scholarly journals PENERAPAN IMPROVED GENERALIZED VARIANCE PADA PENGENDALIAN KUALITAS PAVING BLOCK SEGIENAM

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

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

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

A Research on Remanufacturing Products Quality Control

2011 ◽  
Vol 314-316 ◽  
pp. 2162-2167
Author(s):  
Yun Liu ◽  
Bin Shi Xu ◽  
Pei Jing Shi ◽  
Bo Hai Liu
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Production Process ◽  
Control Chart ◽  
Product Certification ◽  
Low Volume ◽  
Posterior Analysis

The quality of remanufacturing products, which is always restricting the development of remanufacturing industry, is one of sixty-four-dollar questions. By detecting the cores, process control in remanufacturing production and certificating remanufacturing products, quality control of remanufacturing products is studied. Because of cores different in original states, remanufacturing is in low-volume on the whole. Based on Bayesian posterior analysis, the paper improves the control chart and uses the previous data to monitor the production process. Finally, some advances are given to remanufacturing product certification.

scholarly journals PERANCANGAN INSTRUKSI KERJA DOKUMEN DAN VISUAL PADA MESIN ELECTRICAL DISCHARGE MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 80-89
Author(s):  
Ahmad Padhil ◽  
Nurhayati Rauf
Keyword(s):  
Quality Control ◽  
Control Chart ◽  
Electrical Discharge Machine ◽  
Control Process ◽  
Pareto Chart ◽  
Packaging Process ◽  
Process Capability Analysis ◽  
Capability Analysis ◽  
Fishbone Diagram ◽  
Discharge Machine

Quality is a factor that can increase product competitiveness. In improving products quality is necessary to control the quality (Quality Control) of the process activities. This study aims to determine the quality control of granulated sugar packaging process by using the seven tool method consisting of check sheet, histogram, Pareto chart, flow chart, scatter diagram, control chart and fishbone diagram. From 30 days observation concluded that the highest type of damage consist of metal detection by 145 bags and torn packaging by 50 bags. Then, from the results of control chart known that out of control process still happen. Both types of damage then revised, and process capability analysis is carried out, so can be concluded that the process is not capable in making the product according to specifications. Therefore, an analysis using fishbone diagram shows that the causes of damage are human, working methods, and machines. From these factors, Standard Operating Procedures can be done as well as designing new machines in packaging process

scholarly journals Analisis Masalah Kualitas pada M/C Crank Shaft M2 dengan Menggunakan Tool Capability Process di PT XYZ, Pegangsaan Dua, Jakarta

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Gidion Karo Karo ◽  
Jessie Deborah R. Makapedua
Keyword(s):  
Process Improvement ◽  
Control Process ◽  
Process Capability ◽  
Repair Process ◽  
Process Capability Analysis ◽  
Normality Test ◽  
Quality Control Process ◽  
Capability Indices ◽  
Capability Analysis

<p>Process Capability is a tool that is often used in the process of quality improvement, especially for process improvement. This study uses a process capability analysis on crank shaft production line 2 for motorcycles. By using normality test data and process capability indices for calculation of Cp/Cpk, shows that most of the data obtained are not normally distributed, so need to transform the data into normal, which can then be followed by the calculation of process capability. For the calculation of Cp/Cpk, it was found that there were some machines that still need to get tight control to meet the specification. It shows that mass production is still less stable. In order to meet the specifications, it is necessary to improve the quality of the repair process to reduce the variation in the process.</p><p>Keywords: Process Capability, Quality Control, Process Improvement</p>

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 Stability assessment and process capability analysis in a food pasta company

2021 ◽  
pp. 231-251
Author(s):  
Roxana González Álvarez ◽  
Aníbal Barrera García ◽  
Ana Beatriz Guerra Morffi ◽  
Juan Felipe Medina Mendieta
Keyword(s):  
Control Charts ◽  
Process Capability ◽  
Quality Characteristic ◽  
Statistical Quality Control ◽  
Quality Level ◽  
Statistical Control ◽  
Pareto Chart ◽  
Process Capability Analysis ◽  
Reduction Techniques ◽  
Capability Analysis

Statistical quality control is a set of tools and techniques that allows to verify, monitor and control the variability of processes to improve product quality and business competitiveness. The objective of this study was to evaluate the pasta production process of a company that belongs to the food industry sector in terms of stability and compliance of quality specifications. The Six Sigma improvement methodology was used, which focuses on identifying and eliminating the causes of variation in the processes. Data collection was accomplished by the use of different techniques, such as: interviews, brainstorming, review of documents, teamwork and direct observation. In addition, process documentation techniques and classical quality tools including Pareto chart, control charts, process capability analysis, histogram, Ishikawa diagram and experimental design were used. Multivariate data reduction techniques were also applied. The results showed for the quality characteristic Humidity that the process is out of statistical control and it is uncapable to meet the required specifications, for which the causes were investigated and improvement actions were proposed, achieving an increase in the sigma quality level.

LIKELIHOOD RATIO BASED MULTI-ATTRIBUTE CONTROL CHART

2005 ◽  
Vol 12 (02) ◽  
pp. 149-166 ◽  
Author(s):  
M. P. GADRE ◽  
R. N. RATTIHALLI
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Likelihood Ratio ◽  
Control Chart ◽  
Joint Distribution ◽  
Multivariate Quality Control ◽  
Lr Test

In a production process, when quality of the product depends on more than one characteristic, 'Multivariate Quality Control' (MQC) techniques are efficiently used. Many MQC techniques have been developed to control the multivariate variable processes, but no much work has been reported to control the multivariate attribute processes. In this article, to detect a change in the vector of fraction non-conforming, we develop 'Likelihood Ratio based Multi-Attribute Control Chart' (LR-MACC) using the exact joint distribution and the LR-test under multinomial setup. It is verified that, in some situations, LR-MACC is superior to the MNP chart proposed by Lu et al.7 When MACC gives a signal, the attributes responsible are not readily identifiable. Therefore, a procedure to detect the responsible attributes is also developed.

Sign in / Sign up

Export Citation Format

Share Document