Average Sigma Research Articles

B-131 The fallacy of average sigma levels from mixed sample levels

Abstract Background Sigma measures the number of SDs (z-value) from the existing sample mean to the nearest analytical performance standard or allowable error limit. Authors and software programs often measure sigma metrics for each QC sample but use an average sigma to compare methods and select QC strategies. That practice leads to dramatic over or under-estimation of the number of errors reported and the selection of inappropriate QC strategies. Methods Data samples were created to produce sigma values of 3.0, 4.5, and 6.0. Microsoft Excel function NORMSDIST was used to convert sigma to percent to number of errors per million patients. NORMSINV was used to convert the number of errors per million patients to sigma. Results A. Six sigma represents a method with a failure rate of 0.000001% or 0.001 failures of ASP/TEa per million patients. B. Three sigma represents a method with a failure rate of 0.135 percent or 1,350 failures of ASP/TEa per million patients. C. While the average sigma value of samples A and B was 4.5s, the average error rate was 0.0675 percent or 675 failures of ASP/TEa per million patients. D. An error rate was 0.0675 percent converts with the NORMSINV function to a sigma of 3.21. E. A true 4.5 sigma method would have a failure rate of 0.00034 percent or 3.4 failures of ASP/TEa per million patients. Conclusions Sigma studies that present an average sigma value underestimate the true number of errors reported. It would be more scientifically correct to either report the number of errors reported or to report the average sigma value based on the average number of errors. Laboratory professionals should interpret sigma studies and publications cautiously if a single sigma is used to represent two or more data sets.

A-118 Sigma Metric Performance of Chemistry Assays on 4 Instrument Systems

Abstract Background Chemistry panels play a crucial role in determining a person’s general health and well-being. These tests evaluate electrolyte balance and the status of several major organs. The Comprehensive Metabolic Panel (CMP) and Lipid Panel constitute approximately 87% of the blood tests prescribed in medical laboratories. Laboratories monitor assay performance by performing proficiency testing using quality controls (QC). College of American Pathologists (CAP) data is useful for laboratories to compare their proficiency testing performance to other laboratories and instrument systems. Sigma metrics serve as a valuable tool for laboratories to assess the quality and accuracy of their tests. Sigma scores vary from 0 to 6, with the 6 score designated as world-class performance, 5 as excellent, 4 as good, 3 as marginal, and 2 as poor. The analytical performance of 16 clinical assays from Abbott, Beckman, Roche, and Siemens instrument systems were compared using Sigma metrics. Methods Sigma metrics of 16 chemistry analytes were compared across 4 instruments system using CAP data from three reports (C-A 2023, C-C 2023 and C-B 2022). The 4 systems evaluated were Abbott Alinity ci, Beckman AU, Roche COBAS c500/c303 and Siemens Atellica CH. Method imprecision was estimated for each assay using the cumulative coefficient of variation or standard deviation for each assay at a single QC concentration and calculating the average sigma. Since sigma levels can be concentration-dependent, an equivalent CAP sample concentration was chosen for each instrument system evaluation. Method bias was estimated by evaluating each method QC mean to the peer group mean. Sigma values were calculated for each method as Sigma = (TEa - |Bias|) / Precision using the allowable total error from 2014 Clinical Laboratory Improvement Amendments (CLIA) proposed acceptance limits. Average Sigma values were generated for each analyte and graded as an optimal Sigma score. Results The sigma scores varied across assays and instrument systems. Abbott demonstrated the best overall performance. Specifically, the Abbott Alinity system had the highest number (7/16, 44%) of assays at 5 sigma or higher, with Siemens showing the lowest number of assays at 5 Sigma or higher (1/16, 6%). Of the 16 assays evaluated, Abbott has the fewest assays at or below the 3 sigma threshold (5/16) when compared to Roche (7/16), Beckman (7/16), and Siemens (9/16). Sigma scores were comparable between the Abbott ARCHITCT and Alinity clinical chemistry systems. Conclusions Sigma metrics are useful in assessing the performance of clinical assays. Assays with high Sigma have better performance due to lower imprecision and higher accuracy. None of the instrument systems demonstrated optimal six sigma performance for all analytes. However, Abbott demonstrated the best overall performance.

