International Journal of Science, Engineering and TechnologyAuthors: Nirmal Kumar Jingar
Abstract: Enterprise-scale supply chain and cloud operations are increasingly complex due to dynamic demand patterns, distributed infrastructures, stringent compliance requirements, and the need for real-time decision-making. Traditional rule-based automation and manually governed systems struggle to adapt to rapidly changing operational conditions, leading to inefficiencies, higher operational costs, security risks, and limited scalability. As enterprises move toward autonomous operations, the absence of strong governance, transparency, and accountability poses significant risks in mission-critical environments. Existing research has explored autonomous systems using machine learning, reinforcement learning, and AI-driven orchestration for supply chain optimization and cloud resource management. While these approaches demonstrate improved efficiency and adaptability, they often lack integrated governance mechanisms, explainability, policy enforcement, and compliance awareness. Moreover, many existing models operate in isolated domains, fail to coordinate cross-layer decisions, and exhibit limited robustness under real-world enterprise constraints. To address these challenges, this paper proposes a Governed Autonomous System (GAS) framework that integrates AI-driven decision intelligence with policy-aware governance, human-in-the-loop oversight, and compliance-driven control layers. The proposed model combines predictive analytics, autonomous agents, and continuous policy validation to enable secure, explainable, and adaptive decision-making across supply chain and cloud operations. Governance rules dynamically constrain autonomous actions, ensuring alignment with enterprise objectives, regulatory standards, and risk thresholds. Experimental evaluation using simulated enterprise workloads demonstrates that the proposed framework achieves significant improvements in operational efficiency, decision accuracy, and resource utilization. Compared to existing autonomous and non-governed baselines, the system shows higher prediction accuracy, reduced latency, improved cost optimization, and enhanced compliance adherence, validating its effectiveness for enterprise-scale deployment.
DOI: https://doi.org/10.5281/zenodo.18629297
