Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach

Asadi, Shahla ORCID: 0000-0002-8199-2122, Nilashi, Mehrbakhsh, Iranmanesh, Mohammad, Hyun, Sunghyup Sean and Rezvani, Azadeh (2021) Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach. Technovation, 118. p. 102426. doi:10.1016/j.technovation.2021.102426

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Abstract

We have entered a new technological paradigm with the emergence of Internet-embedded software and hardware, so-called the Internet of Things (IoT). Although IoT offers pan-industry business opportunities, most industries are only just beginning to employ it. We thus determine and prioritize the most important factors that influence IoT adoption, and reveal how IoT adoption affects the performance of manufacturing companies. We use a hybrid method that integrates the adaptive neuro-fuzzy inference system with the decision-making trial and evaluation laboratory, a novelty of the study. The literature on this subject informs our selection of the critical adoption factors, namely, technological, environmental, and organizational. The data are acquired from industrial managers involved in the decision-making process of information technology procurement in manufacturing companies in Malaysia. Our results can support IoT adoption guidelines geared to yield maximum efficiency in manufacturing industries, service providers, and governments.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Adaptive neuro-fuzzy inference system; ANFIS; Decision-making trial and evaluation laboratory; DEMATEL; Internet of things; IoT; Manufacturing; Multi-criteria decision-making; Performance
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Anne Pengelly
Date Deposited:	05 Apr 2023 09:16
Last Modified:	21 Nov 2023 04:15
URI:	https://eprints.glos.ac.uk/id/eprint/12596

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