An EWMA control chart using two parametric ratio estimator

Abstract The control chart is one of the important industrial tools used for monitoring the manufacturing process. The performance of control chart may enhance when some additional information about variable under study is available. In this article, a new two parametric ratio estimator is introduced to design the exponentially weighted moving average control chart. The proposed control chart is more efficient than the existing control chart in detecting small and moderate shifts in the process mean at Phase II. The application of proposed control chart in the industry is given with the aid of a real example.

[1]  Connie M. Borror,et al.  Poisson EWMA Control Charts , 1998 .

[2]  Saddam Akber Abbasi,et al.  A new nonparametric EWMA sign control chart , 2012, Expert Syst. Appl..

[3]  Housila P. Singh,et al.  Use of Transformed Auxiliary Variable in Estimating the Finite Population Mean , 1999 .

[4]  Muhammad Riaz,et al.  Monitoring process variability using auxiliary information , 2008, Comput. Stat..

[5]  Javid Shabbir,et al.  An Efficient Shewhart-Type Control Chart to Monitor Moderate Size Shifts in the Process Mean in Phase II , 2016, Qual. Reliab. Eng. Int..

[6]  Jyoti Divecha,et al.  Modified exponentially weighted moving average (EWMA) control chart for an analytical process data , 2011 .

[7]  Muhammad Riaz,et al.  A process variability control chart , 2009, Comput. Stat..

[8]  Michael B. Miller Linear Regression Analysis , 2013 .

[9]  S. W. Roberts,et al.  Control Chart Tests Based on Geometric Moving Averages , 2000, Technometrics.

[10]  Exponential Estimators for Population Mean Using the Transformed Auxiliary Variables , 2015 .

[11]  James M. Lucas,et al.  Exponentially weighted moving average control schemes: Properties and enhancements , 1990 .

[12]  M. Rueda Garcia,et al.  Repeated substitution method: The ratio estimator for the population variance , 1996 .

[13]  Zhengyan Lin,et al.  Enhancing the Mean Ratio Estimators for Estimating Population Mean Using Non-Conventional Location Parameters , 2016 .

[14]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.

[15]  Housila P. Singh,et al.  A new procedure for variance estimation in simple random sampling using auxiliary information , 2013 .

[16]  B. J. Mandel The Regression Control Chart , 1969 .

[17]  Chi-Hyuck Jun,et al.  A control chart using an auxiliary variable and repetitive sampling for monitoring process mean , 2015 .

[18]  Muhammad Riaz,et al.  An improved control chart structure for process location parameter , 2011, Qual. Reliab. Eng. Int..

[19]  S. Steiner EWMA Control Charts with Time-Varying Control Limits and Fast Initial Response , 1999 .

[20]  V. D. Naik,et al.  A general class of estimators for estimating population mean using auxiliary information , 1991 .

[21]  Marianne Frisén,et al.  Statistical Surveillance. Optimality and Methods , 2003 .

[22]  Muhammad Riaz,et al.  Enhancing the performance of EWMA charts , 2011, Qual. Reliab. Eng. Int..

[23]  Cem Kadilar,et al.  Ratio estimators in simple random sampling , 2004, Appl. Math. Comput..

[24]  Muhammad Riaz,et al.  An EWMA-Type Control Chart for Monitoring the Process Mean Using Auxiliary Information , 2014 .

[25]  E. S. Page CONTINUOUS INSPECTION SCHEMES , 1954 .

[26]  Muhammad Riaz,et al.  Monitoring process mean level using auxiliary information , 2008 .