Quality Control Analysis Using the Six Sigma Approach and Failure Mode and Effect Analysis (Fmea) As an Effort To Reduction Product Defects At CV. Hikmah Jaya Convection

CV. Hikmah Jaya Konveksi is a company operating in the convection sector that serves companies, offices, campuses and schools, both private and government. CV. Hikmah Jaya Konveksi as a company operating in the convection sector was founded on January 26 2011. CV. Hikmah Jaya Konveksi is at the address Griya Kebraon Utara VIII No. 27 Surabaya. The aim of this research is to identify types of product defects, find out the sigma level value for defective products produced and analyze the causes of product defects to provide recommendations for existing problems in the company so that it can reduce the level of defects in products. The method used for this research is Six Sigma with the Define, Measure, Analyze, Improve, Control (DMAIC) stages by calculating the company's sigma value and Failure Mode And Effect Analysis (FMEA) for recommendations for improvements to the problem. Based on the results of data processing and data analysis, 3 defects were obtained in the product, including skipped seams, wrinkled seams and slanted seams with an average sigma value of 4.08. Based on the FMEA analysis, the proposed improvements given to minimize defective products are providing training related to sewing SOPs and conducting evaluations. Leaders and Supervisors are responsible for defective products in their respective areas, conducting briefings for each line before production to clarify duties and responsibilities. respectively, providing lubricating oil to machine parts that have the potential to cause friction which causes noise, carried out periodically every 3 weeks, increasing supervision of all workers so that workers can focus when producing uniforms, checking machine maintenance, making maintenance scheduling regularly periodically every month 3 times.

Analysis of the Factors Causing Delays of Delivery Using the Six Sigma Method (Case Study: Kompindo Wiratama Inc.)

The distribution of goods is a very important part of the service or manufacturing industry. One way to maximize the product distribution process is to maintain the quality of service. Service quality is how a company meets or exceeds customer expectations. The problem experienced by Kompindo Wiratama Inc. occurs because of production target discrepancies resulting in delays due several factors. One method that can be used for reducing delivery delays to improve service quality, namely the method of Six Sigma using DMAIC. The concept of DMAIC starts from a stage that define, measure, analyze, improve, and control and at each stage, D-M-A-I-C has objectives and tools different. Obtained an average DPMO value of 33349.08 and an average sigma value of 3.3691. The sigma level that has been achieved is still far from the Decried target, namely 3.4 DPMO and 6 Sigma. The repair phase uses the 5W + 1H analysis technique based on the source of the problem at the fishbone diagram of each CTQ to explain the proposed results of the analysis. After the results of the proposal are given based on the method Six Sigma improvements in handling the four factors of delivery delays will reduce the occurrence of delivery delays in the following months and years. This can improve the quality of service in delivery and maintain customer trust by the company motto, which is "delivery, quality and zero defect”.

Pengendalian Kualitas Produk Menggunakan Lean Six Sigma dan Fuzzy FMEA Dalam Upaya Menekan Kecacatan Produk

Quality control is a process used to ensure the level of quality in a product or service so as to lead to customer satisfaction. UMKM Eko bubut is a UMKM that produces wooden handicrafts with product marketing that reaches the international market. In the problem of product quality control at UMKM Eko Bubut, it was found that there were five types of defects, namely wavy, rough, perforated, cracked, and broken defects. In addition, it was found that there was wastage that occurred which had an effect on disability. Solving quality control problems at UMKM Eko Bubut is by using lean six sigma to identify the sigma level and fuzzy FMEA is used to determine the main priority for repairs to the causes of production defects that occur. The results of the calculation of the defect level of bowl products, it was found that the types of defects were wavy (55.06%), rough (28.29%), perforated (7.82%), cracked (7.22%) and broken (1.61%). The identification results of the waste that occurs are waste defects (28.1%), waste overproduction (17.7%), waste waiting (16.2%), waste motion (16.0%), waste inventory (10.75%), waste transportation (6.55%), and over-processing (4.9%). The results of calculating the DPMO value obtained an average sigma of 3.27 with a DPMO value of 38890.41. This shows that for every 1,000,000 production, there is a possibility that there will be product defects in as many as 38,890 products. Factors that affect product defects are humans, raw materials, methods, machines, and the environment.

