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Article
Publication date: 1 October 1997

James D.T. Tannock

Control charts for statistical quality control have been the subject of academic study for many years. Various analytical approaches to economic control chart design have…

Abstract

Control charts for statistical quality control have been the subject of academic study for many years. Various analytical approaches to economic control chart design have been advanced, although none has found wide use in practice. Describes a simulation approach to the investigation of control chart economics. Simulation can provide guidance on chart design issues such as sample size, sampling interval and the use of alternative chart alarm rules. Applies the method to the economic comparison between variables control charting and other inspection strategies such as 100 per cent inspection. Presents some generalized results, allowing comparison to be made for various scenarios. Emphasizes the importance of process capability in the choice of quality control strategy and demonstrates the economic advantages of control charting where special or assignable causes exist.

Details

International Journal of Quality & Reliability Management, vol. 14 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 1 August 1994

Victor E. Sower, Jaideep G. Motwani and Michael J. Savoie

Proposes the use of a β, or difference‐from‐nominal, control chart forshort‐run industrial processes providing certain conditions are met.While some manufacturers have…

1156

Abstract

Proposes the use of a β, or difference‐from‐nominal, control chart for short‐run industrial processes providing certain conditions are met. While some manufacturers have used delta charts in the past, only recently have delta charts been discussed in the literature –and then under a variety of different names. This has led to a situation where β charts have been inappropriately applied because the conditions for their use were not clearly understood. Explains the β control chart and the conditions appropriate for its use. In addition, presents a case study of the use of the β chart in a short‐run manufacturing process.

Details

International Journal of Quality & Reliability Management, vol. 11 no. 6
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 30 January 2009

Shey‐Huei Sheu and Shin‐Li Lu

The purpose of this paper is to monitor small shifts in the process mean and/or variance for which observational data meet significant autocorrelation.

Abstract

Purpose

The purpose of this paper is to monitor small shifts in the process mean and/or variance for which observational data meet significant autocorrelation.

Design/methodology/approach

A generally weighted moving average (GWMA) control chart for monitoring a process is introduced in which the observations can be modelled as a first‐order autoregressive process with a random error. Using simulation, the average run lengths (ARLs) of control schemes are compared.

Findings

The results showed that the GWMA control chart of observations requires less time to detect small shifts in the process mean and/or variance than the EWMA control chart.

Originality/value

The paper presents a useful discussion of a method that enables the detecting ability of the EWMA control chart to be enhanced and shows that when the observations are drawn from an AR(1) process with random error, the EWMA control chart is far more useful than the Shewhart control chart in detecting small shifts. The GWMA control chart of observations is shown to be superior to the EWMA control chart in detecting small shifts in the process mean and variance. The GWMA control chart of observations requires less time to detect small process mean and/or variance shifts as the level of autocorrelation declines. However, the GWMA and EWMA control charts of observations perform poorly for large shifts.

Details

International Journal of Quality & Reliability Management, vol. 26 no. 2
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 12 August 2014

Yu-Ting Cheng and Chih-Ching Yang

Constructing a fuzzy control chart with interval-valued fuzzy data is an important topic in the fields of medical, sociological, economics, service and management. In…

Abstract

Purpose

Constructing a fuzzy control chart with interval-valued fuzzy data is an important topic in the fields of medical, sociological, economics, service and management. In particular, when the data illustrates uncertainty, inconsistency and is incomplete which is often the. case of real data. Traditionally, we use variable control chart to detect the process shift with real value. However, when the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional statistical process control (SPC) to monitor the fuzzy control chart. The purpose of this paper is to propose the designed standardized fuzzy control chart for interval-valued fuzzy data set.

Design/methodology/approach

The general statistical principles used on the standardized control chart are applied to fuzzy control chart for interval-valued fuzzy data.

Findings

When the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional SPC to monitor the fuzzy control chart. This study proposes the designed standardized fuzzy control chart for interval-valued fuzzy data set of vegetable price from January 2009 to September 2010 in Taiwan obtained from Council of Agriculture, Executive Yuan. Empirical studies are used to illustrate the application for designing standardized fuzzy control chart. More related practical phenomena can be explained by this appropriate definition of fuzzy control chart.

