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

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

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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.

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KAPABILITAS PROSES DENGAN ESTIMASI FUNGSI DENSITAS KERNEL PADA PRODUKSI DENIM DI PT APAC INTI CORPORA

Jurnal Gaussian ◽

10.14710/j.gauss.v7i3.26665 ◽

2018 ◽

Vol 7 (3) ◽

pp. 326-336

Author(s):

Puput Ramadhani ◽

Dwi Ispriyanti ◽

Diah Safitri

Keyword(s):

Quality Control ◽

Production Process ◽

Density Function ◽

Control Chart ◽

Cross Validation ◽

Kernel Density ◽

Process Capability ◽

Conformity Index ◽

Denim Fabric ◽

Kernel Density Function

The quality of production becomes one of the basic factors of consumer decisions in choosing a product. Quality control is needed to control the production process. Control chart is a tool used in performing statistical quality control. One of the alternatives used when the data obtained is not known distribution is analyzed by nonparametric approach based on estimation of kernel density function. The most important thing in estimating kernel density function is optimal bandwidth selection (h) which minimizes Cross Validation (CV) value. Some of the kernel functions used in this research are Rectangular, Epanechnikov, Triangular, Biweight, and Gaussian. If the process control chart is statistically controlled, a process capability analysis can be calculated using the process conformity index to determine the nature of the process capability. In this research, the kernel control chart and process conformity index were used to analyze the slope shift of Akira-F style fabric and Corvus-SI style on the production of denim fabric at PT Apac Inti Corpora. The results of the analysis show that the production process for Akira-F style is statistically controlled, but Ypk > Yp is 0.889823 > 0,508059 indicating that the process is still not in accordance with the specified limits set by the company, while for Corvus- SI is statistically controlled and Ypk < Yp is 0.637742 < 0.638776 which indicates that the process is in accordance with the specification limits specified by the company. Keywords: kernel density function estimation, Cross Validation, kernel control chart, denim fabric, process capability

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On multivariate control charts

Production ◽

10.1590/s0103-65132011005000010 ◽

2011 ◽

Vol 21 (2) ◽

pp. 235-241 ◽

Cited By ~ 8

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.

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

Journal of Physics Conference Series ◽

10.1088/1742-6596/2106/1/012019 ◽

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.

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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

Revista Ingeniería ◽

10.15517/ri.v29i1.34379 ◽

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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PENERAPAN IMPROVED GENERALIZED VARIANCE PADA PENGENDALIAN KUALITAS PAVING BLOCK SEGIENAM

Jurnal Gaussian ◽

10.14710/j.gauss.v9i1.27526 ◽

2020 ◽

Vol 9 (1) ◽

pp. 87-97

Author(s):

Nathasa Erdya Kristy ◽

Mustafid Mustafid ◽

Sudarno Sudarno

Keyword(s):

Quality Control ◽

Production Process ◽

Control Chart ◽

Process Capability ◽

Quality Characteristics ◽

Quality Characteristic ◽

Generalized Variance ◽

Process Capability Analysis ◽

Capability Analysis

In quality assurance of hexagonal paving block products, quality control is needed so the products that produced are in accordance with the specified standards. Quality control carried out involves two interconnected quality characteristics, that is thickness and weight of hexagonal paving blocks, so multivariate control chart is used. Improved Generalized Variance control chart is a tool used to control process variability in multivariate manner. Variability needs to be controlled because of in a production process, sometimes there are variabilities that caused by engine problems, operator errors, and deffect in raw materials that affect the process. The purpose of this study is to apply Improved Generalized Variance control chart in controlling the quality of hexagonal paving block products and calculating the capability of production process to meet the standards. Based on the assumption of multivariate normal distribution test, it can be seen that the data of quality characteristics of hexagonal paving blocks have multivariate distribution. While based on the correlation test between variables it can be concluded that the characteristics of the quality of thickness and weight correlate with each other. The result of the control using these control chart shows that the process is statistically in control. The results of process capability analysis show that the production process has been running according to the standard because the process capability index value is generated using a weighting of 0.5 for each quality characteristic that is 1.01517. Keywords: Paving Block, Quality Control, Variability, Improved Generalized Variance, Process Capability Analysis

