The control of quality in a temporal setting



Quality management and improvement involves time in a number of ways. To monitor systems in their inter-temporal perspective, it is necessary to develop models which represent the process of change and which can be used to measure and monitor a process. Measurements (through sampling, control charts and any other method) may then be used to track and detect variations which may be unexpected, and which would require special attention. In Chapter 6, we noted that the approach underlying the application of control charts was the ‘search for observations deviating from expectations’. To do so, we presumed that processes were stable and sought to devise ‘tests’, ‘probability assessments’, etc. which will reject our presumption that the process or variable being charted were stable. In fact, non-stationarities of various sorts, poor representation of the underlying process, collinearity over time etc. make it necessary to represent the temporal dependence such processes exhibit. Models of various sorts can then be devised to better represent and analyse shifting patterns of data over time, using available statistical means. There are many approaches and methods we can use in such circumstances.To this end, we introduce some basic notions of filtering theory and control charts of processes such as moving average charts, EWMA (exponentially weighted moving average) and ARIMA (Auto regressive and Moving Average Models) and related models.


Control Chart Move Average Temporal Setting Exponentially Weighted Move Average CUSUM Chart 
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Copyright information

© Charles S. Tapiero 1996

Authors and Affiliations

  1. 1.Ecole Supérieure des Sciences Economiques et CommercialesParisFrance

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