Risk-Adjusted Inventory Planning for Liquid-Cooled AI Server Supply Chains Under Demand Uncertainty
DOI:
https://doi.org/10.22399/ijcesen.5424Keywords:
Risk-adjusted inventory, Demand uncertainty, AI server supply chain, Liquid cooling systems, Supply chain riskAbstract
The rapid expansion of artificial intelligence (AI) applications has created a need for advanced computing systems which mainly includes liquid-cooled AI servers. The systems provide better thermal management yet they create more difficulties for supply chain management because their specialized parts and expensive equipment and long manufacturing delays. The supply chain management process faces its most difficult problem when unpredictable demand patterns create operational challenges that result in excessive inventory and product shortages and greater chance of operational interruptions. The study shows how risk-adjusted inventory planning methods help organizations solve their inventory management problems. The study combines existing research on demand uncertainty together with safety stock optimization and risk pooling and supply chain coordination. The study shows that traditional inventory models which depend on deterministic or simplified assumptions produce specific limitations. The research covers the main elements which define liquid-cooled AI server supply chains through their fast technology obsolescence and need for specialized suppliers. Digital supply chains together with machine learning-based forecasting and real-time data integration capabilities drive better inventory performance in organizations. The research study identifies three main problems which supply chain partners face, which include data limitations and model complexity and coordination issues between different partners. The research results demonstrate that organizations can achieve better supply chain operations through the combination of risk-aware strategies together with advanced analytical tools. The review provides a structured foundation which enables future research while creating more effective inventory planning models for high-technology environments.
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