Evaluation of human factors on visual inspection skills in textiles and clothing: A statistical approach

Author:

Ramzan Muhammad Babar1,Qureshi Sheheryar Mohsin2,Habib Muhammad Salman3,Naveed Tayyab4,Awais Muhammad5,Jamshaid Hafsa1ORCID

Affiliation:

1. Faculty of Engineering and Technology, National Textile University, Faisalabad, Punjab, Pakistan

2. Department of Industrial and Manufacturing Engineering, NED University of Engineering and Technology, Karachi, Pakistan

3. Department of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore, Pakistan

4. School of Design and Textile, University of Management and Technology, Lahore, Pakistan

5. Department of fiber and textile technology, university of agriculture, Faisalabad, Pakistan

Abstract

In visual inspection, the impact of different human factors on inspection performance has been evaluated in past. However, there is a need to study the effect of relevant factors and their interrelation in a single framework to monitor and improve inspection performance. This study aims to identify and evaluate such human factors for proposing a framework that indicates their interrelationship. After reviewing the literature, five constructs with their respective observed variables are selected to propose the framework. The survey instrument is developed using the suggested framework to collect data from industry professionals where human labor perform the inspection for products such as textiles. Finally, data are collected through an online survey and analyzed using confirmatory factor analysis to test the hypotheses. The results verify that the selected constructs are good measures of inspection skill, however, some variables are excluded from the model for being insignificant. Results show that the factors related to the constructs – personal, physical/mental, and organizational – are found more influencing than system and inspection-task factors. This study identifies and models the inspection-related significant factors into a framework that will help monitor and improve the performance of an individual or whole inspection station.

Publisher

SAGE Publications

Subject

General Materials Science

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