K M, Zubair and Akhtaruzzaman, Khan and Dil Tabassum, Subha (2025) Ai-Based Decision Systems and Electrical Machine Automation for Enhanced Supply Chain Performance in Manufacturing. American Journal of Engineering, Mechanics and Architecture, 3 (8). pp. 53-79. ISSN 2993-2637

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Abstract

Supply chain management of manufacturing industries underwent a fundamental change through rapid advancements in electrical machine automation which boosted operational efficiency and led to cost reduction and better decision making. A study analyzes how automation capabilities which consist of artificial intelligence, robotics along with Internet of Things technologies work to enhance supply chain optimization. Automated system evaluation uses Fuzzy-Based Smart Manufacturing Dataset along with Intelligent Manufacturing Dataset for Predictive Optimization to demonstrate their ability in lowering operational interruptions while improving output precision and optimizing stock control systems. The combination of predictive maintenance together with real-time data analytics and Just-in-Time manufacturing enables automation to prevent supply chain disruptions and boosts market demand responsiveness. The study investigates major obstacles to automation adoption such as extensive implementation expenses together with the replacement of personnel and security system threats. Automation generates extended financial advantages through labor cost reduction and material waste decrease and power conservation methods. The manufacturing industry will gain advances from future trends that combine predictive analytics with artificial intelligence tools with block-chain based supply chain visibility while developing sustainable automation solutions. The study demonstrates key understandings about supply chain automation functions through its discovery of necessary technological development combined with employee skill transformation. Companies in manufacturing should utilize automation properly to gain better supply chain responsiveness along with higher productivity levels and sustainable outcomes. This research contributes knowledge about smart manufacturing to establish a basis for upcoming studies and industrial automation technical progress.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	01 Sep 2025 08:28
Last Modified:	26 Dec 2025 15:20
URI:	http://eprints.umsida.ac.id/id/eprint/16318

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