Title: Mining the productivity data of the garment industry

Authors: Abdullah Al Imran; Md Shamsur Rahim; Tanvir Ahmed

Addresses: Department of Computer Science and Engineering, American International University-Bangladesh, 408/1, Kuratoli, Dhaka 1229, Bangladesh ' Department of Computer Science and Engineering, American International University-Bangladesh, 408/1, Kuratoli, Dhaka 1229, Bangladesh ' Department of Computer Science, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA

Abstract: The garment industry is one of the key examples of the industrial globalisation of this modern era. It is a highly labour-intensive industry with lots of manual processes. Satisfying the huge global demand for garment products is mostly dependent on the production and delivery performance of the employees in the garment manufacturing companies. So, it is highly desirable among the decision makers in the garments industry to track, analyse and predict the productivity performance of the working teams in their factories. This study explores the application of state-of-the-art data mining techniques for analysing industrial data, revealing meaningful insights and predicting the productivity performance of the working teams in a garment company. As part of our exploration, we have applied eight different data mining techniques with six evaluation metrics. Our experimental results show that the tree ensemble model and gradient boosted tree model are the best performing models in the application scenario.

Keywords: data mining; productivity prediction; pattern mining; classification; garment industry; industrial engineering.

DOI: 10.1504/IJBIDM.2021.118183

International Journal of Business Intelligence and Data Mining, 2021 Vol.19 No.3, pp.319 - 342

Received: 07 Feb 2019
Accepted: 29 Aug 2019

Published online: 29 Sep 2021 *

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