Managing multi-level risks ranging from the fiscal/personnel resources to fluctuating material/production costs have always been challenges due to the integrated nature of Air Force (AF) with governmental and industrial partners. Establishing a quantitative framework that provides basis for making informed decisions on these pressing issues can greatly impact how Air Force strategically allocate valuable resources and deploys operations. Our team has developed a consolidated system that address underlying risks in the asset management and quantitative trading space. Our proprietary technology aggregates AI-based predictive knowledge at higher level with low-level autonomous strategic decision-making process, providing real-time insights and dynamic strategy coordination. The teams believe that our risk management approach can address a variety of problem areas concerning AF including supply chain, cost modelling, and predictive maintenance, where the performance is largely affected by quantifiable systematic risks. The translation of the technology comes from the fact that the problems that underlie finance, operations, and resource allocation can be formulated and optimized in a similar cost-benefit analysis framework that is addressed in quantitative finance. By combining the predictive power from AI with our existing technical infrastructure, our robust and adaptive approach can greatly benefit AF by improving efficiency, productivity, and reducing cost.artificial intelligence,operations research,Risk Management,Strategic Allocation,supply chain,Cost Modelling and Optimization,predictive maintenance
