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Article
Publication date: 29 April 2014

S.T.A. Niaki and Majid Khedmati

The purpose of this paper is to propose two control charts to monitor multi-attribute processes and then a maximum likelihood estimator for the change point of the…

Abstract

Purpose

The purpose of this paper is to propose two control charts to monitor multi-attribute processes and then a maximum likelihood estimator for the change point of the parameter vector (process fraction non-conforming) of multivariate binomial processes.

Design/methodology/approach

The performance of the proposed estimator is evaluated for both control charts using some simulation experiments. At the end, the applicability of the proposed method is illustrated using a real case.

Findings

The proposed estimator provides accurate and useful estimation of the change point for almost all of the shift magnitudes, regardless of the process dimension. Moreover, based on the results obtained the estimator is robust with regard to different correlation values.

Originality/value

To the best of authors’ knowledge, there are no work available in the literature to estimate the change-point of multivariate binomial processes.

Article
Publication date: 23 January 2019

Barry Cobb and Linda Li

Bayesian networks (BNs) are implemented for monitoring a process via statistical process control (SPC) where attribute data are available on output from the system. The…

Abstract

Purpose

Bayesian networks (BNs) are implemented for monitoring a process via statistical process control (SPC) where attribute data are available on output from the system. The paper aims to discuss this issue.

Design/methodology/approach

The BN provides a graphical and numerical tool to help a manager understand the effect of sample observations on the probability that the process is out-of-control and requires investigation. The parameters for the BN SPC model are statistically designed to minimize the out-of-control average run length (ARL) of the process at a specified in-control ARL and sample size.

Findings

The BN model outperforms adaptive np control charts in all experiments, except for some cases where only a large change in the proportion of sample defects is relevant. The BN is particularly useful when small sample sizes are available and when managers need to detect small changes in the proportion of defects produced by the process.

Research limitations/implications

The BN model is statistically designed and parameters are chosen to minimize out-of-control ARL. Future advancements will address the economic design of BNs for SPC with attribute data.

Originality/value

The BNs allow qualitative knowledge to be combined with sample data, and the average percentage of defects can be modeled as a continuous random variable. The framework of the BN easily permits classification of the system operation into two or more states, so diagnostic analysis can be performed simultaneously with statistical inference.

Details

International Journal of Quality & Reliability Management, vol. 36 no. 2
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 29 July 2014

Soroush Avakh Darestani, Azam Moradi Tadi, Somayeh Taheri and Maryam Raeiszadeh

Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper…

Abstract

Purpose

Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to investigate the attributes of fuzzy U control chart.

Design/methodology/approach

If the data were uncertain, they were converted into trapezoidal fuzzy number and the fuzzy upper and lower control limits were trapezoidal fuzzy number calculated using fuzzy mode approach. The result was grouped into four categories (in control, out of control, rather in control, rather out of control). Finally, a case study was presented and the method coding was done in MATLAB software using design U control chart; then, the results were verified.

Findings

The definition of fuzzy numbers for each type of defect sensitivity and the unit can be classified into four groups: in-control and out-of-control, rather in-control and rather out-of-control which represent the actual quality of the products. It can be concluded that fuzzy control chart is more sensitive on recognition out of control patterns.

Originality/value

This paper studies the use of control charts, specifically the attributes of a fuzzy U control chart, for monitoring defects in the format of a case study.

Details

International Journal of Quality & Reliability Management, vol. 31 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 1 March 1999

M. Xie, X.S. Lu, T.N. Goh and L.Y. Chan

Traditional attribute control charts are based on the monitoring of the number of nonconforming items in a sample of fixed size. In modern manufacturing processes, since…

1089

Abstract

Traditional attribute control charts are based on the monitoring of the number of nonconforming items in a sample of fixed size. In modern manufacturing processes, since items can be checked automatically, use of samples of a fixed subjective size with traditional charts is not suitable for on‐line continuous inspection. Furthermore, the sample size usually has to be large when the process fraction nonconforming is not reasonably high. If a decision is to be made only when a sufficient number of items are manufactured and inspected, many nonconforming items might have been produced. In this paper, a control scheme is presented based on the monitoring of cumulative counts of items inspected. This procedure will limit the number of consecutive nonconforming items to a small value when the process has suddenly deteriorated, can detect a sudden process shift quickly and is suitable for continuous inspection processes. The exact probability control limits are derived and the implementation procedure is described.

