Bayesian Process Control for Attributes

Joel M. Calabrese
Additional contact information
Joel M. Calabrese: Department of Business Analysis and Computing Systems, San Francisco State University, San Francisco, California 94132

Management Science, 1995, vol. 41, issue 4, 637-645

Abstract: 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.

Keywords: process control; quality control; economic design; machine maintenance; partially observed Markov decision processes; dynamic programming (search for similar items in EconPapers)
Date: 1995
References: Add references at CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.41.4.637 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:41:y:1995:i:4:p:637-645

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().