scholarly journals Pengendalian Kualitas untuk Meminimalkan Jumlah Cacat pada Produk Kaleng Aeorosol

2012 ◽  
Vol 3 (1) ◽  
pp. 584
Author(s):  
Rida Zuraida ◽  
Bima Rantautama ◽  
Notri Sutrisnohadi ◽  
Chondro Dewo Adi Pratomo
Keyword(s):  
Quality Control ◽  
Data Processing ◽  
Manufacturing Process ◽  
Process Capability ◽  
Statistical Quality Control ◽  
Welding Wire ◽  
Effect Analysis ◽  
Aerosol Cans ◽  
Fmea Method

2.64% is the largest defect percentage of aerosol can product of PT Multi Makmur Indah Industri. To improve the product quality, the study focused on the goal of reducing the percentage of defects using the Statistical Quality Control. After gaining control of the company, we can calculate the process capability in the aerosol can manufacture. The next step is identification of the defects causes that arise using the failure mode and effect analysis (FMEA) method to measure the defect causes risks and as an input in determining control recommendations. From the observations and data processing, it is recognized that the overall phase of the process has a process capability value above 99%. 80% of defects in the aerosol cans product occur during the process of can making, component making and printing. While the most risking defect cause is the destruction ofmachine B coating on the printing process (RPN = 245) and the quality of the welding wire on can manufacturing process (RPN = 160). Therefore, the solution to reduce the defect percentage is replacing the damaged coating machine B as well as upgrading the quality of the welding wire. Thus, it can reduce the losssale as much as 1.06% (Rp110,716,000) per month.

scholarly journals PROCESS CAPABILITY IMPROVEMENT BY PUTTING ‘STATISTICAL PROCESS CONTROL’ INTO PRACTICE

2013 ◽  
pp. 251-256
Author(s):  
PIYUSH KUMAR SONI ◽  
IMTIYAZ KHAN ◽  
ABHISHEK ROHILLA
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Product Quality ◽  
Statistical Process Control ◽  
Manufacturing Process ◽  
Process Capability ◽  
Statistical Process ◽  
Quality Of Products ◽  
Capability Improvement

Quality control helps industries in improvement of its product quality and productivity. Statistical Process Control (SPC) is one of the tools to control the quality of products that practice in bringing a manufacturing process under control. In this paper, the process control of a CNC Grinder manufactured at PMT Machines Ltd. Halol, (Gujarat) India is discussed. The varying measurements have been recorded for a number of samples of a Cam Roller Shoe obtained from a number of trials with the CNC Grinder. SPC technique has been adopted, by which the process is finally brought under control and process capability is improved.

Statistical Quality Control

1945 ◽  
Vol 5 (04) ◽  
pp. 207-217
Author(s):  
H. C. H. Carpenter
Keyword(s):  
Quality Control ◽  
Statistical Methods ◽  
Mass Production ◽  
Manufacturing Process ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
The Subject

The actuary's ideas on statistics tend to be biased in the direction of Life Office statistics. It may therefore be useful to set down a simple use of statistical methods which has been adopted in industry and which was the subject of a lecture to the Students' Society some two years ago, viz. Statistical Quality Control. Where the mass production of articles is employed, various systems of inspection aim at watching the quality of the product to prevent unsatisfactory work being passed on to the consumer or to a later stage in the manufacturing process.

scholarly journals PENERAPAN STATISTICAL QUALITY CONTROL (SQC) PADA PENGENDALIAN MUTU MINYAK TELON (STUDI KASUS DI PT.X)

AGROINTEK ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 72
Author(s):  
Andan Linggar Rucitra ◽  
S Fadiah
Keyword(s):  
Quality Control ◽  
Raw Materials ◽  
Control Process ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Quality Control Process ◽  
Fishbone Diagram ◽  
Potential Factor ◽  
Measurement Of Success

<p><em>Telon oil is</em><em> one of </em><em> </em><em>the </em><em>traditional medicine in the form of </em><em> </em><em>liquid preparations that serves to provide a sense of warmth to the wearer. PT</em><em>.X</em><em> is one of the companies that produce</em><em> </em><em>telon</em><em> oil</em><em>.</em><em> To maintain</em><em> the quality of telon oil from PT.X</em><em> product</em><em>, required overall quality control that is starting from the quality control of raw materials, quality control process to the quality control of the final product. The purpose of this research is to know the application of Statistical Quality Control (SQC) in controlling the quality of telon oil in PT X. </em><em>F</em><em>inal product</em><em> quality</em><em> become one of the measurement of success of a process, so it needs a good quality control. SQC method used in this research is Pareto Diagram and Cause and Effect Diagram. Pareto diagram is a bar graph </em><em>that </em><em>show the problem based on the order of the number of occurrences of the most number of problems until the least happened. A causal diagram is often called a fishbone diagram, a tool for identifying potential causes of an effect or problem. The result of applying the method indicates that 80% defect is caused by unsuitable volume and on the incompatibility of Expired Date (ED) code. The damage is caused by several factors namely the method, labor, and machine while the most potential factor is the volume conformity to reduce the number of defect products.</em></p>