Analisa Pengendalian Kualitas Produk Knob S/A Shift Lever pada PT. Injeksi Plastik Pasifik dengan Metode Six Sigma

Pacific Plastic Injection is a rubber and plastics sector manufacturing company that produces automotive industry components. There are defects in the products produced by PT. Pacific Plastic Injection, namely in Knob S/A Shift Lever products. The purpose of this study is to identify the cause of the defect, obtain sigma level values for Knob S/A Shift Lever products, and provide improvement proposals. The results of this study found types of defects, namely short shot, sink mark, cracking, bubble, weldline, and silver. The factors causing disability come from humans (man) and machines (machine). The DPMO value at the measure stage is 4165.86 which if converted into a sigma level is 4.3. The DPMO value at the measure stage is 4165.86 which if converted into a sigma level is 4.3 This shows that the quality control process at PT. Pacific Plastic Injection is quite good, because it is still within the average Sigma level of USA companies which is at the level of-4 sigma. The improvement proposals provided are providing direction and training for workers regarding the production process, adjusting machines according to procedures, giving directions to workers to follow SOPs, improving production process SOPs, ensuring products have hardened before being released from molding, and checking raw materials before entering the machine.

Quality Control Of Electrical Panel Powder Coating Box Production Using The DMAIC Six Sigma Method at PT DMI Tangerang

PT DMI is a company that manufactures electrical panel boxes. There are still defective products that exceed the company's target on the results of the powder coating process. The results of powder coating at PT DMI through CV XYZ. This study used the Six Sigma method with five stages including Define, Measure, Analyze, Improve, and Control (DMAIC). The results of the define phase of this study showed that there were 3 CTQs in the powder coating results. phase measure the average DPMO value is 4107.496 and the average sigma level is 4.175. the analyze phase found 4 factors of the type of defect including humans, machines, methods and machines. In the improve phase, there are 3 recommendations that have been given, namely making a 5w+1H table, SOP and DOE. the control phase will be carried out by providing this improvement proposal to each company. The results of this study were able to minimize problems that occurred with powder coating results at PT DMI.

ANALISIS PENGEMBANGAN KUALITAS PRODUK KANTONG SEMEN JENIS PASTED KRAFT DENGAN PENDEKATAN SIX SIGMA DI PT. XYZ

The quality development is the most important thing that every company must do to minimize the defective products. One of the methods that can be used to analyze the defective products is Six Sigma which means as a method that can achieve, maintain, and maximize the success of business in a comprehensive and flexible way. The steps to be formulated by implementing the Six Sigma management cycle are DMAIC (Define, Measure, Analyze, Improve and Control). PT. XYZ which is engaged in manufacturing cement bags with one of its products is pasted kraft bags. The purpose of this study was to calculate the sigma score of the current conditions in the company, to analyze the causes of defective product that exceed the company's tolerance standards. The results showed three defective products. Those are printing defects, wrinkled bags and cutting defects. The average sigma score in the production process of Pasted kraft bags was 4.03 sigma, which means that out of 1 million products, there were around 5,858 defective products. The analysis of the core causes of product defects are the poor control of machine component conditions, the less attention of operators to standardize the machine settings, and the worn machine components during the production process. The repair step which is based on the 5W + 1H method are the recommendations for making a form to control the condition of engine components and good SOPs (Standard Operating Procedure), the procurement of field supervisors and regular training to operators.

A-106 Sigma Metric Performance of Chemistries on the Abbott Alinity c Analyzer

Abstract Background The analytical performance of the Abbott Alinity c (Abbott Park, IL) was compared to our current Abbott Architect analyzers using sigma metrics. Methods Sigma metrics of 28 chemistry analytes on our six Abbott Alinity analyzers was compared to three Abbott Architect 16 000 analyzers and one Architect 8000 analyzer using Bio-Rad Multiqual quality control (QC) (Hercules, CA). Method imprecision was estimated for each assay using the cumulative coefficient of variation (CV) of 12 months data for each analyzer at three QC concentrations and calculating the average sigma. Method bias was estimated by comparing each analyzer QC mean to peer group QC means. Sigma values were calculated for each method as (TEa—Bias%)/CV% using allowable total error (TEa) from three sources: the CLIA proficiency testing evaluation limits, Australasian Association of Clinical Biochemistry allowable limits of performance (RCPA quality assurance programs Pty limited, Adelaide, Australia) and desirable biological variation (Ricos C et al, Scand J Clin Lab Invest. 1999, 59(7):491–500). Average sigma values were generated for each analyte and graded as optimal >6 sigma; good 5–6 sigma; marginal 3–5 sigma; or poor <3 sigma. Results Assay sigmas varied across QC concentrations and between analyzers of the same platform. Na, Cl, and CO2 had the lowest sigma performance. Since the sigma calculations are dependent on TEa, tighter TEa limits (Ricos and RCPA) generated lower sigmas compared to the wider CLIA TEa limits especially for Ca, Cl, total bilirubin, CO2, Na, total protein, and HDL. Conclusion None of the chemistries met optimal six sigma performance for all analytes, concentrations of QC and sources of TEa. However, the assay performance between analyzer platforms was comparable.

