Quality and Reliability Engineering International · 2012 · 34 citations · 17 references
EngineeringIndustrial EngineeringOperations ResearchStochastic SimulationStochastic ProcessesPoisson CountsSystems EngineeringStatisticsQuantitative ManagementProcess MeasurementProcess MonitoringProcess AnalysisProbability TheoryProduction ControlService IndustriesStochastic ModelingAutocorrelation StructureProcess ControlBusinessIndustrial InformaticsIndustrial Process ControlPoisson Inar
Count data processes are often encountered in manufacturing and service industries. To describe the autocorrelation structure of such processes, a Poisson integer‐valued autoregressive model of order 1, namely, Poisson INAR(1) model, might be used. In this study, we propose a two‐sided cumulative sum control chart for monitoring Poisson INAR(1) processes with the aim of detecting changes in the process mean in both positive and negative directions. A trivariate Markov chain approach is developed for exact evaluation of the ARL performance of the chart in addition to a computationally efficient approximation based on bivariate Markov chains. The design of the chart for an ARL‐unbiased performance and the analyses of the out‐of‐control performances are discussed. Copyright © 2012 John Wiley & Sons, Ltd.
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