An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic

Several research works have addressed the different aspects and technologies associated with Smart Manufacturing Systems (SMS); however, the evaluation challenges while establishing a new SMS that requires pre-implementation planning and assessment have given little attention. To overcome this limit...

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Main Authors: Grace J., Mahmoud M.A., Mahdi M.N., Mostafa S.A.
Other Authors: 57210261059
Format: Article
Published: MDPI 2023
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spelling my.uniten.dspace-269492023-05-29T17:38:06Z An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic Grace J. Mahmoud M.A. Mahdi M.N. Mostafa S.A. 57210261059 55247787300 56727803900 37036085800 Several research works have addressed the different aspects and technologies associated with Smart Manufacturing Systems (SMS); however, the evaluation challenges while establishing a new SMS that requires pre-implementation planning and assessment have given little attention. To overcome this limitation, this paper formulates an evaluation framework by identifying apparent evaluation factors to measure the effectiveness of a particular SMS configuration before implementation. Three factors from the literature studies have been used as inputs to control the final output of the configuration modal. Compositions were manipulated based on how factors affected the manufacturing cost justification in multiple setups. Different configurations were analyzed based on the trained Fuzzy Logic model by configurations and based on the trained Fuzzy Logic model using MATLAB�s Fuzzy Logic Designer tool. Results obtained from the evaluation performed by various configuration experiments were later presented to actual field engineers from the manufacturing industry to evaluate the satisfaction level of the evaluation framework. The result showed that this proposed configuration model has a satisfactory rate of 83.7%, as this was achieved by significant feedback from field engineers. This study has significantly facilitated the identification of influential factors and the measured relationship of the factors in the formulated configurations, enabling the best configuration approach to be identified. Therefore, it can be concluded that a visualized and measured configuration system can influence decision-making in the manufacturing industry, thus allowing manufacturers to stay competitive by making well-versed decisions proactively. Exclusively, this research has staged a framework for the industry to follow suit and adapt for future research work related to the SMS field. � 2022 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T09:38:06Z 2023-05-29T09:38:06Z 2022 Article 10.3390/app12052560 2-s2.0-85125790637 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125790637&doi=10.3390%2fapp12052560&partnerID=40&md5=cf95037603114d727696aae6ae11de98 https://irepository.uniten.edu.my/handle/123456789/26949 12 5 2560 All Open Access, Gold MDPI Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Several research works have addressed the different aspects and technologies associated with Smart Manufacturing Systems (SMS); however, the evaluation challenges while establishing a new SMS that requires pre-implementation planning and assessment have given little attention. To overcome this limitation, this paper formulates an evaluation framework by identifying apparent evaluation factors to measure the effectiveness of a particular SMS configuration before implementation. Three factors from the literature studies have been used as inputs to control the final output of the configuration modal. Compositions were manipulated based on how factors affected the manufacturing cost justification in multiple setups. Different configurations were analyzed based on the trained Fuzzy Logic model by configurations and based on the trained Fuzzy Logic model using MATLAB�s Fuzzy Logic Designer tool. Results obtained from the evaluation performed by various configuration experiments were later presented to actual field engineers from the manufacturing industry to evaluate the satisfaction level of the evaluation framework. The result showed that this proposed configuration model has a satisfactory rate of 83.7%, as this was achieved by significant feedback from field engineers. This study has significantly facilitated the identification of influential factors and the measured relationship of the factors in the formulated configurations, enabling the best configuration approach to be identified. Therefore, it can be concluded that a visualized and measured configuration system can influence decision-making in the manufacturing industry, thus allowing manufacturers to stay competitive by making well-versed decisions proactively. Exclusively, this research has staged a framework for the industry to follow suit and adapt for future research work related to the SMS field. � 2022 by the authors. Licensee MDPI, Basel, Switzerland.
author2 57210261059
author_facet 57210261059
Grace J.
Mahmoud M.A.
Mahdi M.N.
Mostafa S.A.
format Article
author Grace J.
Mahmoud M.A.
Mahdi M.N.
Mostafa S.A.
spellingShingle Grace J.
Mahmoud M.A.
Mahdi M.N.
Mostafa S.A.
An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic
author_sort Grace J.
title An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic
title_short An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic
title_full An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic
title_fullStr An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic
title_full_unstemmed An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic
title_sort evaluation model of smart manufacturing system configurations prior to implementation using fuzzy logic
publisher MDPI
publishDate 2023
_version_ 1806427729454694400
score 13.214268