Statistical Process Control using Shewhart Control Charts with Supplementary Runs Rules

Koutras, Markos V.; Bersimis, Sotiris; Maravelakis, Petros E.

doi:10.1007/s11009-007-9016-8

Cited by 135 publications

(87 citation statements)

References 52 publications

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“…It may also be noted that for those cases where theoretical (or otherwise) results for the power of a control chart are available, the developed function "power" has given almost the same results which ensures the validity of our function for the said computational purposes (for example see and compare the results ofX , R, S and S 2 charts given by Alwan (2000), Klein (2000), Khoo (2004), Koutras et al (2007), Antzoulakos and Rakitzis (2008), Riaz (2008) and Montgomery (2009).…”

Section: Summary For Out-of-control Probabilitysupporting

confidence: 53%

“…Many authors have proposed runs rules schemes for this purpose; see for example Alwan (2000), Klein (2000), Khoo (2004), Koutras et al (2007), Antzoulakos and Rakitzis (2008) and the references therein. Implementation of such schemes increases the sensitivity of the control chart for smaller shifts, but at the same time complicates their design structure.…”

Section: Introductionmentioning

confidence: 99%

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Efficient power computation for r out of m runs rules schemes

2012

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In this article we develop a power computation code in the R language which provides an easy to use tool to researchers in designing Shewhart control charts. It enables researchers to use different existing and newly introduced sensitizing rules and runs rules schemes designed for Shewhart-type control charts for location and spread. The code provides researchers to compute the power for different options of r out of m rules/schemes. The code is flexible to apply for any sample size, false alarm rate, type of control limits (one-or two-sided), amount of shift in the process parameters and a variety of popular distributions for commonly used Shewhart-type control charts (i.e.X , R, S and S 2 charts). These mentioned benefits of the developed functional code are only partially found in features of the existing software packages and these programs may be enhanced by adding the features of the developed code as a function in their libraries dealing with quality control charting.

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Section: Summary For Out-of-control Probabilitysupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Efficient power computation for r out of m runs rules schemes

2012

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“…Woodall (2000) disputes the effectiveness of the traditionally used runs rules and suggests alternatives, as well as suggesting alternatives to using the mR chart to identify changes in variability in the process. Koutras et al (2007) conclude that the sensitivity improvement achieved by supplementing the classical control chart by runs rules, has a trade off in the false alarm rate. In simple words, runs rules increase sensitivity but also produce more false alarms.…”

Section: The Process Behavior Chartmentioning

confidence: 83%

Management of Data for Health Performance Measurement in the Industrialized Dairy Herd

Josephine¹

2012

American Journal of Animal and Veterinary Sciences

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One part of dairy herd management is to handle disease occurrence by means of health promotion, disease prevention, timely medical treatments, or eradication of disease. Supporting this part of herd management is an essential task for the cattle veterinarian. The study objective was to identify principles and tools for analysis of herd health data in industrialized dairy herds. The analysis takes into account the additional complexity caused by changes in behavior among herd managers and herd personnel due to, for instance, legislative changes to promote animal welfare or food safety. Methods from herd management science were combined with contextspecific information about social mechanisms. The results were synthesized into a concrete 7-step plan of action, as follows: (1) As the foundation, use continuously process behavior charts primarily based on animallevel data. (2) Assure strict definition of the measurements considering purpose, collector and meaning in terms of biology and management. (3) Interpret the patterns in the process behavior charts and search for and remove causes of exceptional variation in a dialogue with the herd manager. (4) Search for options to reduce routine variation. Multivariable or multivariate statistical models can give additional information because of their ability to reveal hidden sources of variation. (5) Set targets at the tactical and strategic levels while accounting for costs and benefits with appropriate methods suggested in the study. Issues related to nonfinancial effects are addressed. (6) Adjust measurement and intervention theory. The previous five steps should initiate an iterative process in which the intervention is evaluated and updated based on the results achieved thus far. (7) Develop a framework in the veterinary practice unit to support the health performance measurement process. The activities in step 7 will almost certainly require expert statistical assistance.

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“…Khoo and Ariffin [8] proposed an improved runs-rules scheme which is a combination of the classical 1-of-1 runs-rule and the wof-(w+v) runs-rules. Koutras et al [9] reviewed the well-known Shewhart-type charts supplemented with additional rules based on the theory of runs and scans. A class of Phase II nonparametric charts based on precedence statistics with runs-rules was proposed by Chakraborti et al [10].…”

Section: Introductionmentioning

confidence: 99%

Distribution-free Phase II Mann–Whitney control charts with runs-rules

Malela‐Majika

Chakraborti

Graham

2015

Int J Adv Manuf Technol

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The addition of runs-rules has been recommended to improve the performance of classical, normal theory Shewhart-type control charts, for detecting small to moderate size shifts. In this paper, we consider adding both standard and improved runs-rules to enhance the performance of the distribution-free Phase II Shewhart-type chart based on the well-known Mann-Whitney statistic proposed by Chakraborti and Van de Wiel [1]. Standard runs-rules are typically of the form w-of-(w+v) with w > 1 and v 0 and the improved runs-rules scheme is a combination of the classical 1-of-1 runs-rule and the w-of-(w+v) runs-rules. The improved scheme improves the performance of the charts in detecting larger shifts while maintaining its performance in detecting small to moderate shifts. The in-control and out-of-control performance of the proposed runs-rules enhanced distribution-free charts are examined through extensive simulations. It is seen that the proposed charts have attractive performance compared to some competing charts, and are better in many cases. An illustrative example is provided, along with a summary and conclusions.

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Statistical Process Control using Shewhart Control Charts with Supplementary Runs Rules

Cited by 135 publications

References 52 publications

Efficient power computation for r out of m runs rules schemes

Efficient power computation for r out of m runs rules schemes

Management of Data for Health Performance Measurement in the Industrialized Dairy Herd

Distribution-free Phase II Mann–Whitney control charts with runs-rules

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