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

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 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 PENAKSIRAN FUNGSI DENSITAS TIPE KERNEL DENGAN METODE CROSS-VALIDATION (C-V)

2007 ◽  
Vol 1 (1) ◽  
pp. 27-31
Author(s):  
Mozart W. Talakua
Keyword(s):  
Maximum Likelihood ◽  
Density Function ◽  
Cross Validation ◽  
Kernel Density ◽  
Smoothing Parameter ◽  
Least Square ◽  
Kernel Density Function

The choice of the bandwidth h is the main problem of kernel density function. In some situations it might be quite useful to have a set of retes corresponding to different bandwidth.It is necessary to agree which bandwidth is an appropriate one. Cross-Validation (C-V) is a well-known method to optimize the smoothing parameter h. In this research, we will analysis about three methods of Cross-Validation: Maximum Likelihood Cross-Validation, Least-Square Cross-Validation (Unbiased Cross-Validation) and Biased Cross-Validation with bandwidth choice optimal.

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 A Method for Calibrating Deterministic Forecasts of Rare Events

2012 ◽  
Vol 27 (2) ◽  
pp. 531-538 ◽  
Author(s):  
Patrick T. Marsh ◽  
John S. Kain ◽  
Valliappa Lakshmanan ◽  
Adam J. Clark ◽  
Nathan M. Hitchens ◽  
...  
Keyword(s):  
Density Function ◽  
Rare Events ◽  
Numerical Models ◽  
Kernel Density ◽  
Spatial Error ◽  
Error Characteristics ◽  
Probabilistic Forecasts ◽  
Insight Into ◽  
Kernel Density Function

Abstract Convection-allowing models offer forecasters unique insight into convective hazards relative to numerical models using parameterized convection. However, methods to best characterize the uncertainty of guidance derived from convection-allowing models are still unrefined. This paper proposes a method of deriving calibrated probabilistic forecasts of rare events from deterministic forecasts by fitting a parametric kernel density function to the model’s historical spatial error characteristics. This kernel density function is then applied to individual forecast fields to produce probabilistic forecasts.

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