New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs

Contemporarily, enterprises strive to continuously enhance quality which is a basis of customer satisfaction. Numerous advancements to the control charting scheme have been made to enhance process monitoring. In this thesis, the variable parameters chart with auxiliary information (abbreviated as VP-AI) is proposed. The VP-AI chart is designed with a regression estimator that has an improved precision due to the use of auxiliary variable to estimate the population mean. By adopting the Markov chain method, the average time to signal (ATS) and expected ATS (EATS) formulae are derived for known and unknown shift sizes. The findings show that the VP-AI chart prevails over the basic VP chart and justifies the integration of auxiliary information to improve the sensitivity of the VP chart. A comparison of the VP-AI chart with its competing charts shows that, for all shifts, the performance of the VP-AI chart surpasses the Shewhart AI (SH-AI), synthetic AI (SYN-AI) and variable sample size and sampling interval AI (VSSI-AI) charts considerably. Additionally, for most shifts, the VP-AI chart has a superior performance in comparison with the exponentially weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The application of the VP-AI chart is shown using an illustrative example based on a real dataset. In many situations, the process is multivariate in nature, where more than one quality characteristic has to be monitored simultaneously. Furthermore, many companies have adopted the short production runs technique to be more flexible and specialized. Hence, in this thesis, the fixed sample size (FSS) 2 T short-run chart is develope

New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs

Contemporarily, enterprises strive to continuously enhance quality which is a basis of customer satisfaction. Numerous advancements to the control charting scheme have been made to enhance process monitoring. In this thesis, the variable parameters chart with auxiliary information (abbreviated as VP-AI) is proposed. The VP-AI chart is designed with a regression estimator that has an improved precision due to the use of auxiliary variable to estimate the population mean. By adopting the Markov chain method, the average time to signal (ATS) and expected ATS (EATS) formulae are derived for known and unknown shift sizes. The findings show that the VP-AI chart prevails over the basic VP chart and justifies the integration of auxiliary information to improve the sensitivity of the VP chart. A comparison of the VP-AI chart with its competing charts shows that, for all shifts, the performance of the VP-AI chart surpasses the Shewhart AI (SH-AI), synthetic AI (SYN-AI) and variable sample size and sampling interval AI (VSSI-AI) charts considerably. Additionally, for most shifts, the VP-AI chart has a superior performance in comparison with the exponentially weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The application of the VP-AI chart is shown using an illustrative example based on a real dataset. In many situations, the process is multivariate in nature, where more than one quality characteristic has to be monitored simultaneously. Furthermore, many companies have adopted the short production runs technique to be more flexible and specialized

A study on the run length properties of the side sensitive group runs double sampling (SSGRDS) control chart

The side sensitive group runs double sampling (SSGRDS) chart incorporates the control charting concepts of the side sensitive group runs (SSGR) and double sampling (DS) charts. The SSGRDS chart which combines the efficiency of its basic charts is an effective approach to increase the speed of mean shift detection. The performance of the SSGRDS chart, based on the average number of observations to signal (ANOS), median number of observations to signal (MNOS) and percentiles of the number of observations to signal (PNOS) is investigated in this paper. Based on the results obtained, it is found that the SSGRDS chart becomes more sensitive in detecting mean shifts with an increase in the size of the process mean shift. With the use of MNOS and PNOS to measure the performance of the SSGRDS chart, the entire run length distribution is considered and this leads to a more complete understanding of the performance of the chart. The findings in this paper will provide a clearer picture on the run length properties of the SSGRDS chart which will facilitate practitioners in using the chart

Hotelling's T 2 control charts with fixed and variable sample sizes for monitoring short production runs

International audienc

A study on the run length properties of the side sensitive group runs double sampling (SSGRDS) control chart

The side sensitive group runs double sampling (SSGRDS) chart incorporates the control charting concepts of the side sensitive group runs (SSGR) and double sampling (DS) charts. The SSGRDS chart which combines the efficiency of its basic charts is an effective approach to increase the speed of mean shift detection. The performance of the SSGRDS chart, based on the average number of observations to signal (ANOS), median number of observations to signal (MNOS) and percentiles of the number of observations to signal (PNOS) is investigated in this paper. Based on the results obtained, it is found that the SSGRDS chart becomes more sensitive in detecting mean shifts with an increase in the size of the process mean shift. With the use of MNOS and PNOS to measure the performance of the SSGRDS chart, the entire run length distribution is considered and this leads to a more complete understanding of the performance of the chart. The findings in this paper will provide a clearer picture on the run length properties of the SSGRDS chart which will facilitate practitioners in using the chart

Synthetic control chart with curtailment for monitoring shifts in fraction nonconforming

International audienc