A Paired Comparison Approach to Evaluating Interviewer Performance

1969 ◽  
Vol 6 (1) ◽  
pp. 66-70
Author(s):  
Purnell H. Benson
Keyword(s):  
Quality Control ◽  
Data Processing ◽  
Computer Program ◽  
Paired Comparison ◽  
Statistical Quality Control ◽  
Comparison Analysis ◽  
Statistical Quality ◽  
Comparison Approach

Paired comparison analysis, as developed in psychometric work, is applied to the problem of statistical quality control of interviewing. Data from telephone interviewing are analyzed. Details are given for preparing a computer program for data processing.

scholarly journals Application of FMEA in the Quality Estimation of Metal Matrix Composite Castings Produced by Squeeze Infiltration

2017 ◽  
Vol 62 (4) ◽  
pp. 2171-2182 ◽  
Author(s):  
K. Gawdzińska ◽  
L. Chybowski ◽  
W. Przetakiewicz ◽  
R. Laskowski
Keyword(s):  
Metal Matrix Composite ◽  
Metal Matrix ◽  
Metal Composite ◽  
Matrix Composites ◽  
Quality Estimation ◽  
Effect Analysis ◽  
Squeeze Infiltration ◽  
Composite Casting ◽  
Fmea Method

AbstractMetal matrix composites (MMCs) are still scarcely described due to various combinations of used materials and a wide array of technologies. Applying the Failure Mode and Effect Analysis (FMEA) method to describe the quality of metal composite castings may contribute to eliminating specific (characteristic only to these materials) defects. This part of the analysis determines the criticality numbers, meaning the frequency of a given failure, detectability level and significance of a given failure to the group of specific composite casting failures. It contributes to establishing the priority number (P), which is a measure used to assess risk, a notion essential in discussing quality in a composite casting.

Quality Control Model for Manufacturing Process Based on GQMM

2011 ◽  
Vol 214 ◽  
pp. 612-617 ◽  
Author(s):  
Zhong Hua Yu ◽  
Juan Zhou
Keyword(s):  
Quality Control ◽  
Manufacturing Process ◽  
Manufacturing System ◽  
Control Model ◽  
Formal Description ◽  
Improve Quality ◽  
Knowledge Based ◽  
Structured Analysis ◽  
Goal Question Metric

A structured quality control model was proposed. It was a multilevel and knowledge-based quality control model which took Goal, Question, Metric, Measure and other fundamental elements as the main line, structured principle as guidance, manufacturing process as carrier, and improve quality of manufacturing system as destination. Firstly, a structured analysis principle was introduced. And then a structured quality control model was proposed combining bearing manufacturing process. A formal description of mappings between Goal-Question-Metric-Measure (GQMM) was also discussed. Finally; the architecture of the model was presented.

scholarly journals Analisis Kesehatan Dan Keselamatan Kerja Menggunakan Failure Mode And Effect Analysis Di PT. XYZ

2021 ◽  
Vol 23 (1) ◽  
pp. 46-58
Author(s):  
Muhammad Ramadan ◽  
Sukanta Sukanta ◽  
Risma Fitriani
Keyword(s):  
Occupational Health ◽  
Production Process ◽  
Health And Safety ◽  
Occupational Health And Safety ◽  
Risk Priority Number ◽  
Effect Analysis ◽  
Fmea Method ◽  
Prime Condition ◽  
A Company

Occupational Health and Safety (OHS) is important to maintain and improve so that the quality of human resources in the company is always in prime condition. Every company has a different level or level of Occupational Health and Safety (OHS). PT. XYZ is a company that uses corrosive materials, therefore Occupational Health and Safety (OHS) analyst at PT XYZ is needed. The Occupational Health and Safety (OHS) analysis carried out in this study used the FMEA method to determine which part of the production process had Occupational Health and Safety (OHS) that needed repair the most. From the research results, it was found that the highest Risk Priority Number (RPN) value was the pickling and degreasing production process.

scholarly journals Smart Manufacturing: Quality Control Perspectives

2021 ◽  
Author(s):  
Joseph Evans Agolla
Keyword(s):  
Quality Control ◽  
Manufacturing Process ◽  
Quality Criteria ◽  
Smart Manufacturing ◽  
Physical Conditions ◽  
Manufacturing Operations ◽  
Accuracy And Precision ◽  
Manufactured Products ◽  
Data Analytic

Quality Control (QC) is a guideline or set of procedures intended to ensure that a manufactured product or performed service adheres to a defined set of quality criteria or meets the requirements of the client or customer. Smart manufacturing is where the work is interfaced work pieces and associated tools that include logistics operations, Cyber Physical Systems, Artificial Intelligence, and Big Data Analytic tools. These form the norm of manufacturing operations to generate large amounts of data, which are used for analysis and prediction. Therefore, help to optimise the quality of manufacturing operations and manufactured products. The change in technologies have, however, altered the traditional way of manufacturing process as well as QC systems. Therefore, to address the challenge of data reliability, the sensors, actuators and instruments used at various levels of integration in the manufacturing process often operating under adverse physical conditions need to provide adequate levels of data accuracy and precision. Methodologically, the Chapter followed critical literature review on QC concepts and Industry 4.0 revolution, thereby culminating into conceptual framework of QC in Smart Manufacturing, which is the main contribution of this Chapter.

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