PROPOSED QUALITY IMPROVEMENT OF ACRYLIC RESIN PRODUCTS WITH SIX SIGMA (AT PT. XYZ)

PT. XYZ is a manufacturing company that produces various types of resins. One of them is acrylic resin, acrylic resin is the raw material for loading solvent base paints such as those used in the automotive paint manufacturing process. In acrylic resin, defects are usually found such as dirty resin, yellow resin color and rust drum. This study aims to determine the value of the sigma level on acrylic resin products before repair, analyze the factors that cause defects in acrylic resin products and provide suggestions for improvements with the Six Sigma approach. So that defects that occur in acrylic products can be reduced or even eliminated, With a six sigma approach in analyzing and calculating data using the DMAIC concept (define, measure, analyze, improve and control), the total acrylic resin defects in the last six months have an average by 8.94% with a type of gross resin defect of 89.88%, drum rust of 95.80% and yellow resin color of 100.00%. While the average sigma value on acrylic resin before repair was 3.15 with DPMO damage of 81,636 for a million productions. Based on the fishbone diagram, the causes of dirty resin defects, yellow resin color, rust drums are from human factors, materials, machines, methods and the environment. Based on the 5W + 1H method, suggestions can be given to companies in the form of periodic checks on machines, making a schedule for cleaning the production area, holding training or training for employees and carrying out supervision from superiors. Keywords: Quality Control, Six Sigma, DMAIC and 5W+1H 1

Efforts To Improve the Quality of Welding Plastic Pallet Type HDX1210R With Six Sigma Method at PT. Pallets

PT. Pallets is a manufacturing company that processes plastic waste into products that have value, namely plastic pallets. A plastic pallet is a flat city-shaped container used as a base for storing and transporting goods in logistics warehousing. This study aims to improve the quality of plastic pallet welding products of the HDX1210R type at PT. Pallets. This study uses the Six Sigma method with the DMAIC (Define - Measure - Analyze - Improve - Control) approach with the help of fishbone diagrams, Pareto diagrams, and FMEA. From the company's historical data from March 2022 to March 2023, 3 types of defects were obtained, with loose welding as the highest type of defect, with a percentage of 89% of the total product defects. The overall DPMO average value is 49908.1405, and the average sigma value is 3.191. From FMEA calculations, the highest RPN value is obtained for frequently changing over machines with a scale value of 480. The proposed improvements can be made by changing the hitter model and size, modifying the mold (pallet molding) by adding the welding area on the pallet and making SOPs for producing HDX1210R-type pallet welding.

Penerapan Six Sigma Dalam Pengendalian Kualitas Produk Mebel Pada PT Alis Jaya Ciptatama

The obstacle faced by PT Alis Jaya Ciptatama is that the production process carried out in 2022 still has many product defects. For this reason, it is necessary to improve the quality of the product. In this case the method used is the Six Sigma method as a quality improvement tool. The results of analysis using Pareto diagrams obtained the highest type of defect with a percentage of 38.2%, and there are three elements that influence this type of defect, namely human, machine and material elements. Through DMAI analysis, the company's average sigma value of 2.70 is still far from the company's expectations of 6, with a Defect Por Million Opportunities (DPMO) of 115362.894. This value is the most critical failure mode so it needs to be repaired. The proposed improvement is by implementing 5S and providing Standard Operating Procedures (SOP) so that operators work in accordance with operational standards in the mill 1 work area. Then a comparison is made between before and having been given the SOP, so the DPMO value is 72,666 and the sigma value is increased by 0.53.

Analisis Peningkatan Kualitas Produk Pot Bunga Dengan Metode Six Sigma Di PT. Sumber Mas Buana Perkasa

PT. Sumber Mas Buana Pekasa is a company engaged in manufacturing that produces various types of plastic-based products, one of the products produced is flower pots which are used as household decorations. The problem being faced is regarding defective products produced during the production process which are less stable. Therefore, quality control is needed which aims to reduce the level of defects by using one method, namely Six Sigma with the stages of define, measure, analyze, improve and control (DMAIC) which can determine the characteristics of defective products. This study aims to determine the type of product defect, the factors causing the defect and provide suggestions for improvement, from the calculation of the average sigma value on the Flowerpot product in July-December 2020 of 3.88 and the DPMO value of 11178. The highest type of defect based on data processing Black Spots 27.2%, Striped 24.7%, Not full 24.6%, and Flashing 23.6%. The repair phase is carried out using the 5W+1H method. Keywords: Quality, Six sigma, DMAIC, DPMO, FMEA, 5W+1H.

