Abstract
This research aims to propose a nonparametric double exponentially weighted moving average (NDEWMA) control chart for detecting shifts in a process using the Mood statistic. A Monte Carlo simulation study was used. The data had a normal distribution and a non-normal distribution where the magnitude of shift size (δ) was δ = 1, 1.05, 1.10 - 2.00 and 3.00, respectively. A performance comparison of the control charts was evaluated by using the Average Run Length (ARL). In the case of an in-control process, a large ARL value is desired, while a small ARL value is desired when the process is out-of-control. From the results of the simulation study, it was observed that the proposed NDEWMA control chart is effective in detecting small shifts in the process and gives better performance compared to the nonparametric exponentially weighted moving average control (NEWMA) control chart based on Mood statistics.
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