scholarly journals STATISTICAL PROCESS CONTROL (SPC) UNTUK PENGENDALIAN KUALITAS PRODUK MEBEL DI UD. IHTIAR JAYA

2020 ◽  
Vol 6 (1) ◽  
pp. 50
Author(s):  
RICKI ELYAS ◽  
WIWIK HANDAYANI
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Method ◽  
Statistical Process ◽  
Dominant Type ◽  
Fishbone Diagram ◽  
Teak Wood ◽  
Product Defect

ABSTRACT The influence of globalization in the industrial world today has led to intense and competitive competition between companies. UD. Ihtiar Jaya is a manufacturing company engaged in furniture, especially those made from teak wood. The company serves retail product sales, as well as servicing or renovating various types of furniture. To be able to produce good quality, quality control activities are carried out. This research aims to determine the number of defective furniture products and the most dominant type of defects at UD. Ihtiar Jaya, to analyze the implementation of quality control at UD. Jaya Jaya uses Statistical Process Control in an effort to reduce the number of defective products, and to identify what factors cause damage/defects in products manufactured by UD. Ihtiar Jaya.In this research, the sample used was the number of defects in furniture products at UD. Ihtiar Jaya in 2018. The technique of determining this sample uses purposive sampling. This research uses the Statistical Process Control method, namely check sheet, pareto diagram, control chart, and fishbone diagram.Based on the results of the check sheet, it was concluded that the average product defect was 10.38% per month. From the results of the Pareto diagram can be seen that the highest level of defects is scratched with a total of 104 units or 61.18% of the total defective products in 2018. While the results of the control chart that have been made can be seen that the quality control is not controlled and not in accordance with standards. From the results of fishbone diagram concluded that the factors that cause product defects are human factors, production machinery, materials, work methods, and work environment.

scholarly journals ANALISIS KUALITAS PRODUK MENGGUNAKAN METODE STATISTIC PROCESS CONTROL (SPC) MINITAB DAN TIME-CHARTING DI PT. COCA-COLA BOTTLING INDONESIA UNIT LAMPUNG

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Joko Saryono ◽  
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Method ◽  
Production Data ◽  
Statistical Process ◽  
Research Results ◽  
Statistic Process Control ◽  
Gas Volume ◽  
A Company

Abstract PT. COCA-COLA BOTTLING INDONESIA is a company engaged in the field of Agro-industry is bottling soft drinks and not sparkling. The products produced are Coca-Cola, Sprite, Fanta, and Tea. To be able to compete with similar industries then the company implements quality control by Statistical Process Control method. In the development of this SPC many methods there are manual or who use the software. Currently PT. Coca-Cola Bottling Indonesia in quality control using Time Charting method, but since the transition from Minitab to Time Charting the tendency of the value of capability below standard, whereas production data is almost the same as using Minitab. The purpose of this research is to analyze the inequality of Statistical Process Control between Minitab 13 and Time Charting. Time Charting method is a new method that is given by the headquarters for the process of quality control can be fast and accurate. Quality control with the Statistical Process Control of Minitab and Time Charting methods after the results of the research results was found to be part of different LSL and USL charging, and Calculate Statistic Using different from Minitab method should still be 6 but in written procedure 3. For writing LSL And USL if the Time Charting is determined by the head office while Minitab analysts fill in based on experiments on the decrease of gas volume marketed in previous years. From the research results obtained Cpk data for Minitab method 13 is Sprite 390 ml 1.47, Sprite 1000 ml 1.90 and Sprite 1500 ml 1.38. The result of the research was using Minitab method and the Charting Time of Capacity that is above 1.33 average. The causes of the resulting inequality of both methods are the LSL, USL and Calculate Statistic Using values. The smaller the value of Calculate Statistic Using the higher Cpk produced. Keywords: Production, Statistical Process Control, Quality.

scholarly journals PENGENDALIAN KUALITAS PRODUK BRAKE LINING PADA FORMULA NON-ASBASE DENGAN METODE STATISTICAL PROSES CONTROL (SPC) DAN ROOT CAUSE ANALYSIS (RCA) DI PT. XYZ SURABAYA

2019 ◽  
Vol 20 (1) ◽  
pp. 1
Author(s):  
Issa Dyah Utami
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Root Cause Analysis ◽  
Hot Press ◽  
Statistical Process ◽  
Cause Analysis ◽  
Root Cause ◽  
Brake Lining