IMPROVING THE QUALITY OF THE CHOCOLATE PRODUCTION PROCESS AT WAHANA INTERFOOD NUSANTARA COMPANY USING DMAIC METHOD

In this century, consumers as product users are becoming more critical and picky in their product selection due to technological and economic advances in the global market. Competition in the industrial sector and client satisfaction necessitate constant innovation by companies. A company that prioritises quality rather than quantity will have an advantage in the global market because not all companies can achieve and sustain high levels of quality. Wahana Interfood Nusantara Company is Indonesia's most complete cocoa bean processing company under the SCHOKO brand. The company processes raw cocoa beans to produce high-quality chocolate and cocoa products. One of the products that experienced a defect at the company was the white couverture chocolate product. The problem statement of this study was what types of defects contribute to the poor quality of white couverture chocolate, what factors contribute to the defects, analyse of the data collected during the production process of the product to come up with root causes and identify the optimum solution. This study uses the DMAIC method (Define, Measure, Analyze, Improve, and Control). They obtained 5 Critical to Quality: broken, cracked, peeled-off, soft, and porous. The DPMO value was 4788.49, and the Sigma value was 2.496, which means that the white couverture chocolate production process is in Indonesia's average Sigma value industry. It was found that 80% of the most dominant defects were peeled off (38.5%), cracked (28.7%), and broken (25.3%). Suggestions for improvements are given to carry out regular machine maintenance, conduct employee training, and carry out SOPs correctly.

Six Sigma Implementation on Wash Motor Twin Tub Washing Machine: A Case Study

The competition of electronics and household industry in Indonesia is very tight, thus encouraging the industries to be more creative and innovative to attract customers. PT XYZ as a company that produces washing machines, especially twin tub washing machine, often experiences problems related to the quality of its constituent parts. Part refers to products manufactured by the supplier. One of them is wash motor, which still many of line drops in production line. Based on the calculation of the average DPMO (Defects per Million Opportunities) and the average sigma value in January 2023 were 251.69 and 4.99. This shows that the sigma level value of the company is between levels 4 and 5 which is in the position of the US industry average. The aim of this study is to reduce the number of defects in twin tub washing machine production using Six Sigma method through the Define, Measure, Analyze, Improve, and Control (DMAIC) phases. The results show that there are four dominant types of defects, namely rivet loose, rusty, noise, and no function. Five-M checklist determines suggested improvements based on man, machine, method, material, and environment factors. The Six Sigma approach has the opportunity to be used to support quality control activities to overcome defects that appear on wash motor and to improve product quality in washing machine factories by providing alternative approaches to suppliers.

Enhance quality improvement through lean six sigma in division Side Board Clavinova Piano’s

A lean production system is one of the main factors that every company must have to improve quality, especially reducing waste that occurs in the production line. The musical instrument industry such as Piano manufacturing includes industries that reduce waste by improving the manufacturing system. The company’s strategy to eliminate this waste is to create an effective and efficient work system. The piano production process still has several problems, such as the presence of waste. This is an obstacle in improving the quality of production by reducing the waste that occurs. For this reason, this research focuses on improving the quality of production by reducing waste that occurs, finding the cause of the problem and taking corrective action. The method used in this research is the integration of Lean and Six Sigma and the DMAIC framework. The process is waste flow and production problems are identified in Define phase and residual measurement is measured in Measurement phase. Fishbone diagrams and the application of FMEA are used to analyze the factors that cause problems that occur and prioritize improvements to solve these problems. The implementation of Value Stream Mapping (VSM) is applied to the Enhance of quality improvement phase to reduce waste. The results showed that product quality increased from an average sigma level of 3.53 to 3.79 where overproduction decreased by 41% and Side Board production leadtime decreased by 373 second.

