Abstract
This paper proposes one-sided coefficient of variation (CV) charts with a linearly covariate error model. The one-sided CV charts are the downward and upward CV charts, which detect decreasing and increasing shifts, respectively. Ignoring the presence of measurement error leads to erroneous conclusions regarding the average run length, especially for small number of measurements per item. However, when measurement error is not ignored in computing the control limits, the performance between the CV charts with and without measurement error is similar. It is recommended to adopt the methods proposed in this paper as a general procedure since in the absence of measurement error, the control limits can be set by letting the size of the measurement error to be zero, whereas traditional methods which assume no measurement error will result in dubious results in the presence of measurement error. Finally, the proposed chart is applied on an illustrative example.
| Original language | English |
|---|---|
| Pages (from-to) | 353-377 |
| Number of pages | 25 |
| Journal | Quality Technology and Quantitative Management |
| Volume | 14 |
| Issue number | 4 |
| Early online date | 30 Mar 2017 |
| DOIs | |
| Publication status | Published - 2 Oct 2017 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Average run length
- coefficient of variation chart
- linearly covariate error model
- measurement error
- multiple measurements
ASJC Scopus subject areas
- Business and International Management
- Industrial relations
- Management Science and Operations Research
- Information Systems and Management
- Management of Technology and Innovation
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