Monitoring of overall equipment effectiveness by multivariate statistical process control

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.

Download Full-text

Design multivariate statistical process control procedure in the case of Ethio cement

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-07-2021-0227 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Daniel Ashagrie Tegegne ◽

Daniel Kitaw Azene ◽

Eshetie Berhan Atanaw

Keyword(s):

Neural Network ◽

Production Process ◽

Control Chart ◽

Cement Industry ◽

Time Interval ◽

Control Procedure ◽

Sufficient Information ◽

Content Type ◽

Multivariate Control Chart ◽

Multivariate Control

PurposeThis study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T2 chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.Design/methodology/approachMultivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T2 statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.FindingsThe finding of this paper is that the points and lines in the classic Hotelling T2 chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T2 control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.Originality/valueModifying the classical Hotelling T2 control chart by integrating with the concept of graph theory and neural network is new of its kind.

Download Full-text

Diagnosing Problems of Distribution-Free Multivariate Control Chart

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.971-973.1602 ◽

2014 ◽

Vol 971-973 ◽

pp. 1602-1606

Author(s):

Wen Li Shi ◽

Xue Min Zi

Keyword(s):

Control Chart ◽

Historical Data ◽

Important Application ◽

Distribution Free ◽

On Line ◽

Multivariate Control Chart ◽

Diagnostic Framework ◽

Multivariate Control ◽

Control Limits ◽

New Distribution

In order to solve the problem of only have a few historical data that can be used in multivariate process monitoring, a new distribution-free multivariate control chart has been proposed. And in the control chart structure the control limits are determined on-line with the future observations and the historical data. Therefore, the proposed control chart has very important application in practice. However, the research doesn’t study the problem of the fault diagnosis after the control chart alarms. So we use LASSO-based diagnostic framework to identify when a detected shift has occurred and to isolate the shifted components.

Download Full-text

Hotelling T2 chart using the generalized multiple dependent state sampling scheme

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-03-2020-0084 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Sandra García-Bustos ◽

Joseph León ◽

María Nela Pastuizaca

Keyword(s):

Control Chart ◽

Average Run Length ◽

Control Process ◽

Sampling Scheme ◽

Real Problem ◽

Optimal Parameters ◽

Content Type ◽

Multivariate Control Chart ◽

Multivariate Control ◽

Dependent State

PurposeThis research proposes a multivariate control chart, whose parameters are optimized using genetic algorithms (GA) in order to accelerate the detection of a change in the vector of means.Design/methodology/approachThis chart is based on a variation of the Hotelling T2 chart using a sampling scheme called generalized multiple dependent state sampling. For the analysis of performances of this chart, the out-of-control average run length (ARL) values were used for different scenarios. In this comparison, it was considered the classic Hotelling T2 chart and the T2 chart using the scheme called multiple dependent state sampling.FindingsIt was observed that the new chart with its optimized parameters is more efficient to detect an out-of-control process. Additionally, a sensitivity analysis was performed, and it was concluded that the best yields are obtained when the change to be considered in the optimization is small. An application in the resolution of a real problem is given.Originality/valueIn this research, a multivariate control chart is proposed based on the Hotelling T2 statistic but adding a sampling scheme. This makes this control chart more efficient than the classic T2 chart because the new chart not only uses the current information of the T2 statistic but also conditions the decision to consider a process as “in- control” on the statistic's previous information. The practitioner can obtain the optimal parameters of this new chart through a friendly program developed by the authors.

Download Full-text

Distribution-Free Multivariate Control Chart Based on Change-Point Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.971-973.1435 ◽

2014 ◽

Vol 971-973 ◽

pp. 1435-1439

Author(s):

Mei Hua Duan ◽

Xue Min Zi

Keyword(s):

Control Chart ◽

Change Point ◽

Point Model ◽

Multivariate Statistical ◽

Statistical Process ◽

Distribution Free ◽

Multivariate Control Chart ◽

The Mean ◽

Change Point Model ◽

Multivariate Control

It is common to monitor several quality characteristics of a process simultaneously in modern quality control, and it is called multivariate statistical process control (MSPC) in the literature. A change-point control chart for detecting shifts in the mean of a multivariate statistical process is developed for the case where the nominal value of the mean is unknown but some historical samples are available. This control chart is called distribution-free multivariate control chart based on change-point model. Its distribution robustness is a significant advantage where we usually know nothing about the underlying distribution. And the simulated results show that this approach has a good performance across the range of possible shifts.

Download Full-text

Oversizing Thread Diagnosis in Tapping Operation

Metals ◽

10.3390/met11040537 ◽

2021 ◽

Vol 11 (4) ◽

pp. 537

Author(s):

Alain Gil Del Val ◽

Fernando Veiga ◽

Mariluz Penalva ◽

Miguel Arizmendi

Keyword(s):

Control Chart ◽

Manufacturing Systems ◽

Spindle Motor ◽

Multivariate Statistical ◽

Statistical Process ◽

Knowing That ◽

On Line ◽

High Level ◽

Screw Threads ◽

Multivariate Control

Automotive, railway and aerospace sectors require a high level of quality on the thread profiles in their manufacturing systems knowing that the tapping process is a complex manufacturing process and the last operation in a manufacturing cell. Therefore, a multivariate statistical process control chart, for each tap, is presented based on the principal components of the torque signal directly measured from spindle motor drive to diagnosis the thread profile quality. This on-line multivariate control chart has implemented an alarm to avoid defected screw threads (oversized). Therefore, it could work automatically without any operator intervention assessing the thread quality and the safety is guaranteed during the tapping process.

Download Full-text

Improving Phase I Monitoring of Dirichlet Regression Profiles

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539318500304 ◽

2018 ◽

Vol 25 (06) ◽

pp. 1850030 ◽

Cited By ~ 1

Author(s):

Hourieh Foroutan ◽

Amirhossein Amiri ◽

Reza Kamranrad

Keyword(s):

Phase I ◽

Control Chart ◽

Control Charts ◽

Compositional Data ◽

Ratio Test ◽

Statistical Process ◽

Explanatory Variables ◽

Monitoring Procedure ◽

Multivariate Control

In most statistical process control (SPC) applications, quality of a process or product is monitored by univariate or multivariate control charts. However, sometimes a functional relationship between a response variable and one or more explanatory variables is established and monitored over time. This relationship is called “profile” in SPC literature. In this paper, we specifically consider processes with compositional data responses, including multivariate positive observations summing to one. The relationship between compositional data responses and explanatory variables is modeled by a Dirichlet regression profile. We develop a monitoring procedure based on likelihood ratio test (lrt) for Phase I monitoring of Dirichlet regression profiles. Then, we compare the performance of the proposed method with the best method in the literature in terms of probability of signal. The results of simulation studies show that the proposed control chart has better performance in Phase I monitoring than the competing control chart. Moreover, the proposed method is able to estimate the real time of a change as well. The performance of this feature is also investigated through simulation runs which show the satisfactory performance. Finally, the application of the proposed method is illustrated based on a real case in comparison with the existing method.

Download Full-text