Engineering Asset Management (EAM) is evolving through Industry 4.0 technologies such as IoT, AI, Digital Twins, and cloud computing, which generate vast amounts of asset-related data. However, conventional asset management systems often fail to integrate heterogeneous data sources, resulting in fragmented information, reactive maintenance, and increased operational costs. This study proposes a Knowledge Graph-Based Engineering Asset Management (KG-EAM) framework to enhance predictive decision intelligence for smart industrial assets. The framework integrates data from Industrial IoT devices, SCADA systems, Enterprise Asset Management (EAM) platforms, Computerized Maintenance Management Systems (CMMS), and engineering documentation into a unified semantic knowledge ecosystem. By combining ontology modeling, graph databases, graph embeddings, machine learning, and explainable AI (XAI), the framework enables predictive maintenance, root cause analysis, asset health prediction, and risk-aware decision-making. The proposed approach improves asset reliability, maintenance planning, operational efficiency, knowledge sharing, and lifecycle management while reducing downtime, maintenance costs, and unexpected equipment failures across diverse industrial sectors.
Vladimir Glushkov, Victor Glushkov· International Journal of Mod...· 0 citations
AI-Based Decision Intelligence (DI) Platforms are transforming digital business operations by combining artificial intelligence, machine learning, predictive analytics, natural language processing, and business analytics to support intelligent decision-making. Unlike traditional Business Intelligence systems that mainly provide historical insights, DI platforms deliver predictive and prescriptive recommendations that improve decision accuracy, operational efficiency, business agility, and customer satisfaction. The proposed framework integrates data collection, intelligent analytics, predictive modeling, decision optimization, and continuous learning within a scalable cloud-based ecosystem. The study demonstrates that AI-driven decision intelligence significantly enhances organizational performance and supports successful digital transformation across various industries. Furthermore, emerging technologies such as Explainable AI, federated learning, autonomous decision systems, and ethical AI governance will further strengthen enterprise decision ecosystems. Overall, AI-Based Decision Intelligence Platforms serve as a critical enabler for sustainable growth, competitive advantage, and intelligent enterprise transformation in the digital era.
Vladimir Glushkov, V. Glushkov· International Journal of Art...· 0 citations
The findings have shown that the combination of intelligent security solutions and a standardized governance model not only increases the resilience of the organisation, but also reduces cyber risks, facilitates compliance with regulatory requirements and safeguard the trust of customers.
Vladimir Glushkov, Victor Glushkov· International Journal of Com...· 0 citations
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