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Analysis of the Adoption Decisions of the Components of the Internet of Things in the Ready-Made Garment Industry.

Authors :
Bhuiyan, Md Zisanul Haque
Bari, A. B. M. Mainul
Source :
IEOM North American Conference Proceedings; 2023, p272-284, 13p
Publication Year :
2023

Abstract

The ready-made garment (RMG) industry is one of the largest industrial sectors in Bangladesh in terms of economy and employment. As the world economy moves forward, this highly people-dense industry needs to be digitalized. However, the importance of digital technology, particularly the Internet of Things (IoT), has just lately been recognized by most industries, which is also an important part of Industry 4.0. The IoT has seven important elements. Though IoT has the potential to grow businesses, it is difficult to implement all the elements of IoT simultaneously. Thus, the study aims to build a hierarchy model of IoT elements to be implemented in the RMG industry of Bangladesh. Through an extensive literature review, fourteen significant elements of IoT were identified. Then the study employs an integrated multi-criteria decision-making (MCDM) framework to rank the elements of IoT based on their potential for application in the Bangladeshi RMG industry. The weights of the evaluation criteria were determined using a fuzzy analytical hierarchy process (FAHP), and the elements of IoT were ranked using a preference ranking organization method for enrichment evaluations (PROMETHEE II) based on the criteria weights obtained from FAHP. The study findings suggest that the Sensors are the most significant component of IoT, while Network Interconnection is the least significant component for IoT implementation. This study is expected to assist the stakeholders in the RMG and other related industrial sectors in formulating appropriate strategies and policies for the proper deployment of IoT. [ABSTRACT FROM AUTHOR]

Details

Language :
English
Database :
Complementary Index
Journal :
IEOM North American Conference Proceedings
Publication Type :
Conference
Accession number :
176285612