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Abstract
The study proposes a control chart namely G-Chart based on Gini’s mean difference for monitoring the changes in a process variability assuming normality of the quality characteristic to be monitored. The design structure of G-Chart is derived and its comparison is made with those of well-known R and S charts. It is observed G-Chart needs no coefficient as opposed to R and S charts which need d2 and c4 coefficients respectively to find an unbiased estimate of the process standard deviation σ. Using the power curves as a performance measure it is observed that G-Chart is more powerful than R-Chart and is very close competitor to the S-Chart in terms of discriminatory power for detecting shifts in process variability. The non-normality effect is also examined on the three charts and it is observed that G-Chart is least affected by the departure from normality.
Additional information
Notes on contributors
Muhammad Riaz
Muhammad Riaz Lecturer in the Department of Statistics at Quaid-i-Azam University, Pakistan. His research interests include Statistical Process Control, Statistical inference, Experimental Designs, Non-Parametric statistical techniques, and Computational statistics.
Aamir Saghirr
Aamir Saghirr Department of Statistics at Quaid-i-Azam University, Pakistan. His research interests include Time Series Analysis, Statistical Process Control and Sample Survey.