XYZ adalah salah satu perusahaan manufaktur yang bergerak dalam produksi komponen otomotif, produk yang dihasilkan yaitu Brake Lining dengan formula non-asbase diantaranya formula Lf-4, KN-356, NA-109F, NA-135, dan NA-146. Dalam menghadapi persaingan yang ada, PT. XYZ berusaha meningkatkan kualitas produk yang dihasilkan dengan target reject sebesar 0,5% pada tahun 2018. Permasalahan manajemen quality control yang ada di PT. XYZ salah satunya pada area hot-press nomor 11 dan 12 tidak pernah bisa mencapai target produksi dan seringnya keterlambatan barang dari area pre-forming. Sehingga perlu adanya penanganan segera dengan melakukan analisis perbaikan kualitas guna menghasilkan produk yang sesuai spesifikasi dan dapat mencapai target produksi yang ditetapkan oleh perusahaan. Metode yang digunakan dalam penelitihan ini adalah Statistical Process Control (SPC) dan Root Cause Analysis (RCA). Hasil analisis check sheet dan diagram pareto dapat dilihat frekuensi defect paling banyak terjadi pada formula LF-4 model K-2252 dengan jumlah cacat sebanyak 69 produk pada bulan Agustus sampai September dengan presentase reject sebanyak 19%. Serta dari gambar control chart P data dalam keadaan tidak terkontrol. Hal ini terjadi karena faktor manusia, faktor metode, faktor mesin, dan faktor material.

scholarly journals Estimating the Time to Benefit for Preventive Drugs with the Statistical Process Control Method: An Example with Alendronate

Drugs & Aging ◽  
2016 ◽  
Vol 33 (5) ◽  
pp. 347-353 ◽  
Author(s):  
Esther M. M. van de Glind ◽  
Hanna C. Willems ◽  
Saeid Eslami ◽  
Ameen Abu-Hanna ◽  
Willem F. Lems ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Method ◽  
Statistical Process

Effectiveness Indices for Statistical Process Control Chart Performance

2019 ◽  
Vol 27 (1) ◽  
pp. 221-226
Author(s):  
Siyuan F. Yang ◽  
Wei-Ting K. Chien
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Statistical Process

scholarly journals Monitoring of mechanical sugarcane harvesting through control charts

2015 ◽  
Vol 35 (6) ◽  
pp. 1079-1092 ◽  
Author(s):  
Murilo A. Voltarelli ◽  
Rouverson P. da Silva ◽  
Cristiano Zerbato ◽  
Carla S. S. Paixão ◽  
Tiago de O. Tavares
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Grinding Wheel ◽  
Individual Values ◽  
Statistical Process ◽  
Minas Gerais State ◽  
Weighted Moving Average

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

Study on Quality Control Method in Multi-Specification and Small-Batch Production

2014 ◽  
Vol 1006-1007 ◽  
pp. 363-366
Author(s):  
Hai Dong ◽  
Qing Quan Tong ◽  
Yi Kai Wang
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Control Chart ◽  
Control Method ◽  
Quality Characteristics ◽  
Batch Production ◽  
Factors Affecting ◽  
Quality Control Method ◽  
Element Theory

Aiming at the problem of less quality characteristics data in multi-specification and small-batch production, matter-element theory was applied to adjust similarity of factors affecting the quality during the processing, thus similar processes was divided and data shortage problem was resolved. In addition, the relative range method was applied to translate characteristics data, thus drawing the control chart to judge process control state.Through the analysis of a case,the validity of quality control method was verified in the multi-specification and small-batch production.

The Intelligent Quality Control Technology System Based on the Integration Methods of SPC and EPC

2012 ◽  
Vol 263-266 ◽  
pp. 839-842 ◽  
Author(s):  
Zhong Qiu Jiang
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Process ◽  
Control Mode ◽  
Statistical Process ◽  
Technology System ◽  
Quality Control Process ◽  
Dynamic Quality ◽  
And Function

SPC (statistical process control) and EPC (engineering process control) is the scientific methods of quality quality control and quality improvement. It is the difficult problems of quality control process for network manufacturing enterprise how to effectively solve the dynamic quality fluctuation monitoring and the fluctuation abnormal diagnostic analysis and timely process adjustments, this paper designs the intelligent quality control mode and function system architecture, the modes expatiates quality management network based on quality control network of the workshop level and enterprise class network, and researches the integration applications of statistical process control and enterprise ERP quality system, and applies J2EE technology to achieve the system organic combination of design and development.

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