scholarly journals Comparison of short runs control chart and T2 Hotelling control chart for monitoring sunergy glass production process

2021 ◽  
Vol 2106 (1) ◽  
pp. 012019
Author(s):  
M Qori’atunnadyah ◽  
Wibawati ◽  
W M Udiatami ◽  
M Ahsan ◽  
H Khusna
Keyword(s):  
Production Process ◽  
Control Chart ◽  
Mass Production ◽  
Control Charts ◽  
Manufacturing Industry ◽  
Average Run Length ◽  
Glass Production ◽  
Short Run ◽  
Multivariate Control Chart ◽  
Multivariate Control

Abstract In recent years, the manufacturing industry has tended to reduce mass production and produce in small quantities, which is called “Short Run Production”. In such a situation, the course of the production process is short, usually, the number of productions is less than 50. Therefore, a control chart for the short run production process is required. This paper discusses the comparison between multivariate control chart for short run production (V control chart) and T2 Hotelling control chart applied to sunergy glass data. Furthermore, a simulation of Average Run Length (ARL) was carried out to determine the performance of the two control charts. The results obtained are that the production process has not been statistically controlled using either the V control chart or the T2 Hotelling control chart. The number of out-of-control on the control chart V using the the EWMA test is more than the T2 Hotelling control chart. Based on the ARL value, it shows that the V control chart is more sensitive than the T2 Hotelling control chart.

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

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.

scholarly journals Evaluasi Kinerja Bagan Kendali Fm Pada Proses Short-Run

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Darmanto Darmanto
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Job Shop ◽  
Average Run Length ◽  
Just In Time ◽  
Multivariate Normal ◽  
Sensitivity Level ◽  
Short Run ◽  
Control Limits ◽  
Vector Shift

<p><em>The manufacturing production process that is currently trend is short-run. Short-run process is a job shop and a just in-time. These causes the process parameters to be unknown due to unavailability of data and generally a small amount of product. The control chart is one of the control charts which  designed for the short run. The procedure of the control chart follows the concept of succesive difference and under the assumption of the multivariate Normal distribution. The sensitivity level of a control chart is evaluated based on the average run length (ARL) value. In this study, the ARL value was calculated based on the shift simulation of the average vector by recording the first m-point out of the control limits. The average vector shift simulation of the target () is performed simultaneously with the properties of a positive shift (=+ δ). Variations of data size and many variables in this study were m = 20, 50 and p = 2, 4, 8, respectively. Each scheme (a combination of δ, m and p) is iterated 250,000 times. The simulation results show that for all schemes when both parameters are known ARL<sub>0 </sub>≈ 370. But, when parameters are unknown, ARL<sub>1</sub> turn to smaller. This conclusion also implied when the number of p and n are increased, it reduce the sensitivity of the control chart.</em></p>

scholarly journals On multivariate control charts

Production ◽  
2011 ◽  
Vol 21 (2) ◽  
pp. 235-241 ◽  
Author(s):  
Marianne Frisén
Keyword(s):  
Control Chart ◽  
Industrial Production ◽  
Control Charts ◽  
Multivariate Control Charts ◽  
Multivariate Control Chart ◽  
Multivariate Control

Industrial production requires multivariate control charts to enable monitoring of several components. Recently there has been an increased interest also in other areas such as detection of bioterrorism, spatial surveillance and transaction strategies in finance. In the literature, several types of multivariate counterparts to the univariate Shewhart, EWMA and CUSUM methods have been proposed. We review general approaches to multivariate control chart. Suggestions are made on the special challenges of evaluating multivariate surveillance methods.

Hotelling T2 chart using the generalized multiple dependent state sampling scheme

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.

