Improved Non-Parametric Double Homogenously Weighted Moving Average Control Chart for Monitoring Changes in Process Location - Oladipupo, Olayinka O.; Adekeye, Kayode Samuel; Olaomi, John O.; Alayande, Semiu A. (Statistics, Optimization & Information Computing, 2025)
Parametric control charts’ statistical performance often raises concerns when dealing with processes that lack a predefined probability distribution. In such cases, non-parametric control charts emerge as a viable alternative. Additionally, the adoption of ranked set sampling, with its ability to reduce process parameter variability and enhance control chart performance, proves to be advantageous over traditional simple random sampling techniques. In the field of statistical process control (SPC), control charts are essential tools used to monitor and improve process performance. Among various control charts, the double homogeneously weighted moving average (DHWMA) control chart is recognized for its capability to detect small shifts in process parameters. The study focusses on enhancing the sensitivity and robustness of the NPDHWARSS for detection of shift in process mean and make it more reliable across diverse applications. This study aims to improve the non-parametric double homogeneously weighted moving average control chart, employing the Wilcoxon signed rank test and leveraging the ranked set sampling method, referred to as NPIDHWMA-WSR in this paper. To evaluate the efficiency of the proposed control chart, a comparison was conducted against non-parametric double exponentially weighted moving average using signed rank test (NPRDEWMA-SR) and non-parametric double homogeneously weighted moving average (NPDHWMA) control charts. The comparative analysis highlights the superior performance of the proposed NPIDHWMA-WSR control chart, especially in scenarios involving minimal to moderate changes in process location, as evidenced by metrics such as average run length (ARL), standard deviation run length (SDRL), and median deviation run length (MDRL). Additionally, the study presents a practical application, providing skilled practitioners with tangible evidence of the chart’s effectiveness in maintaining both product and process quality. Moreover, the NPIRDHWMA-WSR control chart identified an out-of-control (OOC) condition by the 18th sample, whereas the competing NPDHWMA-RSS control chart didn’t signal OOC until the 22nd sample.