Originality/value

This paper uses a simpler approach to construct the standardized interval-valued chart for fuzzy data based on traditional standardized control chart which is easy and straightforward. Moreover, the control limit of the designed standardized fuzzy control chart is an interval with (LCL, UCL), which consists of the conventional range of classical standardized control chart.

Details

Management Decision, vol. 52 no. 7
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 1 October 1995

Zhang Wu

Studies the necessity of controlling the variation of the skewnessof the process distribution in order to reduce the product scrap.Proposes a γ control chart for detecting…

559

Abstract

Studies the necessity of controlling the variation of the skewness of the process distribution in order to reduce the product scrap. Proposes a γ control chart for detecting the skewness shift, also implements a simulation procedure to decide the control limits of the γ chart.

Details

International Journal of Quality & Reliability Management, vol. 12 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 25 September 2009

P. Castagliola, G. Celano and S. Fichera

The purpose of this paper is to introduce and investigate the performances of a new CUSUM‐S2 control chart designed to monitor the sample variance of samples from a…

Abstract

Purpose

The purpose of this paper is to introduce and investigate the performances of a new CUSUM‐S2 control chart designed to monitor the sample variance of samples from a normally distributed population.

Design/methodology/approach

The proposed chart monitors a statistic computed as a logarithmic transformation of the sample variance; the introduction of the sample variance logarithmic transformation has a twofold effect: to quickly detect the occurrence of an “out‐of‐control” condition; to deal with a quasi‐standard normal statistic.

Findings

A design strategy trying to minimize the “out‐of‐control” average run length (ARL) of the chart is presented and the statistical performance of the CUSUM‐S2 chart has been assessed through a comparison with an EWMA‐S2 control chart proposed in the literature to monitor the process dispersion.

Research limitations/implications

The paper only deals with uncorrelated normally distributed data.

Practical implications

The obtained results show how the CUSUM‐S2 chart is particularly suitable when reduction in the process dispersion should be detected by means of subgroups having limited sample sizes.

Originality/value

The paper shows the new CUSUM‐S2 control chart allows a decreasing of the variability to be detected faster than the corresponding EWMA‐S2 control chart proposed earlier in the literature.

Details

Journal of Quality in Maintenance Engineering, vol. 15 no. 4
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 18 December 2006

Fong‐Jung Yu, Hsiang Chin and Hsiao Wei Huang

Control charts are important tools of statistical quality control. In 1956, Duncan first proposed the economic design of xcontrol charts to control normal process means…

Abstract

Control charts are important tools of statistical quality control. In 1956, Duncan first proposed the economic design of xcontrol charts to control normal process means and insure that the economic design control chart actually has a lower cost, compared with a Shewhart control chart. An moving average (MA) control chart is more effective than a Shewhart control chart in detecting small process shifts and is considered by some to be simpler to implement than the CUSUM. An economic design of MA control chart has also been proposed in 2005. The weaknesses to only the economic design are poor statistics because it does not consider type I or type II errors and average time to signal when selecting design parameters for control chart. This paper provides a construction of an economic‐statistical model to determine the optimal parameters of an MA control chart to improve economic design. A numerical example is employed to demonstrate the model’s working and its sensitivity analysis is also provided.

Details

Asian Journal on Quality, vol. 7 no. 3
Type: Research Article
ISSN: 1598-2688

Keywords

Article
Publication date: 1 June 2001

Yan Xu

Traditional statistical tools are subject to certain constraints when they are applied to quality control in industries where the number of faults per working day is…

1649

Abstract

Traditional statistical tools are subject to certain constraints when they are applied to quality control in industries where the number of faults per working day is limited. An effective quality monitoring and analyzing tool is therefore needed to meet the specific requirements of these industrial sectors. Proposes a so‐called “Cause‐classified Control Chart”, based on fieldwork in the Nanchang Telecommunications Office of China. Trial results from several posts and telecommunications offices in China in recent years have positively shown that the Cause‐Classified Control Chart is an effective tool for quality enhancement in these specific industrial sectors.

