Abstract
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.
Highlights
Statistical process control (SPC) is a statistical method application, which implies monitoring the status of a process, and the ability to maintain process stabilization by distinguishing between common causes and assignable causes of variation in a process (Montgomery [1])
The performance of a control chart is generally measured in terms of its average run lengths (ARLs) and standard deviation of the run length (SDRL)
We proposed a novel sum of squares exponentially weighted moving average chart, named as the auxiliary information-based (AIB)-SSEWMA chart, for effectively monitoring the small process mean and/or variability shifts
Summary
Statistical process control (SPC) is a statistical method application, which implies monitoring the status of a process, and the ability to maintain process stabilization by distinguishing between common causes and assignable causes of variation in a process (Montgomery [1]). Crowder [3], Ng and Case [4], Lucas and Saccucci [5], Steiner [6], and Sheu and Lin [7] developed extended EWMA charts to improve the performance of detecting small process mean shifts. Riaz [22], Ahmad et al [23], and Riaz [24] developed AIB-Shewhart mean charts using regression estimators, ratio-type, and location estimators, respectively, for monitoring process mean shifts. Haq [27] utilized regression estimators to develop an AIB-EWMA chart to monitor increases and/or decreases in the process dispersion. Haq [34] first introduced the AIB-MaxEWMA chart for simultaneously detecting both increases and decreases in the mean and/or dispersion of a process.
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