Details

International Journal of Quality & Reliability Management, vol. 16 no. 2
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 3 July 2020

Siim Koppel and Shing Chang

Modern production facilities produce large amounts of data. The computational framework often referred to as big data analytics has greatly improved the capabilities of…

Abstract

Purpose

Modern production facilities produce large amounts of data. The computational framework often referred to as big data analytics has greatly improved the capabilities of analyses of large data sets. Many manufacturing companies can now seize this opportunity to leverage their data to gain competitive advantages for continuous improvement. Six Sigma has been among the most popular approaches for continuous improvement. The data-driven nature of Six Sigma applied in a big data environment can provide competitive advantages. In the traditional Six Sigma implementation – define, measure, analyze, improve and control (DMAIC) problem-solving strategy where a human team defines a project ahead of data collection. This paper aims to propose a new Six Sigma approach that uses massive data generated to identify opportunities for continuous improvement projects in a manufacturing environment in addition to human input in a measure, define, analyze, improve and control (MDAIC) format.

Design/methodology/approach

The proposed Six Sigma strategy called MDAIC starts with data collection and process monitoring in a manufacturing environment using system-wide monitoring that standardizes continuous, attribute and profile data into comparable metrics in terms of “traffic lights.” The classifications into green, yellow and red lights are based on pre-control charts depending on how far a measurement is from its target. The proposed method monitors both process parameters and product quality data throughout a hierarchical production system over time. An attribute control chart is used to monitor system performances. As the proposed method is capable of identifying changed variables with both spatial and temporal spaces, Six Sigma teams can easily pinpoint the areas in need to initiate Six Sigma projects.

Findings

Based on a simulation study, the proposed method is capable of identifying variables that exhibit the biggest deviations from the target in the Measure step of a Six Sigma project. This provides suggestions of the candidates for the improvement section of the proposed MDAIC methodology.

Originality/value

This paper proposes a new approach for the identifications of projects for continuous improvement in a manufacturing environment. The proposed framework aims to monitor the entire production system that integrates all types of production variables and the product quality characteristics.

Details

International Journal of Lean Six Sigma, vol. 12 no. 2
Type: Research Article
ISSN: 2040-4166

Keywords

Article
Publication date: 26 April 2018

Somayeh Fadaei and Alireza Pooya

The purpose of this paper is to apply fuzzy spectrum in order to collect the vague and imprecise data and to employ the fuzzy U control chart in variable sample size using…

Abstract

Purpose

The purpose of this paper is to apply fuzzy spectrum in order to collect the vague and imprecise data and to employ the fuzzy U control chart in variable sample size using fuzzy rules. This approach is improved and developed by providing some new rules.

Design/methodology/approach

The fuzzy operating characteristic (FOC) curve is applied to investigate the performance of the fuzzy U control chart. The application of FOC presents fuzzy bounds of operating characteristic (OC) curve whose width depends on the ambiguity parameter in control charts.

Findings

To illustrate the efficiency of the proposed approach, a practical example is provided. Comparing performances of control charts indicates that OC curve of the crisp chart has been located between the FOC bounds, near the upper bound; as a result, for the crisp control chart, the probability of the type II error is of significant level. Also, a comparison of the crisp OC curve with OCavg curve and FOCα curve approved that the probability of the type II error for the crisp chart is more than the same amount for the fuzzy chart. Finally, the efficiency of the fuzzy chart is more than the crisp chart, and also it timely gives essential alerts by means of linguistic terms. Consequently, it is more capable of detecting process shifts.

Originality/value

This research develops the fuzzy U control chart with variable sample size whose output is fuzzy. After creating control charts, performance evaluation in the industry is important. The main contribution of this paper is to employs the FOC curve for evaluating the performance of the fuzzy control chart, while in prior studies in this area, the performance of fuzzy control chart has not been evaluated.

Details

The TQM Journal, vol. 30 no. 3
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 1 March 1996

Jim Freeman and Nikolaos Evangeliou

Demonstrates the training value of a new computer‐aided learning package, SQCC_ATT. Control charts are used extensively in quality management, and computer simulation…

2094

Abstract

Demonstrates the training value of a new computer‐aided learning package, SQCC_ATT. Control charts are used extensively in quality management, and computer simulation provides a valuable facility for generating charts under a variety of different working assumptions. Warns about the potential for misuse of control charts by untrained and inexperienced staff. Concludes that specialist simulation games are likely to play an increasingly important role in training in management control techniques.

