Abstract
In practice most processes are known to produce autocorrelated observations. Autocorrelation degrades the performance of control charts by producing frequent false alarms when the process is stable or makes the charts respond slowly to the detection of out-of-control states. The effects due to autocorrelation can be eliminated by using modified charts. In this procedure, the control limits of the traditional charts are adjusted to account for the autocorrelation. In this paper, we present the Mixed EWMA-CUSUM and Mixed CUSUM-EWMA modified charts for monitoring correlated data. The performance of these charts are compared to existing modified charts such as the Shewhart, CUSUM, EWMA, combined Shewhart-CUSUM and combined Shewhart-EWMA schemes using the average run length, extra quadratic loss and relative average run length measures. Examples are given to illustrate how the charts perform.
| Original language | English |
|---|---|
| Pages (from-to) | 429-453 |
| Number of pages | 25 |
| Journal | Quality Technology and Quantitative Management |
| Volume | 14 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2 Oct 2017 |
Bibliographical note
Publisher Copyright:© 2017 International Chinese Association of Quantitative Management.
Keywords
- Autocorrelation
- average run length
- mean shift
- mixed CUSUM-EWMA
- mixed EWMA-CUSUM
- statistical process control
ASJC Scopus subject areas
- Business and International Management
- Industrial relations
- Management Science and Operations Research
- Information Systems and Management
- Management of Technology and Innovation