The Case against Generally Weighted Moving Average (GWMA) Control Charts

TL;DR

This paper critiques the use of GWMA control charts, highlighting their computational inefficiency and lack of advantages over simpler EWMA charts, which often perform better.

Contribution

The paper provides a critical analysis of GWMA control charts, demonstrating their drawbacks and advocating for the use of simpler, more effective EWMA charts.

Findings

GWMA charts lack recursive formulas, requiring all past data storage.

GWMA charts do not have the Markov property, necessitating simulation for performance evaluation.

EWMA charts offer comparable or superior performance with simpler implementation.

Abstract

We argue against the use of generally weighted moving average (GWMA) control charts. Our primary reasons are the following: 1) There is no recursive formula for the GWMA control chart statistic, so all previous data must be stored and used in the calculation of each chart statistic. 2) The Markovian property does not apply to the GWMA statistics, so computer simulation must be used to determine control limits and the statistical performance. 3) An appropriately designed, and much simpler, exponentially weighted moving average (EWMA) chart provides as good or better statistical performance. 4) In some cases the GWMA chart gives more weight to past data values than to current values.