scholarly journals Quality Control of Metaxa Cognac

Proceedings ◽  
2020 ◽  
Vol 55 (1) ◽  
pp. 15
Author(s):  
Andreea Donise ◽  
Gabriela Nita ◽  
Mihaela Emanuela Craciun ◽  
Mihaela Mihai
Keyword(s):  
Quality Control ◽  
Customer Satisfaction ◽  
Control Charts ◽  
Reliable Control ◽  
Control Diagram ◽  
Control Limits ◽  
Degree Of Filling

Quality represents all the characteristics and features of a product or service that satisfies individual requirements. In other words, quality is measured by the degree of customer satisfaction regarding a product or service. The implementation of the control diagram is studied at the distribution store reception of Metaxa cognac orders. The delivery was performed in boxes of six bottles. The control charts follow the degree of filling of the bottles. The volume of filling was measured on a batch of 36 bottles to establish reliable control limits. The studied sample comprised 36 bottles distributed in six boxes.

Comparison Between the Economic-Statistical Design of Double and Triple Sampling X ¯ \bar{X} Control Charts

2017 ◽  
Vol 32 (1) ◽  
Author(s):  
Azamsadat Iziy ◽  
Bahram Sadeghpour Gildeh ◽  
Ehsan Monabbati
Keyword(s):  
Optimal Control ◽  
Quality Control ◽  
Control Chart ◽  
Control Charts ◽  
Statistical Design ◽  
Sampling Interval ◽  
Point Of View ◽  
Double Sampling ◽  
Control Limits

AbstractControl charts have been established as major tools for quality control and improvement in industry. Therefore, it is always required to consider an appropriate design of a control chart from an economical point of view before using the chart. The economic design of a control chart refers to the determination of three optimal control chart parameters: sample size, the sampling interval, and the control limits coefficient. In this article, the double sampling (DS)

scholarly journals Construction of Fuzzy Mean using Standard Deviation ( X  S  ) Control Chart with Process Capability

2019 ◽  
Vol 8 (4) ◽  
pp. 5390-5396
Keyword(s):  
Quality Control ◽  
Standard Deviation ◽  
Set Theory ◽  
Control Chart ◽  
Control Charts ◽  
Total Quality ◽  
Statistical Process ◽  
Research Article ◽  
Fuzzy Mean ◽  
Control Limits

The Quality has established over a number of points such as inspection, quality control, quality assurance, and total quality control and the effects produced by the above phases are used to check and develop the production/service procedure. Statistical process control (SPC) is a powerful collection of problem solving tools valuable in attaining process steadiness and enlightening capability through the decline of variability. Fuzzy set theory is a utilitarian tool to succeed the uncertainty environmental circumstances and the Fuzzy control limits provide a more accurate and flexible rating than the traditional control charts. The purpose of this research article is to construct the fuzzy mean using standard deviation ( X S   ) control chart with the assistance of process capabilityThe Quality has established over a number of points such as inspection, quality control, quality assurance, and total quality control and the effects produced by the above phases are used to check and develop the production/service procedure. Statistical process control (SPC) is a powerful collection of problem solving tools valuable in attaining process steadiness and enlightening capability through the decline of variability. Fuzzy set theory is a utilitarian tool to succeed the uncertainty environmental circumstances and the Fuzzy control limits provide a more accurate and flexible rating than the traditional control charts. The purpose of this research article is to construct the fuzzy mean using standard deviation ( X S   ) control chart with the assistance of process capability

scholarly journals Performance of Moving Average (MA) Chart Under Three Delta Control Limits and Six Delta Initiatives

2019 ◽  
Vol 7 (2) ◽  
pp. 157-160
Author(s):  
Kalpesh S Tailor
Keyword(s):  
Quality Control ◽  
Normal Distribution ◽  
Six Sigma ◽  
Control Charts ◽  
Moving Average ◽  
Mean Deviation ◽  
Telephone Company ◽  
Graphical Tool ◽  
Control Limits