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Economic-statistical design of variable parameters non-central chi-square control chart

Production ◽

10.1590/s0103-65132011005000031 ◽

2011 ◽

Vol 21 (2) ◽

pp. 259-270 ◽

Cited By ~ 3

Author(s):

Maysa Sacramento de Magalhães ◽

Francisco Duarte Moura Neto

Keyword(s):

Cost Function ◽

Production Process ◽

Control Chart ◽

Control Charts ◽

Variable Parameter ◽

Statistical Design ◽

Optimization Techniques ◽

Chi Square ◽

Gradient Based ◽

The Cost

Production processes are monitored by control charts since their inception by Shewhart (1924). This surveillance is useful in improving the production process due to increased stabilization of the process, and consequently standardization of the output. Control charts keep track of a few key quality characteristics of the outcome of the production process. This is done by means of univariate or multivariate charts. Small improvements in control chart methodology can have significant economic impact in the production process. In this investigation, we propose the monitoring of a single variable by means of a variable parameter non-central chi-square control chart. The design of the chart is accomplished by means of optimizing a cost function. We use here a simulated annealing optimization tool, due to the difficulty of classical gradient based optimization techniques to handle the optimization of the cost function. The results show some of the drawbacks of using this model.

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An Application of Univariate and Multivariate Control Charts in Monitoring Water Quality

ASM Science Journal ◽

10.32802/asmscj.2020.sm26(1.3) ◽

2020 ◽

pp. 1-7

Author(s):

Siti Rahayu Mohd Hashim ◽

Azwaan Andrew ◽

Wilter Azwal Malandi

Keyword(s):

Water Quality ◽

Control Chart ◽

Control Charts ◽

Ph Value ◽

Statistical Process ◽

Multivariate Control Charts ◽

Monitoring Process ◽

Process Dispersion ◽

Water Treatments ◽

Multivariate Control

Control chart is a tool for detecting an out-of-control signal in statistical process control (SPC). It is widely used in process monitoring in order to detect changes in process mean or process dispersion. This study aims to illustrate the application of multivariate control charts in monitoring water quality at one of the water treatments plants in Kota Kinabalu, Sabah. The tested water quality variables in this study are turbidity, pH value, dissolved oxygen (DO) and concentration of ferum. Two multivariate control charts, Hotelling’sT2 and MCUSUM control charts are constructed under the violation of the multivariate normality assumption. The purpose is to study the effect of non-normal data upon the monitoring process using the selected multivariate control charts. By comparing the monitoring process between the two types of control charts, the consistency of the results is studied. All the univariate and multivariate control charts produced out-of-control signals from different points, hence inconclusive results obtained. Keywords: Water quality; multivariate control chart; univariate control chart; Hotelling’s T2; MCUSUM

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MULTIVARIATE CONTROL CHARTS BASED ON DATA DEPTH FOR SUBGROUP LOCATION AND SCALE - CASE STUDY

CBU International Conference Proceedings ◽

10.12955/cbup.v6.1292 ◽

2018 ◽

Vol 6 ◽

pp. 1042-1049

Author(s):

Izabela D. Czabak-Górska

Keyword(s):

Control Chart ◽

Control Charts ◽

Quality Characteristics ◽

Data Depth ◽

Multivariate Control Charts ◽

Production Practice ◽

Classic Approach ◽

Multivariate Control ◽

T2 Control Chart

The purpose of the article is to present a method for determining control charts, which allow to control few interrelated quality characteristics. Often, in production practice, there is a need to simultaneously control several interrelated quality characteristics. The use of univariate control charts, separately for each quality characteristics, may lead to inadequate corrective actions of a production process. In such situations, it is recommended to use multivariate control charts, for example, the T2 control chart. However, the use of this classic approach involves making complicated calculations. Therefore, the author of this paper suggests using multivariate control charts based on data depth proposed by Liu. In this paper, the author presented the idea and principles of the multivariate control chart based on data depth and then, using it to assess the statistical stability of the process in a manufacturing company, engaged in the production of window fittings.

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