Poisson Moving Average VersuscChart for Nonconformities

Abstract

The c chart is often used to monitor the number of nonconformities in an inspection unit of product, especially when the chance of an occurrence of a nonconformity is relatively small. However, the c chart is slow in detecting small shifts. In this article, a more efficient alternative based on the construction of a Poisson moving average control chart for the number of nonconformities is suggested. Comparisons between the performances of the new approach in terms of its average run length (ARL) profiles and that of the classical c chart show that the new approach provides faster detection of out-of-control conditions while maintaining a longer in-control ARL. Two examples are given to show how the new approach is put to work in real situations. The first example deals with the case where the standard value for the number of nonconformities; i.e., the mean of the Poisson distribution, c, is known while in the second example, c is unknown and hence is estimated from a preliminary sample of inspection units.

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