scholarly journals Business Process Re-Engineering to Digitalise Quality Control Checks for Reducing Physical Waste and Resource Use in a Food Company

2021 ◽  
Vol 13 (22) ◽  
pp. 12341
Author(s):  
Guillermo Garcia-Garcia ◽  
Guy Coulthard ◽  
Sandeep Jagtap ◽  
Mohamed Afy-Shararah ◽  
John Patsavellas ◽  
...  
Keyword(s):  
Quality Control ◽  
Business Process ◽  
Lean Manufacturing ◽  
Industry 4.0 ◽  
Control Process ◽  
Essential Element ◽  
Additional Information ◽  
Food Company ◽  
Quality Control Process ◽  
The Right

Quality control is an essential element of manufacturing operations that reduces product defects and provides excellent products of the right specifications to the end consumer. Industry 4.0 solutions, such as digitalisation, along with lean manufacturing tools, may support quality control operations. This paper presents a case study of a food company wherein quality control checks were optimised using business process re-engineering to reduce physical waste and resource usage. Following close analysis of the company’s pack-house operations, it was proposed to adopt elements of Industry 4.0 by digitalising the quality control process. Implementing such a solution led to a reduction in the time needed to complete recorded checks, an increase in the time the pack-house quality control team spends with packers on the production lines, and the facilitation of defects identification. It also ensured that the product met the customers’ specifications and reduced the likelihood of rejection at the customers’ depot. The new system also enabled monitoring of each line in real-time and gathering of additional information faster and more accurately. This article proves how employing lean principles in combination with Industry 4.0 technologies can lead to savings in resources and a reduction in waste, which leads to improvements in operational efficiency.

RANCANGAN PERBAIKAN PENGENDALIAN KUALITAS PADA PRODUK BARU DENGAN PENDEKATAN SIX SIGMA

2021 ◽  
Vol 23 (1) ◽  
pp. 50-69
Author(s):  
Kholid Ramadhani
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Qualitative Methodology ◽  
Control Process ◽  
Spare Part ◽  
Root Cause ◽  
Quality Control Process ◽  
Other Information ◽  
The Right ◽  
Document Review

Abstract. This study was aimed to identify cause of defect that occurred in newproducts at automotive - spare part company by designing the right qualitymanagement system with root cause analysis. Qualitative methodology was used,where data and other information collected by interviews, direct observation,document review, and evaluation of reports related to quality achievement. Thecompletion phase of this study was used the DMAIC concept as a corrective action andcontinuous improvement for quality achievement. From analysis result, the factors thatcause defects in new products are differentiated by 11 defect categories. Thedominant defect category was ‘no taping option’ and TBO. The proposed qualitycontrol design to reduce the number of defects are by adding more specific trainingfor new operators according to their own work, adding quality control process byQC after the sub- assembling process, replace all opposite jigs with push pull jigand adding verification tools and other production facilities by the QA-QC departmentKeywords: Quality control system, Six Sigma, DMAIC, defect

scholarly journals Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3987 ◽  
Author(s):  
Javier Villalba-Diez ◽  
Daniel Schmidt ◽  
Roman Gevers ◽  
Joaquín Ordieres-Meré ◽  
Martin Buchwitz ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Control Process ◽  
Optical Quality ◽  
Soft Sensor ◽  
Training Data ◽  
Printing Industry ◽  
Quality Control Process

Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a high-resolution optical quality control camera to increase the accuracy and reduce the cost of an industrial visual inspection process in the Printing Industry 4.0. During the process of producing gravure cylinders, mistakes like holes in the printing cylinder are inevitable. In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface to the used engraving file and performs an automatic quality control process by learning features through exposure to training data. The DNN sensor developed achieved a fully automated classification accuracy rate of 98.4%. Further research aims to use these results to three ends. Firstly, to predict the amount of errors a cylinder has, to further support the human operation by showing the error probability to the operator, and finally to decide autonomously about product quality without human involvement.

Analysis of the information management system in the manufacturing process of cigarette enterprises using fuzzy AHP

2021 ◽  
pp. 1-11
Author(s):  
Song Gang ◽  
Wang Xiaoming ◽  
Wu Junfeng ◽  
Li Shufang ◽  
Liu Zhuowen ◽  
...  
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Information Management ◽  
Manufacturing Process ◽  
Management System ◽  
Fuzzy Ahp ◽  
Control Process ◽  
Information Management System ◽  
Manufacturing Execution System ◽  
Quality Control Process

In view of the production quality management of filter rods in the manufacturing and execution process of cigarette enterprises, this paper analyzes the necessity of implementing the manufacturing execution system (MES) in the production process of filter rods. In this paper, the filter rod quality system of cigarette enterprise based on MES is fully studied, and the constructive information management system demand analysis, cigarette quality control process, system function module design, implementation and test effect are given. This paper utilizes the Fuzzy analytic hierarchy process to find the optimal system for processing the manufacturing of cigarette. The implementation of MSE based filter rod quality information management system for a cigarette enterprise ensures the quality control in the cigarette production process. Through visualization, real-time and dynamic way, the information management of cigarette production is completed, which greatly improves the quality of cigarette enterprise manufacturing process.

