Adjustment of Control Limits for Geometric Charts
- Authors
- Kim, Byung Jun; Lee, Jaeheon
- Issue Date
- Sep-2015
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- average run length (ARL); control limits; geometric chart; high-quality process; Phase I; statistical process control
- Citation
- COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.22, no.5, pp 519 - 530
- Pages
- 12
- Journal Title
- COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
- Volume
- 22
- Number
- 5
- Start Page
- 519
- End Page
- 530
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/9147
- DOI
- 10.5351/CSAM.2015.22.5.519
- ISSN
- 2287-7843
2383-4757
- Abstract
- The geometric chart has proven more effective than Shewhart p or np charts to monitor the proportion nonconforming in high-quality processes. Implementing a geometric chart commonly requires the assumption that the in-control proportion nonconforming is known or accurately estimated. However, accurate parameter estimation is very difficult and may require a larger sample size than that available in practice in high-quality process where the proportion of nonconforming items is very small. Thus, the error in the parameter estimation increases and may lead to deterioration in the performance of the control chart if a sample size is inadequate. We suggest adjusting the control limits in order to improve the performance when a sample size is insufficient to estimate the parameter. We propose a linear function for the adjustment constant, which is a function of the sample size, the number of nonconforming items in a sample, and the false alarm rate. We also compare the performance of the geometric charts without and with adjustment using the expected value of the average run length (ARL) and the standard deviation of the ARL (SDARL).
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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