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Article
Publication date: 21 September 2012

Jeh‐Nan Pan and Sheau‐Chiann Chen

The purpose of this paper is to explore the relationship between multivariate process capability indices and loss functions for both nominal‐the‐best and smaller‐the‐better cases…

Abstract

Purpose

The purpose of this paper is to explore the relationship between multivariate process capability indices and loss functions for both nominal‐the‐best and smaller‐the‐better cases, so the likelihood and consequences resulting from the nonconforming of a manufacturing process or an environmental system can be evaluated simultaneously.

Design/methodology/approach

In this paper, the authors present a new approach of correlated risk assessment by linking the multiple process capability indices and loss functions, in which the multivariate process capability indices and multivariate loss functions describe the likelihood and consequences as a result of nonconformities in multivariate manufacturing or environmental system, respectively. Then, the associated relationship equations are developed using multivariate methods. Moreover, a step‐by‐step procedure is provided to facilitate the implementation of the correlated risk assessment.

Findings

Given the multivariate process capability indices, the authors show that the expected loss can be estimated by developed relationship equations and two numerical examples are also given, to demonstrate how the correlated manufacturing and environmental risks can be properly assessed by linking the multivariate process capability indices and multivariate loss function.

Practical implications

The risk information of likelihood and expected loss, classified in the four planning zones of a strategic planning matrix, provides practising managers and engineers with a decision‐making tool for prioritizing their quality improvement projects when conducting risk assessment for any multivariate process or environmental system.

Originality/value

Once the existing quality/environmental problems and their Key Performance Indicators are identified, one may conduct risk assessment by applying the relationship equations to evaluate the impact of correlated risk on manufacturing processes or multiple environmental emissions inside company; this can lead to the direction of continuous improvement for any industry.

Article
Publication date: 31 December 2015

Jeh-Nan Pan, Chung-I Li and Wei-Chen Shih

In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption…

Abstract

Purpose

In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions.

Design/methodology/approach

In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMC p , RNMC pm and RNMC pu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMC p and NMC pm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case.

Findings

A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices.

Practical implications

Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system.

Originality/value

Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions.

Details

International Journal of Quality & Reliability Management, vol. 33 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 14 December 2021

Arijit Maji and Indrajit Mukherjee

The purpose of this study is to propose an effective unsupervised one-class-classifier (OCC) support vector machine (SVM)-based single multivariate control chart (OCC-SVM) to…

Abstract

Purpose

The purpose of this study is to propose an effective unsupervised one-class-classifier (OCC) support vector machine (SVM)-based single multivariate control chart (OCC-SVM) to simultaneously monitor “location” and “scale” shifts of a manufacturing process.

Design/methodology/approach

The step-by-step approach to developing, implementing and fine-tuning the intrinsic parameters of the OCC-SVM chart is demonstrated based on simulation and two real-life case examples.

Findings

A comparative study, considering varied known and unknown response distributions, indicates that the OCC-SVM is highly effective in detecting process shifts of samples with individual observations. OCC-SVM chart also shows promising results for samples with a rational subgroup of observations. In addition, the results also indicate that the performance of OCC-SVM is unaffected by the small reference sample size.

Research limitations/implications

The sample responses are considered identically distributed with no significant multivariate autocorrelation between sample observations.

Practical implications

The proposed easy-to-implement chart shows satisfactory performance to detect an out-of-control signal with known or unknown response distributions.

Originality/value

Various multivariate (e.g. parametric or nonparametric) control chart(s) are recommended to monitor the mean (e.g. location) and variance (e.g. scale) of multiple correlated responses in a manufacturing process. However, real-life implementation of a parametric control chart may be complex due to its restrictive response distribution assumptions. There is no evidence of work in the open literature that demonstrates the suitability of an unsupervised OCC-SVM chart to simultaneously monitor “location” and “scale” shifts of multivariate responses. Thus, a new efficient OCC-SVM single chart approach is proposed to address this gap to monitor a multivariate manufacturing process with unknown response distributions.

Details

International Journal of Quality & Reliability Management, vol. 40 no. 2
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 26 July 2021

Imane Mjimer, ES-Saadia Aoula and EL Hassan Achouyab

This study aims to monitor the overall equipment effectiveness (OEE) indicator that is one of the best indicators used to monitor the performance of the company by the multivariate

Abstract

Purpose

This study aims to monitor the overall equipment effectiveness (OEE) indicator that is one of the best indicators used to monitor the performance of the company by the multivariate control chart.