An SQC chart is a graphical tool for representation of the data for knowing the extent of variations from the expected standard. This technique was first suggested by W.A. Shewhart of Bell Telephone Company based on 3σ limits. M. Harry, the engineer of Motorola has introduced the concept of six sigma in 1980. In 6σ limits, it is presumed to attain 3.4 or less number of defects per million of opportunities. Naik V.D and Desai J.M proposed an alternative of normal distribution, which is named as moderate distribution. The parameters of this distribution are mean and mean deviation. Naik V.D and Tailor K.S. have suggested the concept of 3-delta control limits and developed various control charts based on this distribution. Using these concepts, control limits based on 6-delta is suggested in this paper. Also the moving average chart is studied by using 6-delta methodology. A ready available table for mean deviation is prepared for the quality control experts for taking fast actions. 

scholarly journals Comparison of Some Non-Parametric Quality Control Methods

2021 ◽  
Vol 27 (130) ◽  
pp. 197-209
Author(s):  
Hiba Mustafa Fawzi ◽  
Asmaa Ghalib Jaber
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Control Methods ◽  
K Nearest Neighbor ◽  
Signed Rank ◽  
Control Limits ◽  
Non Parametric

    Multivariate Non-Parametric control charts were used to monitoring the data that generated by using the simulation, whether they are within control limits or not. Since that non-parametric methods do not require any assumptions about the distribution of the data.  This research aims to apply the multivariate non-parametric quality control methods, which are Multivariate Wilcoxon Signed-Rank ( ) , kernel principal component analysis (KPCA) and k-nearest neighbor ( −

Thresholds for Safety Inspection Measurements Based on Control Charts

1997 ◽  
Vol 04 (02) ◽  
pp. 205-226 ◽  
Author(s):  
Andrew Y. Cheng ◽  
Regina Y. Liu ◽  
James T. Luxhøj
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Average Run Length ◽  
Federal Aviation Administration ◽  
Performance Measure ◽  
Statistical Quality Control ◽  
Traffic Density ◽  
Inspection System ◽  
Air Carriers ◽  
Control Limits

The rapid growth of air traffic density has long demanded the Federal Aviation Administration (FAA) to design an effective safety inspection system. Well defined thresholds are essential to such an inspection system since they provide standards for both monitoring and regulating purposes. In this paper, we use control chart techniques to derive thresholds and standards for inspection measures, and to provide charts for monitoring them continuously. These thresholds charts play the same role as control charts do in statistical quality control for the monitoring of manufacturing processes. In quality control, the centerline of the chart indicates the target value and the control limits determine whether the process is out of control. In safety inspections, we view the centerline as the safety standard, and justify some properly chosen levels of control limits as meaningful thresholds. For FAA safety inspection surveillance results concerning air carriers, these thresholds are termed alert, advisory, expected, and informational. They provide a concrete measure of the inspection results in terms of the severity of potential flaws, and serve as a guideline for the general rating of the safety performance of each carrier. Furthermore, we can now use the so-called average run length to measure the effectiveness of the inspection system with the proposed thresholds. This approach is implemented on a dataset of an operational performance measure collected from ten air carriers over a period of 6 months. The results are very supportive.

The Inertial Properties of Quality Control Charts

Technometrics ◽  
2005 ◽  
Vol 47 (4) ◽  
pp. 425-436 ◽  
Author(s):  
William H Woodall ◽  
Mahmoud A Mahmoud
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Quality Control Charts

scholarly journals On Performance of Two-Parameter Gompertz-Based X¯ Control Charts

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Johnson A. Adewara ◽  
Kayode S. Adekeye ◽  
Olubisi L. Aako
Keyword(s):  
Control Chart ◽  
Coverage Probability ◽  
Control Charts ◽  
Average Run Length ◽  
Real Life ◽  
Correction Method ◽  
Gompertz Distribution ◽  
Real Life Data ◽  
Control Limits ◽  
Two Parameter