Details

Managerial Auditing Journal, vol. 16 no. 4
Type: Research Article
ISSN: 0268-6902

Keywords

Article
Publication date: 22 February 2021

Carmen Patino-Rodriguez, Diana M. Pérez and Olga Usuga Manco

The purpose of this paper is to evaluate the performance of a modified EWMA control chart (γEWMA control chart), which considers data distribution and incorporate its…

Abstract

Purpose

The purpose of this paper is to evaluate the performance of a modified EWMA control chart (γEWMA control chart), which considers data distribution and incorporate its correlation structure, simulating in-control and out-of-control processes and to select an adequate value for smoothing parameter with these conditions.

Design/methodology/approach

This paper is based on a simulation approach using the methodology for evaluating statistical methods proposed by Morris et al. (2019). Data were generated from a simulation considering two factors that associated with data: (1) quality variable distribution skewness as an indicator of quality variable distribution; (2) the autocorrelation structure for type of relationship between the observations and modeled by AR(1). In addition, one factor associated with the process was considered, (1) the shift in the process mean. In the following step, when the chart control is modeled, the fourth factor intervenes. This factor is a smoothing parameter. Finally, three indicators defined from the Run Length are used to evaluate γEWMA control chart performance this factors and their interactions.

Findings

Interaction analysis for four factor evidence that the modeling and selection of parameters is different for out-of-control and in-control processes therefore the considerations and parameters selected for each case must be carefully analyzed. For out-of-control processes, it is better to preserve the original features of the distribution (mean and variance) for the calculation of the control limits. It makes sense that highly autocorrelated observations require smaller smoothing parameter since the correlation structure enables the preservation of relevant information in past data.

Originality/value

The γEWMA control chart there has advantages because it gathers, in single chart control: the process and modelling characteristics, and data structure process. Although there are other proposals for modified EWMA, none of them simultaneously analyze the four factors nor their interactions. The proposed γEWMA allows setting the appropriate smoothing parameter when these three factors are considered.

Details

International Journal of Quality & Reliability Management, vol. 38 no. 9
Type: Research Article
ISSN: 0265-671X

Keywords

Open Access
Article
Publication date: 17 August 2021

Abeer A. Zaki, Nesma A. Saleh and Mahmoud A. Mahmoud

This study aims to assess the effect of updating the Phase I data – to enhance the parameters' estimates – on the control charts' detection power designed to monitor…

Abstract

Purpose

This study aims to assess the effect of updating the Phase I data – to enhance the parameters' estimates – on the control charts' detection power designed to monitor social networks.

Design/methodology/approach

A dynamic version of the degree corrected stochastic block model (DCSBM) is used to model the network. Both the Shewhart and exponentially weighted moving average (EWMA) control charts are used to monitor the model parameters. A performance comparison is conducted for each chart when designed using both fixed and moving windows of networks.

Findings

Our results show that continuously updating the parameters' estimates during the monitoring phase delays the Shewhart chart's detection of networks' anomalies; as compared to the fixed window approach. While the EWMA chart performance is either indifferent or worse, based on the updating technique, as compared to the fixed window approach. Generally, the EWMA chart performs uniformly better than the Shewhart chart for all shift sizes. We recommend the use of the EWMA chart when monitoring networks modeled with the DCSBM, with sufficiently small to moderate fixed window size to estimate the unknown model parameters.

Originality/value

This study shows that the excessive recommendations in literature regarding the continuous updating of Phase I data during the monitoring phase to enhance the control chart performance cannot generally be extended to social network monitoring; especially when using the DCSBM. That is to say, the effect of continuously updating the parameters' estimates highly depends on the nature of the process being monitored.

Details

Review of Economics and Political Science, vol. 6 no. 4
Type: Research Article
ISSN: 2356-9980

Keywords

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