Design/methodology/approach

To improve continually the performance of a company, many research studies tend to apply Lean six sigma approach. It is one of the best ways used to reduce the variability in the process by using the univariate control chart to know the trend of the variable and make the action before process deviation. Nevertheless, and when the need is to monitor two or more correlated characteristics simultaneously, the univariate control chart will be unable to do it, and the multivariate control chart will be the best way to successfully monitor the correlated characteristics.

Findings

For this study, the authors have applied the multivariate control chart to control the OEE performance rate which is composed by the quality rate, performance rate and availability rate, and the relative work from which the authors have adopted the same methodology (Hadian and Rahimifard, 2019) was done for project monitoring, which is done by following different indicators such as cost, and time; the results of this work shows that by applying this tool, all project staff can meet the project timing with the cost already defined at the beginning of the project. The idea of monitoring the OEE rate comes because the OEE contains the three correlated indicators, we can’t do the monitoring of the OEE just by following one of the three because data change and if today we have the performance and quality rate are stable, and the availability is not, tomorrow we can another indicator impacted and, in this case, the univariate control chart can’t response to our demand. That’s why we have choose the multivariate control chart to prevent the trend of OEE performance rate. Otherwise, and according to production planning work, they try to prevent the downtime by switching to other references, but after applying the OEE monitoring using the multivariate control chart, the company can do the monitoring of his ability to deliver the good product at time to meet customer demand.

Research limitations/implications

The application was done per day, it will be good to apply it per shift in order to have the ability to take the fast reaction in case of process deviation. The other perspective point we can have is to supervise the process according to the control limits found and see if the process still under control after the implementation of the Multivariate control chart at the OEE Rate and if we still be able to meet customer demand in terms of Quantity and Quality of the product by preventing the process deviation using multivariate control chart.

Practical implications

The implication of this work is to provide to the managers the trend of the performance of the workshop by measuring the OEE rate and by following if the process still under control limits, if not the reaction plan shall be established before the process become out of control.

Originality/value

The OEE indicator is one of the effective indicators used to monitor the ability of the company to produce good final product, and the monitoring of this indicator will give the company a visibility of the trend of performance. For this reason, the authors have applied the multivariate control chart to supervise the company performance. This indicator is composed by three different rates: quality, performance and availability rate, and because this tree rates are correlated, the authors have tried to search the best tool which will give them the possibility to monitor the OEE rate. After literature review, the authors found that many works have used the multivariate control chart, especially in the field of project: to monitor the time and cost simultaneously. After that, the authors have applied the same approach to monitor the OEE rate which has the same objective : to monitor the quality, performance and availability rate in the same time.

Details

International Journal of Lean Six Sigma, vol. 13 no. 4
Type: Research Article
ISSN: 2040-4166

Keywords

Article
Publication date: 31 December 2015

Jeffrey E. Jarrett

The purpose of this paper is to suggest better methods for monitoring the diagnostic and treatment services for providers of public health and the management of public health…

2032

Abstract

Purpose

The purpose of this paper is to suggest better methods for monitoring the diagnostic and treatment services for providers of public health and the management of public health services. In particular, the authors examine the construction and use of industrial quality control methods as applied to the public providers, in both the prevention and cure for infectious diseases and the quality of public health care providers in such applications including water quality standards, sewage many others. The authors suggest implementing modern multivariate applications of quality control techniques and/or better methods for univariate quality control common in industrial applications in the public health sector to both control and continuously improve public health services. These methods entitled total quality management (TQM) form the foundation to improve these public services.

Design/methodology/approach

The study is designed to indicate the great need for TQM analysis to utilize methods of statistical quality control. All this is done to improve public health services through implementation of quality control and improvement methods as part of the TQM program. Examples of its use indicate that multivariate methods may be the best but other methods are suggested as well.

Findings

Multivariate methods provide the best solutions when quality and reliability tests show indications that the variables observed are inter-correlated and correlated over time. Simpler methods are available when the above factors are not present.

Research limitations/implications

Multivariate methods will provide for better interpretation of results, better decisions and smaller risks of both Type I and Type II errors. Smaller risks lead to better decision making and may reduce costs.

Practical implications

Analysts will improve such things as the control of water quality and all aspects of public health when data are collected through experimentation and/or periodic quality management techniques.

Social implications

Public health will be better monitored and the quality of life will improve for all especially in places where public development is undertaking rapid changes.

Originality/value

The manuscript is original because it uses well known and scientific methods of analyzing data in area where data collection is utilized to improve public health.

Details

International Journal of Quality & Reliability Management, vol. 33 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 4 September 2017

Sagar Sikder, Subhash Chandra Panja and Indrajit Mukherjee

The purpose of this paper is to develop a new easy-to-implement distribution-free integrated multivariate statistical process control (MSPC) approach with an ability to recognize…

Abstract

Purpose

The purpose of this paper is to develop a new easy-to-implement distribution-free integrated multivariate statistical process control (MSPC) approach with an ability to recognize out-of-control points, identify the key influential variable for the out-of-control state, and determine necessary changes to achieve the state of statistical control.

