Integrating Intelligent Systems and Cloud-Based Analytics for Robust Manufacturing Resource Planning and Resource Allocation: A Comprehensive Framework
Abstract
The contemporary industrial landscape is undergoing a paradigm shift driven by the convergence of traditional manufacturing resource planning (MRP) and advanced computational intelligence. This research explores the integration of artificial intelligence, cloud computing, and real-time data analytics to address the persistent challenges of uncertainty in production environments. By synthesizing foundational models of MRP with modern advancements in cloud storage and predictive maintenance, this paper proposes an expansive framework for optimizing resource allocation. The study investigates the transition from deterministic scheduling to possibilistic and hybrid intelligent models that accommodate the stochastic nature of global supply chains. Furthermore, it examines the role of cloud-enabled big data analytics in enhancing responsiveness to large-scale disruptions and natural disasters. The findings suggest that a unified approach, leveraging both the computational power of cloud platforms and the adaptive nature of genetic algorithms and neural networks, provides a superior mechanism for managing order assignments and assembly line efficiencies. This article provides an exhaustive theoretical elaboration on the evolution of these systems, offering a roadmap for future industrial applications.
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References
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