The Quality Loss Index QLI and Its Properties
-
David D. Hanagal
and
Jagadiswar Samajdwar
Abstract
This article investigates the quality loss index QLI proposed by Samajdwar et al. for normal variables. The probability distribution of QLI and its components are obtained. The first component represents a quality loss index for the process variability, while the second component represents a quality loss index for the shift in the process mean. Moreover, a method is derived allowing to estimate the nonconformance probability in either side of the target value for normal variables. Finally, an additive property of the quality loss index is established, which enables to define an composite quality loss index and use it within a continual process improvement strategy. The composite quality loss index indicates the process performance and identifies the parameter(s) which show a potential for improvement.
© de Gruyter 2011
Articles in the same Issue
- Editorial
- Control Charts Based on the g-and-h Distribution
- Economic Reliability Group Acceptance Sampling Plans Based on the Inverse-Rayleigh and the Log-Logistic Distributions
- Use of Auxiliary Information in Estimating the Finite Population Mean in Survey Sampling
- One-Sided Cumulative Sum (CUSUM) Control Charts for the Zero-Truncated Binomial Distribution
- Significance Test for the Half Logistic Distribution
- The Quality Loss Index QLI and Its Properties
- Statistical Quality Control Limits for the Sample Mean Chart Using Robust Extreme Ranked Set Sampling
Articles in the same Issue
- Editorial
- Control Charts Based on the g-and-h Distribution
- Economic Reliability Group Acceptance Sampling Plans Based on the Inverse-Rayleigh and the Log-Logistic Distributions
- Use of Auxiliary Information in Estimating the Finite Population Mean in Survey Sampling
- One-Sided Cumulative Sum (CUSUM) Control Charts for the Zero-Truncated Binomial Distribution
- Significance Test for the Half Logistic Distribution
- The Quality Loss Index QLI and Its Properties
- Statistical Quality Control Limits for the Sample Mean Chart Using Robust Extreme Ranked Set Sampling