Design/methodology/approach

The proposed approach integrates the control chart technique, the Mahalanobis-Taguchi System concept, the Andrews function plot, and nonlinear optimization for multivariate process control. Mahalanobis distance, Taguchi’s orthogonal array, and the main effect plot concept are used to identify the key influential variable responsible for the out-of-control situation. The Andrews function plot and nonlinear optimization help to identify direction and necessary correction to regain the state of statistical control. Finally, two different real life case studies illustrate the suitability of the approach.

Findings

The case studies illustrate the potential of the proposed integrated multivariate process control approach for easy implementation in varied manufacturing and process industries. In addition, the case studies also reveal that the multivariate out-of-control state is primarily contributed by a single influential variable.

Research limitations/implications

The approach is limited to the situation in which a single influential variable contributes to out-of-control situation. The number and type of cases used are also limited and thus generalization may not be debated. Further research is necessary with varied case situations to refine the approach and prove its extensive applicability.

Practical implications

The proposed approach does not require multivariate normality assumption and thus provides greater flexibility for the industry practitioners. The approach is also easy to implement and requires minimal programming effort. A simple application Microsoft Excel is suitable for online implementation of this approach.

Originality/value

The key steps of the MSPC approach are identifying the out-of-control point, diagnosing the out-of-control point, identifying the “influential” variable responsible for the out-of-control state, and determining the necessary direction and the amount of adjustment required to achieve the state of control. Most of the approaches reported in open literature are focused only until identifying influencing variable, with many restrictive assumptions. This paper addresses all key steps in a single integrated distribution-free approach, which is easy to implement in real time.

Details

International Journal of Quality & Reliability Management, vol. 34 no. 8
Type: Research Article
ISSN: 0265-671X

Keywords

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 parameter…

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: 1 June 2006

Ramesh Marasini and Nashwan Dawood

The monitoring and control of business processes and their variables have strategic importance in order to respond to the dynamics of the world of business. Many monitoring…

585

Abstract

The monitoring and control of business processes and their variables have strategic importance in order to respond to the dynamics of the world of business. Many monitoring processes are focussed on controlling time and cost and the overall performance is evaluated through a standard set of key performance indicators. These passive approaches do not consider a holistic/system view and therefore ignore the interrelationships between various external and internal variables impacting a business process. This paper investigates an application of multivariate statistical process control techniques [mainly principal component analysis (PCA) and partial least squares (PLS)] which have been successfully used in process and chemical industries, to model, monitor, control and predict business process variables. A prototype, innovative managerial control system (IMCS), was developed to investigate the application of PCA and PLS techniques to monitor, control and predict business process performance. Data was collected and analysed using a case study in a precast concrete building products company. This study has proved that the PCA approach can be effectively used to control business processes. Also, the PLS approach is found to provide better forecasts as compared to commonly used decomposition method. The benefits and limitations of using multivariate statistical process control techniques as applied to business process control are highlighted.

Details

Construction Innovation, vol. 6 no. 2
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 1 June 1991

Meng‐Koon Chua and Douglas C. Montgomery

Three functions are identified and integrated into one unique control scheme for multivariate quality control. The control scheme will identify any out‐of‐control samples, select…

Abstract

Three functions are identified and integrated into one unique control scheme for multivariate quality control. The control scheme will identify any out‐of‐control samples, select the subset of variables that are out of control, and diagnose the out‐of‐control variables. New control variable selection algorithm and diagnosis methods are proposed and a framework for the control scheme is developed based on simulation results.

Details

International Journal of Quality & Reliability Management, vol. 8 no. 6
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 26 June 2009

Davood Shishebori and Ali Zeinal Hamadani

The aim of this paper is to consider the effect of gauge measurement capability on the multivariate process capability index (MCp).

Abstract

Purpose

The aim of this paper is to consider the effect of gauge measurement capability on the multivariate process capability index (MCp).

Design/methodology/approach

With respect to measurement capability, the paper investigates the statistical properties of the estimated MCp and considers the effect of gauge measurement capability on the lower confidence bound, hypothesis testing, critical value and power of testing for MCp at the mentioned state.

Findings

The results show that gauge measurement capabilities will notably change the results of estimating and testing the process capability index.

Originality/value

The research would help quality experts to determine whether their processes meet the required capability, and to make more reliable decisions.

Details

International Journal of Quality & Reliability Management, vol. 26 no. 6
Type: Research Article
ISSN: 0265-671X

Keywords

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