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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
MDPI
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tenaga Nasional |
| Summary: | 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. |
|---|