The Optimization Design Method of Multivariate Control Chart with Adaptive Sample Size

2011 ◽  
Vol 403-408 ◽  
pp. 4108-4113
Author(s):  
Hai Yu Wang
Keyword(s):  
Markov Chain ◽  
Sample Size ◽  
Control Chart ◽  
Optimization Design ◽  
Design Method ◽  
Average Run Length ◽  
Fixed Sample ◽  
Markov Chain Method ◽  
Multivariate Control Chart ◽  
Multivariate Control

ARL (Average Run Length) is used as a tool to measure the performance of control chart. But it isn’t very accurate. In this paper, APL (Average Product Length) is used as a criterion of multivariate control chart performance assessment and a multivariate chart with adaptive sample size is proposed. A Markov chain method is proposed to calculate the APL of multivariate chart with adaptive sample size and then the optimization design method of this chart is discussed. By comparing with traditional fixed sample size design method, we can find that this method have higher efficiency.

scholarly journals Study on Likelihood-Ratio-Based Multivariate EWMA Control Chart Using Lasso

2021 ◽  
Vol 25 (1) ◽  
pp. 3-15
Author(s):  
Takumi Saruhashi ◽  
Masato Ohkubo ◽  
Yasushi Nagata
Keyword(s):  
Covariance Matrix ◽  
Likelihood Ratio ◽  
Control Chart ◽  
Control Charts ◽  
Ewma Chart ◽  
Regularization Parameters ◽  
Multivariate Control Chart ◽  
Multivariate Control ◽  
Mean Vector ◽  
Variance Covariance Matrix

Purpose: When applying exponentially weighted moving average (EWMA) multivariate control charts to multivariate statistical process control, in many cases, only some elements of the controlled parameters change. In such situations, control charts applying Lasso are useful. This study proposes a novel multivariate control chart that assumes that only a few elements of the controlled parameters change. Methodology/Approach: We applied Lasso to the conventional likelihood ratio-based EWMA chart; specifically, we considered a multivariate control chart based on a log-likelihood ratio with sparse estimators of the mean vector and variance-covariance matrix. Findings: The results show that 1) it is possible to identify which elements have changed by confirming each sparse estimated parameter, and 2) the proposed procedure outperforms the conventional likelihood ratio-based EWMA chart regardless of the number of parameter elements that change. Research Limitation/Implication: We perform sparse estimation under the assumption that the regularization parameters are known. However, the regularization parameters are often unknown in real life; therefore, it is necessary to discuss how to determine them. Originality/Value of paper: The study provides a natural extension of the conventional likelihood ratio-based EWMA chart to improve interpretability and detection accuracy. Our procedure is expected to solve challenges created by changes in a few elements of the population mean vector and population variance-covariance matrix.

scholarly journals DIAGRAM KONTROL MULTIVARIAT np DAN DIAGRAM KONTROL JARAK CHI-SQUARE DALAM PENGENDALIAN KUALITAS PRODUK KAIN DENIM (Studi Kasus di PT Apac Inti Corpora)

2018 ◽  
Vol 7 (4) ◽  
pp. 385-396
Author(s):  
Dwi Harti Pujiana ◽  
Mustafid Mustafid ◽  
Di Asih I Maruddani
Keyword(s):  
Production Process ◽  
Control Chart ◽  
Control Charts ◽  
Process Capability ◽  
Chi Square ◽  
Multivariate Control Charts ◽  
Distance Control ◽  
Denim Fabric ◽  
Multivariate Control

Denim fabric sort number 78032 is one type of fabric in the last 4 years almost every month produced by PT Apac Inti Corpora. In the continuity of denim fabric production process, there are data defects (non-conformity) that causes the quality of denim fabric decreases. To maintain the consistency of the quality of products produced in accordance with the specified specifications, it is necessary to control the quality of the production process that has been running for this. Multivariate control charts attributes used are multivariate control charts np using the number of samples and the proportion of disability data with correlation between variables while the chi-square distance control charts use squared distances with uncorrelated data between variables. The results showed that in the multivariate control chart np there were 2 out-of-control observations in the phase II data using control limits from phase I data already controlled by the value of BKA of 636321.4. While in the chi-square distance control chart showed all observations are in in-control condition with BKA value of 0.06536. Controlled production process obtained multivariate process capability value  for multivariate control np diagram of 0.625142 <1 which means the process is not capable, while the value of process capability in the chi-square distance control chart is 1.1329> 1 which means the process is capable. Keywords: denim fabric, multivariate np control chart, chi-square distance control chart, multivariate process capability

scholarly journals Robust Multivariate Shewhart Control Chart Based on the Stahel-Donoho Robust Estimator and Mahalanobis Distance for Multivariate Outlier Detection

