scholarly journals PENERAPAN METODE PENGENDALIAN KUALITAS MEWMA BERDASARKAN ARL DENGAN PENDEKATAN RANTAI MARKOV (Studi Kasus: Batik Semarang 16, Meteseh)

2021 ◽  
Vol 10 (1) ◽  
pp. 125-135
Author(s):  
Enggartya Andini ◽  
Sudarno Sudarno ◽  
Rita Rahmawati
Keyword(s):  
Markov Chain ◽  
Production Process ◽  
Control Chart ◽  
Average Run Length ◽  
Moving Average ◽  
Control Limit ◽  
Quality Characteristic ◽  
Markov Chain Approach ◽  
Mewma Control Chart ◽  
Weighted Moving Average

An industrial company requires quality control to maintain quality consistency from the production results so that it is able to compete with other companies in the world market. In the industrial sector, most processes are influenced by more than one quality characteristic. One tool that can be used to control more than one quality characteristic is the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. The graph is used to determine whether the process has been controlled or not, if the process is not yet controlled, the next analysis that can be used is to use the Average Run Length (ARL) with the Markov Chain approach. The markov chain is the chance of today's event is only influenced by yesterday's incident, in this case the chance of the incident in question is the incident in getting a sampel of data on the production process of batik cloth to get a product that is in accordance with the company standards. ARL is the average number of sample points drawn before a point indicates an uncontrollable state. In this study, 60 sample data were used which consisted of three quality characteristics, namely the length of the cloth, the width of the cloth, and the time of the fabric for the production of written batik in Batik Semarang 16 Meteseh. Based on the results and discussion that has been done, the MEWMA controller chart uses the λ weighting which is determined using trial and error. MEWMA control chart can not be said to be stable and controlled with λ = 0.6, after calculating, the value is obtained Upper Control Limit (BKA) of 11.3864 and Lower Control Limit (BKB) of 0. It is known that from 60 data samples there is a Tj2 value that comes out from the upper control limit (BKA) where the amount of 15.70871, which indicates the production process is not controlled statistically. Improvements to the MEWMA controller chart can be done based on the ARL with the Markov Chain approach. In this final project, the ARL value used is 200, the magnitude of the process shift is 1.7 and the r value is 0.28, where the value of r is a constant obtained on the r parameter graph. The optimal MEWMA control chart based on ARL with the Markov Chain approach can be said to be stable and controlled if there is no Tj2 value that is outside the upper control limit (BKA). The results of the MEWMA control chart based on the ARL with the Markov Chain approach show that the process is not statistically capable because the MCpm value is 0.516797 and the MCpmk value is 0.437807, the value indicates a process capability index value of less than 1. Keywords: Handmade batik, Multivariate Exponentially Weighted Moving Average (MEWMA), Average Run Length (ARL), Capability process.

scholarly journals PENERAPAN PENGENDALIAN KUALITAS DENGAN MEWMA DAN FUNGSI DENSITAS KERNEL MULTIVARIAT (Studi Kasus: PT Sukorejo Indah Textile Kab. Batang)

2019 ◽  
Vol 8 (1) ◽  
pp. 46-57
Author(s):  
Mifta Fara Sany ◽  
Rukun Santoso ◽  
Arief Rachman Hakim
Keyword(s):  
Quality Control ◽  
Product Quality ◽  
Control Chart ◽  
Control Charts ◽  
Average Length ◽  
Moving Average ◽  
Control Limit ◽  
Trial And Error ◽  
Mewma Control Chart ◽  
Weighted Moving Average

In an era of industrial revolution 4.0, technology is increasingly sophisticated, requiring companies to be more creative. Product quality control is an effort to minimize the defective products produced by the company. The production of weaving sarongs at PT SUKORINTEX pays attention to the accuracy of the length and width of the sarong to conform to the standards set by the company. To find out the quality of woven sarong products at PT SUKORINTEX, analysis was performed using Multivariate Exponentially Weighted Moving Average (MEWMA) control charts and multivariate kernel control charts. The research variable was the characteristics of the X sarongs which is reflected in 2 variates, namely the average length and average width. Based on the results and discussion that has been done, the MEWMA control chart used a weighting λ which is determined using trial and error. MEWMA control charts can be said to be stable and controlled by λ = 0.1, Upper Control Limit (UCL) of 14.62943, and Lower Control Limit (LCL) of 0. Multivariate kernel control chart were declared uncontrolled with α = 0.1 and level = 0.06130611 because there were data that was outside the contour. Chart improvement was done by trial and error and obtained a controlled chart results at α = 0.01 and a level value of 0.03125701. Based on this case study, the quality control of the average length and width of WADIMOR woven sarong types 30 STR with MEWMA is better than the multivariate kernel density, because MEWMA is controlled and stable in controlling product quality. The results of the MEWMA control chart show a capable process because more than 1 process capability index value is obtained. Keywords: Multivariate Exponentially Weighted Moving Average (MEWMA) control chart, multivariate kernel control chart, process capability.

