistic for decision making. We characterize features of the optimal solution and show that the optimal policy has a simple control limit structure. Numerical results are provided which indicate that the procedure may provide significant savings over non-Bayesian techniques.

• 저널Management Science · 41(4) · 637–645
• 토픽Advanced Statistical Process Monitoring · Statistics, Probability and Uncertainty
• DOI10.1287/mnsc.41.4.637
• 저자Joel M. Calabrese