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

*Puspita Ayu Utami  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Mustafid Mustafid  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Tatik Widiharih  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Published: 28 Feb 2020.
Open Access Copyright 2020 Jurnal Gaussian
License URL: http://creativecommons.org/licenses/by-nc-sa/4.0

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Abstract

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

Keywords: Galvanized Steel; Quality Control; Six Sigma; Multivariate Exponentially Weighted Moving Average; Process Capability Analysis

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Section: Articles
Language: ID
In Vol 9, No 1 (2020): Jurnal Gaussian
Statistics: 81 108
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