scholarly journals Exponentially Weighted Moving Average Control Charts for the Process Mean Using Exponential Ratio Type Estimator

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hina Khan ◽  
Saleh Farooq ◽  
Muhammad Aslam ◽  
Masood Amjad Khan
Keyword(s):  
Control Charts ◽  
Sampling Design ◽  
Control Structure ◽  
Moving Average ◽  
Auxiliary Information ◽  
Ranked Set Sampling ◽  
Process Mean ◽  
Average Control ◽  
The Mean ◽  
Weighted Moving Average

This study proposes EWMA-type control charts by considering some auxiliary information. The ratio estimation technique for the mean with ranked set sampling design is used in designing the control structure of the proposed charts. We have developed EWMA control charts using two exponential ratio-type estimators based on ranked set sampling for the process mean to obtain specific ARLs, being suitable when small process shifts are of interest.

Monitoring process mean and variability with generally weighted moving average control charts

2009 ◽  
Vol 57 (1) ◽  
pp. 401-407 ◽  
Author(s):  
Shey-Huei Sheu ◽  
Shih-Hung Tai ◽  
Yu-Tai Hsieh ◽  
Tse-Chieh Lin
Keyword(s):  
Control Charts ◽  
Moving Average ◽  
Process Mean ◽  
Monitoring Process ◽  
Average Control ◽  
Weighted Moving Average

scholarly journals A New Sum of Squares Exponentially Weighted Moving Average Control Chart Using Auxiliary Information

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1888
Author(s):  
Jen-Hsiang Chen ◽  
Shin-Li Lu
Keyword(s):  
Control Charts ◽  
Moving Average ◽  
Auxiliary Information ◽  
Sum Of Squares ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Industrial Management ◽  
Run Lengths ◽  
Weighted Moving Average ◽  
Exponentially Weighted

The concept of control charts is based on mathematics and statistics to process forecast; which applications are widely used in industrial management. The sum of squares exponentially weighted moving average (SSEWMA) chart is a well-known tool for effectively monitoring both the increase and decrease in the process mean and/or variability. In this paper, we propose a novel SSEWMA chart using auxiliary information, called the AIB-SSEWMA chart, for jointly monitoring the process mean and/or variability. With our proposed chart, the attempt is to enhance the performance of the classical SSEWMA chart. Numerical simulation studies indicate that the AIB-SSEWMA chart has better detection ability than the existing SSEWMA and its competitive maximum EWMA based on auxiliary information (AIB-MaxEWMA) charts in view of average run lengths (ARLs). An illustrated example is used to demonstrate the efficiency of the proposed AIB-SSEWMA chart in detecting small process shifts.

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