Abstract
The nonparametric (NP) control charts are famous for detecting a shift in the process parameters (location and/or dispersion) when the underlying process characteristic does not follow the distributional assumptions. Similarly, when the cost of estimations is very high and the ranking of observational is relatively simple, the ranked set sampling (RSS) technique is preferred over the simple random sampling (SRS) technique. On the other hand, the NP triple exponentially weighted moving average (EWMA) control chart based on SRS is superior to the NP EWMA and NP double EWMA (NP DEWMA) based on the SRS technique to detect a shift in the process location. This study designed an advanced form of NP TEWMA Wilcoxon signed-rank based on RSS, denoted as TEWMA − SR RSS control chart to identify a shift in the process location parameter. The Monte Carlo simulation method is used to assess the performance of the proposed TEWMA − SR RSS control chart along with SRS-based NP TEWMA (TEWMA-SR), SRS-based NP TEWMA sign (TEWMA-SN), SRS-based TEWMA − X ¯ , and RSS-based NP DEWMA-SR DEWMA − SR RSS control charts. The study shows that the proposed TEWMA − SR RSS control chart is more efficient in identifying shifts (especially in small shifts) in the process location than the existing control charts. Finally, a real-life application is also provided for the practical implementation of the proposed TEWMA − SR RSS control chart.
Highlights
Variations are an essential part of every manufacturing and service process, and these variations can be categorized as common and special cause variations. e common cause variations are harmless, and the mechanism or process that operates under these variations is called in-control (IC)
Statistical process control (SPC) tool kit is famous for monitoring the shift in the process parameters
Monte Carlo Simulation. e Monte Carlo simulation is used to obtain the run length (RL) characteristics of the proposed control chart. e simulation algorithm is developed in R software to compute the RL characteristics. 104 random samples of the size n > 1 from any distribution covered by the study for a shift (δ) were generated. e simulation algorithm to determine the nominal values of ARL0 under various distributions can be explained in the following steps
Summary
Zahid Rasheed ,1,2 Hongying Zhang ,1 Muhammad Arslan ,3 Babar Zaman ,4 Syed Masroor Anwar ,5 Muhammad Abid ,6 and Saddam Akber Abbasi 7. E nonparametric (NP) control charts are famous for detecting a shift in the process parameters (location and/or dispersion) when the underlying process characteristic does not follow the distributional assumptions. Is study designed an advanced form of NP TEWMA Wilcoxon signed-rank based on RSS, denoted as TEWMA − SRRSS control chart to identify a shift in the process location parameter. E Monte Carlo simulation method is used to assess the performance of the proposed TEWMA − SRRSS control chart along with SRS-based NP TEWMA (TEWMA-SR), SRS-based NP TEWMA sign (TEWMA-SN), SRS-based TEWMA − X, and RSS-based NP DEWMA-SR (DEWMA − SRRSS) control charts. E study shows that the proposed TEWMA − SRRSS control chart is more efficient in identifying shifts (especially in small shifts) in the process location than the existing control charts. A real-life application is provided for the practical implementation of the proposed TEWMA − SRRSS control chart
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