Monitoring compositional data using multivariate exponentially weighted moving average scheme

2018 ◽  
Vol 34 (3) ◽  
pp. 391-402 ◽  
Author(s):  
Kim Phuc Tran ◽  
Philippe Castagliola ◽  
Giovanni Celano ◽  
Michael B. C. Khoo
Keyword(s):  
Compositional Data ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Average Scheme ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Progressive mean exponentially weighted moving average control chart for monitoring the process location

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nurudeen Ayobami Ajadi ◽  
Osebekwin Asiribo ◽  
Ganiyu Dawodu
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Performance Measure ◽  
Exponentially Weighted Moving Average ◽  
Average Scheme ◽  
Content Type ◽  
The Mean ◽  
Memory Type ◽  
Weighted Moving Average ◽  
Exponentially Weighted

PurposeThis study aims to focus on proposing a new memory-type chart called progressive mean exponentially weighted moving average (PMEWMA) control chart. This memory-type chart is an improvement on the existing progressive mean control chart, to detect small and moderate shifts in a process.Design/methodology/approachThe PMEWMA control chart is developed by using a cumulative average of the exponentially weighted moving average scheme known as the progressive approach. This scheme is designed based on the assumption that data follow a normal distribution. In addition, the authors investigate the robustness of the proposed chart to the normality assumption.FindingsThe variance and the mean of the scheme are computed, and the mean is found to be an unbiased estimator of the population mean. The proposed chart's performance is compared with the existing charts in the literature by using the average run-length as the performance measure. Application examples from the petroleum and bottling industry are also presented for practical considerations. The comparison shows that the PMEWMA chart is quicker in detecting small shifts in the process than the other memory-type charts covered in this study. The authors also notice that the PMEWMA chart is affected by higher kurtosis and skewness.Originality/valueA new memory-type scheme is developed in this research, which is efficient in detecting small and medium shifts of a process mean.

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