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2772
Author(s):  
Ishaq Adeyanju Raji ◽  
Nasir Abbas ◽  
Mu’azu Ramat Abujiya ◽  
Muhammad Riaz
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Real Life ◽  
Robust Estimator ◽  
Estimation Of Parameters ◽  
Run Length ◽  
Control Charting ◽  
Shewhart Control ◽  
Multivariate Control Chart ◽  
Multivariate Control

While researchers and practitioners may seamlessly develop methods of detecting outliers in control charts under a univariate setup, detecting and screening outliers in multivariate control charts pose serious challenges. In this study, we propose a robust multivariate control chart based on the Stahel-Donoho robust estimator (SDRE), whilst the process parameters are estimated from phase-I. Through intensive Monte-Carlo simulation, the study presents how the estimation of parameters and presence of outliers affect the efficacy of the Hotelling T2 chart, and then how the proposed outlier detector brings the chart back to normalcy by restoring its efficacy and sensitivity. Run-length properties are used as the performance measures. The run length properties establish the superiority of the proposed scheme over the default multivariate Shewhart control charting scheme. The applicability of the study includes but is not limited to manufacturing and health industries. The study concludes with a real-life application of the proposed chart on a dataset extracted from the manufacturing process of carbon fiber tubes.

scholarly journals Propuesta de Carta de Control Multivariada utilizando la Media Winsorizada basada en la Carta |S| / Proposed Multivariate Control Chart using Winsorized Mean based on the |S| Chart

2018 ◽  
Vol 29 (1) ◽  
pp. 65-79
Author(s):  
Rister Junior Barreto Pombo ◽  
Angellys Paola Ariza Guerrero ◽  
Roberto José Herrera Acosta
Keyword(s):  
Covariance Matrix ◽  
Control Chart ◽  
Control Charts ◽  
Quality Monitoring ◽  
Multivariate Control Charts ◽  
Global Quality ◽  
Simultaneous Evaluation ◽  
Multivariate Control Chart ◽  
Implementation Methodology ◽  
Multivariate Control

Resumen— El monitoreo global de la calidad de un producto está sujeto a la evaluación simultánea de varias de sus características; es necesario bajo estas condiciones la implementación de las cartas de control tipo multivariadas. La variabilidad, en este caso la matriz de varianza covarianza, es sin duda el más importante de los estadísticos desde la perspectiva multivariada, que puede ser monitoreada con distintas cartas. Entre éstas se encuentran: las cartas Shewhart, CUSUM y EWMA. En este artículo se desarrolla una metodología de implementación de la Media Winsorizada en la carta de control multivariada de varianza efectiva |S|, encontrando una gran utilidad en procesos con valores extremos.  El estudio muestra además una comparación entre la carta de control tradicional multivariante y la carta propuesta, que muestra mayor sensibilidad.Abstract— The global quality monitoring of a product is often subject to the simultaneous evaluation of several of its features; under these circumstances it is necessary to implement multivariate control charts. Variability, in this particular case, the variance-covariance matrix is indisputably the most important of the statistics from the multivariate perspective and it can be monitored with different charts, among these: Shewhart, CUSUM and EWMA. This article develops the Winsorized Mean in the effective variance multivariate control |S|-chart implementation methodology and it was demonstrated that the modification was more efficient when the sample hat outliers. This study shows a comparison between the traditional multivariate control chart and a proposed chart which was found to have more sensitivity. 

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