In this paper, two methods of control chart were proposed to monitor the process based on the two-parameter Gompertz distribution. The proposed methods are the Gompertz Shewhart approach and Gompertz skewness correction method. A simulation study was conducted to compare the performance of the proposed chart with that of the skewness correction approach for various sample sizes. Furthermore, real-life data on thickness of paint on refrigerators which are nonnormal data that have attributes of a Gompertz distribution were used to illustrate the proposed control chart. The coverage probability (CP), control limit interval (CLI), and average run length (ARL) were used to measure the performance of the two methods. It was found that the Gompertz exact method where the control limits are calculated through the percentiles of the underline distribution has the highest coverage probability, while the Gompertz Shewhart approach and Gompertz skewness correction method have the least CLI and ARL. Hence, the two-parameter Gompertz-based methods would detect out-of-control faster for Gompertz-based X¯ charts.

scholarly journals Impact of Variations in Test Method Parameters onIn VitroActivity of Surotomycin against Clostridium difficile and Surotomycin Quality Control Limits for Broth Microdilution and Agar Dilution Susceptibility Testing

2015 ◽  
Vol 54 (3) ◽  
pp. 749-753 ◽  
Author(s):  
Maria M. Traczewski ◽  
Jennifer Deane ◽  
Daniel Sahm ◽  
Steven D. Brown ◽  
Laurent Chesnel
Keyword(s):  
Quality Control ◽  
Clostridium Difficile ◽  
Calcium Concentration ◽  
Susceptibility Testing ◽  
Test Method ◽  
Test Parameter ◽  
Content Type ◽  
Parameter Variations ◽  
Agar Dilution ◽  
Control Limits

Test parameter variations were evaluated for their effects on surotomycin MICs. Calcium concentration was the only variable that influenced MICs; therefore, 50 μg/ml (standard for lipopeptide testing) is recommended. Quality control ranges forClostridium difficile(0.12 to 1 μg/ml) andEggerthella lenta(broth, 1 to 4 μg/ml; agar, 1 to 8 μg/ml) were approved by the Clinical and Laboratory Standards Institute based on these data.

scholarly journals Fuzzy x- and s Control Charts: A Data-Adaptability and Human-Acceptance Approach

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Ming-Hung Shu ◽  
Dinh-Chien Dang ◽  
Thanh-Lam Nguyen ◽  
Bi-Min Hsu ◽  
Ngoc-Son Phan
Keyword(s):  
Surface Roughness ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Control Surface ◽  
Process Conditions ◽  
Fuzzy Data ◽  
Size Condition ◽  
Resolution Identity ◽  
And Control ◽  
Control Limits

For sequentially monitoring and controlling average and variability of an online manufacturing process, x¯ and s control charts are widely utilized tools, whose constructions require the data to be real (precise) numbers. However, many quality characteristics in practice, such as surface roughness of optical lenses, have been long recorded as fuzzy data, in which the traditional x¯ and s charts have manifested some inaccessibility. Therefore, for well accommodating this fuzzy-data domain, this paper integrates fuzzy set theories to establish the fuzzy charts under a general variable-sample-size condition. First, the resolution-identity principle is exerted to erect the sample-statistics’ and control-limits’ fuzzy numbers (SSFNs and CLFNs), where the sample fuzzy data are unified and aggregated through statistical and nonlinear-programming manipulations. Then, the fuzzy-number ranking approach based on left and right integral index is brought to differentiate magnitude of fuzzy numbers and compare SSFNs and CLFNs pairwise. Thirdly, the fuzzy-logic alike reasoning is enacted to categorize process conditions with intermittent classifications between in control and out of control. Finally, a realistic example to control surface roughness on the turning process in producing optical lenses is illustrated to demonstrate their data-adaptability and human-acceptance of those integrated methodologies under fuzzy-data environments.

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