Mitocytosis, a migrasome-mediated mitochondrial quality-control process

Cell ◽  
2021 ◽  
Vol 184 (11) ◽  
pp. 2896-2910.e13
Author(s):  
Haifeng Jiao ◽  
Dong Jiang ◽  
Xiaoyu Hu ◽  
Wanqing Du ◽  
Liangliang Ji ◽  
...  
Keyword(s):  
Quality Control ◽  
Control Process ◽  
Mitochondrial Quality Control ◽  
Quality Control Process

scholarly journals A Two-Stage Quality Control Method for 2-m Temperature Observations Using Biweight Means and a Progressive EOF Analysis

2013 ◽  
Vol 141 (2) ◽  
pp. 798-808 ◽  
Author(s):  
Zhifang Xu ◽  
Yi Wang ◽  
Guangzhou Fan
Keyword(s):  
Quality Control ◽  
Control Method ◽  
Control Process ◽  
Lapse Rate ◽  
Systematic Bias ◽  
Eof Analysis ◽  
Two Stage ◽  
Quality Control Method ◽  
Quality Control Process ◽  
The Difference

Abstract The relatively smooth terrain embedded in the numerical model creates an elevation difference against the actual terrain, which in turn makes the quality control of 2-m temperature difficult when forecast or analysis fields are utilized in the process. In this paper, a two-stage quality control method is proposed to address the quality control of 2-m temperature, using biweight means and a progressive EOF analysis. The study is made to improve the quality control of the observed 2-m temperature collected by China and its neighboring areas, based on the 6-h T639 analysis from December 2009 to February 2010. Results show that the proposed two-stage quality control method can secure the needed quality control better, compared with a regular EOF quality control process. The new method is, in particular, able to remove the data that are dotted with consecutive errors but showing small fluctuations. Meanwhile, compared with the lapse rate of temperature method, the biweight mean method is able to remove the systematic bias generated by the model. It turns out that such methods make the distributions of observation increments (the difference between observation and background) more Gaussian-like, which ensures the data quality after the quality control.

scholarly journals Blockchain in manufacturing quality control: A computer simulation study

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247925
Author(s):  
Pooi-Mun Wong ◽  
Shreya R. K. Sinha ◽  
Chee-Kong Chui
Keyword(s):  
Quality Control ◽  
Data Security ◽  
Control Process ◽  
Production Quality ◽  
Consensus Algorithm ◽  
Defect Tolerance ◽  
Production Quality Control ◽  
Peer Supervision ◽  
Quality Control Process ◽  
Quality Controller

Blockchain has been applied to quality control in manufacturing, but the problems of false defect detections and lack of data transparency remain. This paper proposes a framework, Blockchain Quality Controller (BCQC), to overcome these limitations while fortifying data security. BCQC utilizes blockchain and Internet-of-Things to form a peer-to-peer supervision network. This paper also proposes a consensus algorithm, Quality Defect Tolerance (QDT), to adopt blockchain for during-production quality control. Simulation results show that BCQC enhances data security and improves defect detections. Although the time taken for the quality control process increases with the number of nodes in blockchain, the application of QDT allows multiple inspections on a workpiece to be consolidated at a faster pace, effectively speeding up the entire quality control process. The BCQC and QDT can improve the quality of parts produced for mass personalization manufacturing.

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>

scholarly journals Directions and methods of improving quality control processes for weapons system software

2016 ◽  
pp. 21-31
Author(s):  
A. V. Samonov ◽  
S. A. Sharov
Keyword(s):  
Quality Control ◽  
Software Quality ◽  
Control Process ◽  
System Software ◽  
Life Cycle Model ◽  
Cycle Model ◽  
Software Life Cycle ◽  
Technological Basis ◽  
Quality Control Process ◽  
Automated Technique

The work looks at directions of developing regulatory and procedural and technological basis of weapons system software control and quality assurance processes. We present an improved software life cycle model and describe an automated technique of implementing a systemic end-to-end software quality control process.

scholarly journals Analisis Proses Pengendalian Mutu dalam Pelaksanaan Standar Pelayanan Minimal (SPM) di Instalasi Farmasi RS Keluarga Sehat

2017 ◽  
Vol 5 (1) ◽  
pp. 35-42
Author(s):  
LIa Alfa Rosida ◽  
Sudiro Sudiro
Keyword(s):  
Quality Control ◽  
Performance Appraisal ◽  
Control Process ◽  
Control Group ◽  
Pharmacy Staff ◽  
Quality Performance ◽  
Quality Control Process ◽  
Performance Report ◽  
Report Data ◽  
And Performance

The indicator achievement of minimum service standard (MSS) for the accuracy of distributingdrugs in Pharmacy Unit of Keluarga Sehat Hospital has not been achieved, even the incidence ofthe distributionerror from 2013 to 2016 continues to increase. The purpose of this researchwas to analyze the quality control process in the implementation of MSS in Pharmacy Unit of Keluarga Sehat Hospital. This was a qualitative research, with research subject 3pharmacy officers and 3 people of pharmacy management services.Data collectedby in-depth interview and observation of pharmaceutical performance report data and analysed by content analysis. The result of the research showed that the evaluation of pharmacy staff performance has not been implemented, because there is no performance appraisal indicator yet. Comparison was done only by comparing reports with general target, medical support manager double job resulted in no feedback to Pharmacy Unit, and so it has not supported the implementation of MSS. The Improvement of performancehas not been implementedand has notfound the concept of improvement. The new management will attempt to conduct a comparative study, including pharmacy installation into the Quality Control Group (GKM) or Problem Solving for Better Health (PSBH), find the cause of the problem and develop the policy. It can be concluded that the quality control of the MSS in the Pharmacy Unit still not going well and need to be improved especially related to quality performance appraisal and performance improvement based on the SOP.

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