scholarly journals An Approach to Construct a Control Chart for Standard Deviation Based on Six Sigma

2016 ◽  
Vol 2 (1) ◽  
pp. 52-57
Author(s):  
Radhakrishnan R. ◽  
◽  
Balamurugan P. ◽  
Keyword(s):  
Standard Deviation ◽  
Control Chart ◽  
Six Sigma

Control chart for high-quality processes based on Six Sigma quality

2017 ◽  
Vol 34 (1) ◽  
pp. 2-17 ◽  
Author(s):  
Ravichandran Joghee
Keyword(s):  
Standard Deviation ◽  
Control Chart ◽  
Six Sigma ◽  
Fine Tuning ◽  
Target Range ◽  
Product Characteristics ◽  
Process Mean ◽  
Content Type ◽  
Step Procedure ◽  
Process Product

Purpose The purpose of this paper is to develop an innovative and quite new Six Sigma quality control (SSQC) chart for the benefit of Six Sigma practitioners. A step-by-step procedure for the construction of the chart is also given. Design/methodology/approach Under the assumption of normality, in this paper, the construction of SSQC chart is proposed in which the population mean and standard deviation are drawn from the process specification from the perspective of Six Sigma quality (SSQ). In this chart, the concept of target range is used to restrict the shift in the process within plus or minus 1.5 times of standard deviation. This control chart is useful in monitoring the process to ensure that the process is well maintained within the specification limits with minimum variation (shift). Findings A step-by-step procedure is given for the construction of the proposed SSQC chart. It can be easily understood and its application is also simple for Six Sigma practitioners. The proposed chart suggests for timely improvements in process mean and variation. The illustrative example shows the improved performance of the proposed new procedure. Research limitations/implications The proposed approach assumes a normal population described by the known specification of the process/product characteristics though it may not be in all cases. This may call for a thorough study of the population before applying the chart. Practical implications The proposed SSQC chart is an innovative approach and is quite new for the practitioners. The paper assumes that the population standard deviation is known and is drawn from the specification of the process/product characteristics. The proposed chart helps in fine-tuning the process mean and bringing the process standard deviation to the satisfactory level from the perspective of SSQ. Originality/value The paper is the first of its kind. It is innovative and quite new to the Six Sigma practitioners who will find its application interesting.

Petroleum Exploration Risk in Prospect Portfolio Selection

2017 ◽  
pp. 40-65
Keyword(s):  
Standard Deviation ◽  
Portfolio Selection ◽  
Six Sigma ◽  
Efficient Frontier ◽  
Sharpe Ratio ◽  
Petroleum Exploration ◽  
Management Approach ◽  
Portfolio Optimisation ◽  
Exploration Risk ◽  
Return Variance

Presented method is applied to petroleum exploration for prospect portfolio selection to achieve investment objectives controlling risk. DMAIC framework applies stochastic techniques to risk management. Optimisation resolves Efficient Frontier of portfolios for desired range of expected return with initially defined increment. Simulation measures Efficient Frontier portfolios calculating mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target limits. Analysis considers mean return, Six Sigma metrics and Sharpe Ratio and selects the portfolio with maximal Sharpe Ratio as initially the best portfolio. Optimisation resolves Efficient Frontier in a narrow interval with smaller increments. Simulation measures Efficient Frontier performance including mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target. Analysis identifies the maximal Sharpe Ratio portfolio, i.e. the best portfolio for implementation. Selected prospects in the portfolio are individual projects. So, Project Management approach is used for control.

A control chart for the lognormal standard deviation

2017 ◽  
Vol 15 (1) ◽  
pp. 1-36 ◽  
Author(s):  
Wei-Heng Huang ◽  
Arthur B. Yeh ◽  
Hsiuying Wang
Keyword(s):  
Standard Deviation ◽  
Control Chart

scholarly journals Economic Design of EWMA Control Charts with Variable Sampling Intervals for Monitoring the Mean and Standard Deviation under Preventive Maintenance and Taguchi’s Loss Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Li Xue ◽  
Zhen He
Keyword(s):  
Standard Deviation ◽  
Control Chart ◽  
Economic Model ◽  
Preventive Maintenance ◽  
Control Charts ◽  
Loss Functions ◽  
Variable Sampling ◽  
Sampling Intervals ◽  
Optimal Values ◽  
The Mean

The control chart and the maintenance management need to control process quality and reduce out-of-control cost. They are two key tools in the production process; however, they have usually been analyzed separately in the literature. Moreover, the existing studies integrating these two concepts suffer from three significant drawbacks as follows: (1) using control charts with fixed parameters to monitor the process, so that the small and middle shifts are detected slowly; (2) monitoring the mean and standard deviation separately, whereas, in real condition, the mean and standard deviation should be monitored simultaneously; (3) quality loss function is not usually used to design economic model, which leads to a large social quality loss in the monitoring process of control chart. To eliminate these weaknesses, the economic design of the exponential weighted moving average (EWMA) control chart with variable sampling intervals (VSI) for monitoring the mean and standard deviation under preventive maintenance and Taguchi’s loss functions is proposed. The optimal values of the parameters are determined to minimize the loss-per-item in an average cycle process. In addition, a genetic algorithm is used in a numerical example to search for the optimal values of the parameters. According to the sensitivity analysis, the effect of the model parameters on the solution of the economic model is obtained. Finally, the comparison study shows that the VSI EWMA control charts designed by the joint economic model are expected to reduce loss.