Performance evaluation of routine analytes using six sigma principle in a stand-alone clinical laboratory

Sigma metrics of analytes based on data generated from IQC and EQAS is a reliable way of assessing and improving quality performance in the clinical laboratory. This study was undertaken to assess the sigma metrics of analytes used on a routine basis on a VITROS integrated analyser. Steps to improve quality based on the IQC and EQAS results was done monthly, based on QGI scores. The average sigma metrics at the end of year was compared with subsequent year. Data is presented for this comparison for four consecutive years between April 2017 to March 2021. 75% of analytes (12 out of 16) performed ≥ six sigma level on the current analyser in 2020-21, while 25% to 57% of routine analytes performed ≥ six sigma across published studies cited. Tests used for diagnostics which have high severity risk index such as Sodium, Creatinine, Glucose, Calcium and Uric Acid, all except Sodium had average sigma scores of more than six in VITROS 5600 analyzer used in this laboratory. On comparing performance of these important analytes across studies cited here, it was noticed that creatinine was a differentiator in this study which consistently performed at >6 sigma while in five of six studies cited the sigma score is less than six. In the context of the revised CLIA guidelines in 2019 wherein TE(a) for ten routine analytes have been reduced, it was noticed that with the lowering of TE(a) for many of these analytes, except uric acid, Triglycerides and AST, the performance shifted from > six sigma to a sigma lower than six.

Application of sigma metrics in haematology laboratory

Objectives: Sigma metrics analysis indicates; the level quality control has achieved and how far a given process deviates from perfection. Though the concept is not new to quality and utility, its implementation in the haematology laboratory is still in its nascent stages. The study aimed to compare the utility of sigma metrics as a comprehensive quality control tool in Haematology laboratory against the current quality tools of Internal Quality Control (IQC) and External quality assurance scheme for haematology analyser.Methods: In the present study, IQC data was analysed from June 2021 to September 2021. Control material included 3 batches of second party controls (SPC). Sigma were calculated for five key haematological parameters run in Sysmex XN 350. OPSpecs charts were prepared. Sigma metrics were calculated using the formula Sigma = (TEa – Bias) / CV, Bias was taken from IQC kit insert. Quality goal index was also calculated when needed.Results: Average sigma metrics achieved for selected parameters was, RBC: 8.1, HB: 7.6, HCT 5.6, PLT: 7.0, and WBC: 6.7. The laboratory has achieved excellent to world class performance in all analytes. Sigma QC rules were applied and modifications proposed to the existing internal QC protocols to reduce the number of rejections.Conclusion: Labs must preferentially use the sigma metrics and sigma QC rules to design its IQC protocols, for individual parameters, rather than follow the ‘one fit for all’ recommendation. This would help save precious time, effort, unnecessary control runs and improve efficiency with better focus on quality control, where required.

Grain boundaries with high Σ value and strain in grain matrix induce crack initiation in extruded 6000 series aluminium alloys

The bending properties of 6000 series Al alloys are affected by the precipitates, shear band generation, preferred orientation, and residual stress distribution. Such alloys are characterized by initial cracks of extruded profiles that initiate at depths of 100–200 μm. Studies have used TEM to investigate the effect of precipitates. However, it is also important to evaluate the energetic stability of the grain boundaries and grain matrix around the crack origin at a micro level. To clarify the crack initiation mechanism, we considered the kernel average misorientation (KAM) and sigma (Σ) value at the crack initiation point. In cross sections from the surface region up to a depth of 400 μm, A6061 showed 15% more low-order Σ values of 3, 5, and 7, indicating that it had an energetically stable and strong interface compared to A6005C. The KAM map and Σ value of the surrounding area were obtained using electron backscatter diffraction measurements. The KAM map indicated that the initial crack occurred at the grain boundary with a high strain concentration and high Σ value. STEM-EDS results revealed precipitates in the grain matrix of AlMgSiCu-based Q or Q’ compounds. Their number density and PFZ width were almost the same, and they had only a small effect on crack initiation. However, the morphology of the precipitates in A6061 was smaller than that in A6005C; this may affect the strain distribution in the grain matrix. Thus, we focused on the energetic stability of the grain boundaries and grain matrix and found that cracks initiated at unstable grain boundaries with a large Σ value where strain was concentrated when a tensile force was applied.

Pengendalian Kualitas Produk Tas Di Megatama Group Menggunakan Six Sigma

Business competition in the fashion industry will certainly greatly affect the level of quality that must be achieved by a company. Companies are competing to do many product innovations and improve quality and reduce reject products so that companies achieve the expected benefits or profits. If the company cannot control the quality properly, it can be imagined that the reject product will be high and this will certainly harm the company, not to mention the availability of uncertain materials can also be one of the factors hindering production. In one production of 5000 products there are 100 defective products that must be repaired. Defective products occur mostly in manual processes, namely in the gluing process. This research was conducted to find out related to quality control to reduce rejects that have been happening so far. The method used is Six Sigma DMAIC. Based on the calculation of the average DPMO value and the average sigma value of 3.7 with the possibility of damage to the production of 12,000 pcs when producing 1,000,000 pcs of bags.