Trend rules or zone rules may be used to introduce some memory which results in faster detection of small shifts. Previous observations do not influence the probability of future out-of-control signals. Comparison of Statistical PerformanceĪ major disadvantage of the Shewhart-type control charts is that they only use information about the process in the last plotted point and therefore these charts have no memory. Case 2: An I-MR chart shows an in-control process while the time-weighted charts show a clear upward trend in process data. Case 1:An I-MR chart shows an out-of-control process while no such signs are seen in the time-weighted control charts. It is tough to make a decision if the intent of the analysis is not understood. The data is analyzed using an I-MR chart as well as an EWMA chart and the inferences drawn are contradictory. The following data sets provide an example of the different conclusions reached by two different control charts. Example of Different Control Charts Results When and how to use a time-weighted control chart has always been an area of confusion for quality supervisors in production lines (operational understanding) as well as SPC practitioners (comparison of statistical performance). Practitioners often do not focus enough on the “intent” of using a particular type of control chart which may lead to incorrect interpretation of results. Yet, the intent and method of application for both types of time-weighted charts are entirely different. For example, the same data set can be analyzed using an individual-moving range (I-MR) chart as well as time-weighted control charts like an exponentially weighted moving average (EWMA) chart or a cumulative sum (cusum) control chart. This is particularly the case when using time-weighted control charts. The confusion increases with applicability of two different control charts for the same data. With the wide range of control chart options available, the selection of the chart that best suits a particular process can be a difficult task. Selecting the wrong type can result in many false alarms, leading to expensive and fruitless searches for assignable causes. Which chart to use depends mainly on the classification of the data, the type of underlying distribution and the intent of the application. ![]() Selecting the right type of control chart is a vital starting point for statistical process control (SPC).
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