Details

Training for Quality, vol. 4 no. 1
Type: Research Article
ISSN: 0968-4875

Keywords

Article
Publication date: 19 April 2013

Sukhraj Singh and D.R. Prajapati

The purpose of this paper is to study the effect of correlation on the performance of CUSUM and EWMA charts. The performance of the CUSUM and EWMA charts is measured in…

Abstract

Purpose

The purpose of this paper is to study the effect of correlation on the performance of CUSUM and EWMA charts. The performance of the CUSUM and EWMA charts is measured in terms of average run lengths (ARLs) for the positively correlated data. The ARLs at various set of parameters of the CUSUM and EWMA charts are computed, using MATLAB. The behavior of the CUSUM and EWMA chart at the various shifts in the process mean is studied, analyzed and compared at different levels of correlation (Φ). The optimum schemes for both the charts are suggested for various levels of correlation (Φ).

Design/methodology/approach

Positively correlated observations having normal distribution are generated with the help of the MATLAB. Performance of both the charts in terms of ARLs is measured and compared at various levels of correlation (Φ). The optimal schemes of charts which give the desired in‐control ARLs are suggested for various levels of correlation (Φ).

Findings

For each level of correlation (Φ) various schemes of both the charts are suggested. Moreover those suggested schemes which give quick response to the shifts in the process mean is termed as optimal scheme. It is concluded that CUSUM schemes are preferred as compared to the EWMA schemes for quicker response. The optimal schemes of CUSUM and EWMA chart are also compared with the EWMAST chart suggested by Winkel and Zhang (2004).

Research limitations/implications

Both the schemes are optimized by assuming the autocorrelated numbers to be normally distributed. But this assumption may also be relaxed to design these schemes for autocorrelated data. Moreover sample size of four is taken while developing these schemes; various other schemes can also be developed for different sample sizes. Control charts for attribute type of data can also be developed for different level of correlation (Φ).

Practical implications

For a specific control chart, if the in‐control ARL of the process outputs of any industry is in accordance with the simulated in‐control ARL. It means the process outputs must have same level of correlation (Φ) corresponding to the simulated in‐control ARL and the suggested optimal schemes, corresponding to that level of correlation (Φ), must be adopted to avoid the false alarm rate. The correlation among the process outputs of any industry can be find out and corresponding to that level of correlation the suggested control chart parameters can be applied. Thus false alarms generated, will be minimum for the suggested schemes at different level of correlation (Φ).

Social implications

If the optimal CUSUM schemes are employed in process/service industry, there will be a considerable amount of saving in time and money expended in search of causes behind frequent false alarms. The rejection level of products in the industries can be reduced by designing the better control chart schemes which will also reduce the loss to the society, as suggested by Taguchi.

Originality/value

The research findings could be applied to various manufacturing industries as well as service industries where the data is positively correlated and normally distributed.

Article
Publication date: 6 June 2016

S. Mohammad Hashemian, Rassoul Noorossana, Ali Keyvandarian and Maryam Shekary A.

The purpose of this paper is to compare the performances of np-VP control chart with estimated parameter to the np-VP control chart with known parameter using average…

Abstract

Purpose

The purpose of this paper is to compare the performances of np-VP control chart with estimated parameter to the np-VP control chart with known parameter using average time-to-signal (ATS), standard deviation of the time-to-signal (SDTS), and average number of observations to signal (ANOS) as performance measures.

Design/methodology/approach

The approach used in this study is probabilistic in which the expected values of performance measures are calculated using probabilities of different estimators used to estimate process parameter.

Findings

Numerical results indicate different performances for the np-VP control chart in known and estimated parameter cases. It is obvious that when process parameter is not known and is estimated using Phase I data, the chart does not perform as user expects. To tackle this issue, optimal Phase I estimation scenarios are recommended to obtain the best performance from the chart in the parameter estimation case in terms of performance measures.

Practical implications

This research adds to the body of knowledge in quality control of process monitoring systems. This paper may be of particular interest to practitioners of quality systems in factories where products are monitored to reduce the number of defectives and np chart parameter needs to be estimated.

Originality/value

The originality of this paper lies within the context in which an adaptive np control chart is studied and the process parameter unlike previous studies is assumed unknown. Although other types of control charts have been studied when process parameter is unknown but this is the first time that adaptive np chart performance with estimated process parameter is studied.

Details

International Journal of Quality & Reliability Management, vol. 33 no. 6
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…

1162

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

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