scholarly journals PENGENDAIAN MULTIVARIATE DENGAN DIGRAM KONTROL MEWMA ENGGUNAKAN METODE SIX SIGMA (STUDI KASUS PT FUMIRA SEMARANG TAHUN 2019)

2020 ◽  
Vol 9 (1) ◽  
pp. 98-111
Author(s):  
Puspita Ayu Utami ◽  
Mustafid Mustafid ◽  
Tatik Widiharih
Keyword(s):  
Production Process ◽  
Control Chart ◽  
Six Sigma ◽  
Moving Average ◽  
Steel Production ◽  
Galvanized Steel ◽  
Exponentially Weighted Moving Average ◽  
Mewma Control Chart ◽  
Weighted Moving Average ◽  
Exponentially Weighted

As one of the biggest corrugation producing industries, PT Fumira Semarang is always required to fulfill customer needs by continuously improving their quality. Galvanized Steel is the raw material for the production of corrugation at PT Fumira Semarang. There are three important quality characteristics to be controlled in order that the results of galvanized steel production fit the standards to be manufactured as corrugation are waves, rust, and scratches. Six Sigma is a method for controlling quality. Six Sigma has focus on reducing defects, by standard 3,4 defects per one million opportunties. This research aims to identify the galvanized steel production process using Six Sigma method with MEWMA control chart and the capability of the process to fit the standards. Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is a tool used to control multivariate process averages. The result of this research are MEWMA control chart with lambda 0.7 shows that the process is controlled statistically and The Sigma value for waves is 2,33, for rust 2,05, and for scratches 2,64. And the research reveals the galvanized steel production process has not fit to the standard because the process capabilty index is 0,2805. Keywords: Galvanized Steel, Quality Control, Six Sigma, Multivariate Exponentially Weighted Moving Average, Process Capability Analysis

scholarly journals The Expected Average Run Length of the EWMA Median Chart with Estimated Process Parameters

2020 ◽  
Vol 49 (3) ◽  
pp. 19-24
Author(s):  
Huay Woon You ◽  
Michael Khoo Boon Chong ◽  
Chong Zhi Lin ◽  
Teoh Wei Lin
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Process Parameters ◽  
Average Run Length ◽  
Moving Average ◽  
Background Knowledge ◽  
Exponentially Weighted Moving Average ◽  
Control Phase ◽  
Run Length ◽  
Weighted Moving Average

The performance of a control chart is commonly investigated based on the assumption of known process parameters. Nevertheless, in most manufacturing and service applications, the process parameters are usually unknown to practitioners. Hence, they are estimated from an in-control Phase-I samples. As such, the performance of the control chart with estimated process parameters will behave differently from the corresponding chart with known process parameters. To study this issue, the exponentially weighted moving average (EWMA) median chart is examined in this article. The EWMA median chart is traditionally investigated based on the average run length (ARL). The limitation of the ARL is that it requires practitioners to specify the shift size in advance. This phenomenon is not ideal for practitioners who do not have background knowledge of the process. In view of this, the EWMA median chart with known and estimated process parameters is studied based on the ARL and expected average run length (EARL). The results indicate that as long as the particular shift size is within the range of shifts, the performance of the chart is almost the same, for the EWMA median chart with known and estimated process parameters.

Design of a Variable Sampling Interval EWMA Median Control Chart

2019 ◽  
Vol 26 (05) ◽  
pp. 1950021 ◽  
Author(s):  
Kim Phuc Tran ◽  
Philippe Castagliola ◽  
Thi Hien Nguyen ◽  
Anne Cuzol
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Food Production ◽  
Average Run Length ◽  
Moving Average ◽  
Sampling Interval ◽  
Performance Comparison ◽  
Variable Sampling Interval ◽  
Variable Sampling ◽  
Weighted Moving Average

In the literature, median type control charts have been widely investigated as easy and efficient means to monitor the process mean when observations are from a normal distribution. In this work, a Variable Sampling Interval (VSI) Exponentially Weighted Moving Average (EWMA) median control chart is proposed and studied. The Markov chains are used to calculate the average run length to signal (ARL). A performance comparison with the original EWMA median control chart is made. The numerical results show that the proposed chart is considerably more effective as it is faster in detecting process shifts. Finally, the implementation of the proposed chart is illustrated with an example in food production process.

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