scholarly journals SAMPLE STANDARD DEVIATION(s) CHART UNDER THE ASSUMPTION OF MODERATENESS AND ITS PERFORMANCE ANALYSIS

2017 ◽  
Vol 5 (6) ◽  
pp. 368-377
Author(s):  
Kalpesh S. Tailor
Keyword(s):  
Standard Deviation ◽  
Performance Analysis ◽  
Normal Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Curve Analysis ◽  
Mean Deviation ◽  
Sample Standard Deviation ◽  
Underlying Distribution ◽  
Control Limits

Moderate distribution proposed by Naik V.D and Desai J.M., is a sound alternative of normal distribution, which has mean and mean deviation as pivotal parameters and which has properties similar to normal distribution. Mean deviation (δ) is a very good alternative of standard deviation (σ) as mean deviation is considered to be the most intuitively and rationally defined measure of dispersion. This fact can be very useful in the field of quality control to construct the control limits of the control charts. On the basis of this fact Naik V.D. and Tailor K.S. have proposed 3δ control limits. In 3δ control limits, the upper and lower control limits are set at 3δ distance from the central line where δ is the mean deviation of sampling distribution of the statistic being used for constructing the control chart. In this paper assuming that the underlying distribution of the variable of interest follows moderate distribution proposed by Naik V.D and Desai J.M, 3δ control limits of sample standard deviation(s) chart are derived. Also the performance analysis of the control chart is carried out with the help of OC curve analysis and ARL curve analysis.

A lean six sigma approach for improving university campus office moves

2019 ◽  
Vol 10 (4) ◽  
pp. 928-947 ◽  
Author(s):  
Jennifer Wheeler-Webb ◽  
Sandra L. Furterer
Keyword(s):  
Standard Deviation ◽  
Six Sigma ◽  
Lean Six Sigma ◽  
Similar Process ◽  
University Campus ◽  
Content Type ◽  
Control Methodology ◽  
Six Sigma Approach ◽  
Improvement Effort

Purpose The purpose of this study was to improve the quoting, scheduling, invoicing and paying for campus office moves at a university. The Lean Six Sigma project goal was to improve the campus office move process by making it less complicated, free-up program managers’ time and pay the vendor on time. Design/methodology/approach The team used the Lean Six Sigma Define-Measure-Analyze-Improve-Control methodology to improve the process. Findings The average time from the campus move to when the invoice was paid improved by 27%, with an improved median of 16%. The standard deviation was greatly reduced by 51%. The average invoiced date to paid date remained statistically the same, and the median increased from 20 to 30 days, due to a policy change to move the target from 20 to 30 days. The standard deviation of the invoice to paid date was greatly reduced by 38%. This was a successful project because the sponsors were on-board from the beginning and included the process owners in the improvement effort. Originality/value Other higher education institutions or other industry areas with a similar process can implement this methodology and processes outlined in this case study to improve efficiency and cost effectiveness and as a guide for improving other processes within institutions.

Research and Development Risk in Projects Selection

2017 ◽  
pp. 66-91
Keyword(s):  
Standard Deviation ◽  
Six Sigma ◽  
Efficient Frontier ◽  
Sharpe Ratio ◽  
Management Approach ◽  
Portfolio Optimisation ◽  
Expected Return ◽  
Return Variance ◽  
Stochastic Techniques ◽  
Development Risk

Elaborated method is applied to R&D for project portfolio selection to achieve investment objectives controlling risk. DMAIC framework applies stochastic techniques to risk management. Optimisation resolves Efficient Frontier of portfolios for desired range of expected return with initially defined increment. Simulation measures Efficient Frontier portfolios calculating mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target limits. Analysis considers mean return, Six Sigma metrics and Sharpe Ratio and selects the portfolio with maximal Sharpe Ratio as initially the best portfolio. Optimisation resolves Efficient Frontier in a narrow interval with smaller increments. Simulation measures Efficient Frontier performance including mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target. Analysis identifies the maximal Sharpe Ratio portfolio, i.e. the best portfolio for implementation. Selected projects in the portfolio are individual projects. So, Project Management approach is used for control.

scholarly journals Nonparametric Double EWMA Control Chart for Process Monitoring

2016 ◽  
Vol 39 (2) ◽  
pp. 167 ◽  
Author(s):  
Muhammad Riaza ◽  
Saddam Akber Abbasib
Keyword(s):  
Standard Deviation ◽  
Control Chart ◽  
Average Run Length ◽  
Location Parameter ◽  
Quality Characteristic ◽  
Quadratic Loss ◽  
Run Length ◽  
Ewma Control Chart ◽  
Monitoring Process ◽  
Nonparametric Techniques

<p>In monitoring process parameters, we assume normality of the quality characteristic of interest, which is an ideal assumption. In many practical sit- uations, we may not know the distributional behavior of the data, and hence, the need arises use nonparametric techniques. In this study, a nonparametric double EWMA control chart, namely the NPDEWMA chart, is proposed to ensure efficient monitoring of the location parameter. The performance of the proposed chart is evaluated in terms of different run length properties, such as average, standard deviation and percentiles. The proposed scheme is compared with its recent existing counterparts, namely the nonparametric EWMA and the nonparametric CUSUM schemes. The performance mea- sures used are the average run length (ARL), standard deviation of the run length (SDRL) and extra quadratic loss (EQL). We observed that the pro- posed chart outperforms the said existing schemes to detect shifts in the process mean level. We also provide an illustrative example for practical considerations.</p>

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