Quality Variation Minimizer: A New Approach for Quality Improvement in Textile Industry

Amin, Muhammad; Amanullah, M. Mohamed; Akbar, Atif

doi:10.52763/pjsir.phys.sci.59.2.2016.109.113

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Sometimes, the preferences and tastes of consumers are enough for them to have an inclination for certain types of products. On the other hand, consumer purchasing decisions are supported by the good or bad experiences they have had when acquiring a garment and the opinions of friends and relatives around them (Amin, Amanullah, & Akbar, 2016).…”

Section: Preferences Of the Ecuadorian Textile Marketmentioning

confidence: 99%

Statistical Analysis of Manufacturing, Trade and Service in the Textile Industry in Ecuador, 2000-2020

Bravo¹

2022

JSS

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The aim of this scientific contribution is to show a statistical analysis of the manufacturing, trade and services of the textile industry in Ecuador, between the years 2000 and 2020. To achieve this objective, an exhaustive study of the Scopus international database and of the Ecuadorian government database called the National Institute of Statistics and Censuses (by its acronym in Spanish INEC). This research provided the following lines of results: 1) Bibliometric study of the textile industry in Ecuador; 2) Statistical study of the textile industry in Ecuador; 3) Preferences of the Ecuadorian textile market. The fundamental conclusion of this study is that: Manufacturing activities have 11,006 establishments, of which 74.2% correspond to the manufacture of clothing, except leather clothing. Total employed personnel in activities related to the textile sector amounts to 115,937 people, of which 68,215 are women and 47,722 men. In the last 11 years, the textile industry has maintained an average share of between 1% and 2% in total GDP, according to data from the Central Bank of Ecuador.

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Section: Preferences Of the Ecuadorian Textile Marketmentioning

confidence: 99%

Statistical Analysis of Manufacturing, Trade and Service in the Textile Industry in Ecuador, 2000-2020

Bravo¹

2022

JSS

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GLM‐based control charts for the inverse Gaussian distributed response variable

Kinat

Amin

Mahmood

2019

Quality & Reliability Eng

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In the modern era of digitalization, manufacturing industries needed monitoring methods to timely detect an abrupt change in the process. Control charts are widely used online monitoring method and used in several sectors for the surveillance of the process. Usually, control charts are developed for a single study variable, but there exists auxiliary information along with the study variable. Because of the linear relation between the study variable and auxiliary variable, several control chart studies are designed based on the simple linear regression model, but they are restricted to the normally distributed response variable. When the response variable follows an exponential family distribution, then the generalized linear modeling (GLM) approach provides better estimates. Hence, this study is designed to propose GLM‐based control charts when the response variable follows the inverse Gaussian (IG) distribution. In GLM‐based control charts, deviance and Pearson residuals of the IG regression are considered as plotting statistics. For the evaluation purpose, a simulation study is designed, and the performance of the proposed methods is compared with existing counterparts in terms of the run length properties. Moreover, run‐rules are also implemented to gain the efficiency of the Shewhart type GLM‐based control charts under small‐to‐moderate shifts. Finally, an example related to the yarn manufacturing industry is also used to highlight the importance of the stated proposal.

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Quality optimization and process capability analysis of ring spun Supima cotton yarn

Oncul¹

2021

Materials Testing

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The response surface method and process capability analysis were conducted in this study to improve evenness, hairiness, imperfections, and tensile strength of ring spun Supima cotton yarn by optimizing values of spinning parameters including traveler mass, yarn density, spindle speed, and yarn twist. The yarns were spun into three densities: 20 Ne, 30 Ne, and 38 Ne. An USTER Tester 5-S800 was used to evaluate the irregularity parameters of yarn. A Lloyd tester was also used to determine the yarn breaking load. The response surface method was applied to optimize the statistical values obtained from the tests of the yarns produced based on the combination values of system parameters. Each response was subjected to a response surface design study, and the optimal values were achieved by using a response optimizer to perform multiple response optimization. Interpreting the response surface design results yielded information such as the absolute values of effects, response degree of significance, data compatibility, and model suitability. Individual desirability, composite desirability, different estimates for each response, and the relationship between response and system variables were all revealed by interpreting the response optimization results. The capability indices of process capability analysis were used to compare process performance before and after response optimization. By interpreting the capability indices’ values of process capability analysis, the capability of the process to meet requirements and improvement potential were acquired. The data was analyzed by implementing Minitab software (Version 19).

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Quality Variation Minimizer: A New Approach for Quality Improvement in Textile Industry

Cited by 3 publications

References 10 publications

Statistical Analysis of Manufacturing, Trade and Service in the Textile Industry in Ecuador, 2000-2020

Statistical Analysis of Manufacturing, Trade and Service in the Textile Industry in Ecuador, 2000-2020

GLM‐based control charts for the inverse Gaussian distributed response variable

Quality optimization and process capability analysis of ring spun Supima cotton yarn

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