1. Machine Learning/Artificial Intelligence in Centralized Enterprise Logistics and Supply Chain Support
PROBLEM STATEMENT
BENEFITS
 Multiple information management systems currently used in logistics, supply chain, and operational planning leads to:
 Miscommunication
 Misallocation of spares
 Misinformation
Accelerated resource exhaustion
Preventable damage to on-board components
Terabytes (TB) of data generated by deployed systems cannot be processed quickly and effectively enough to inform commanders, maintainers, logisticians, and program managers for real-time decision making severely limiting tactical agility in support of operations
Fuse data from all sites into a single source of truth central to the mission to facilitate:
Slowed resource expenditures (monetary spending, resource consumption, manpower)
 Informed budgetary decisions
 Alignment of workforce personnel with mission-critical tasks
Limited chain of custody of data
Enhance the ability to make well-informed decisions at the tactical and strategic levels
Reduce unnecessary damage to onboard components resulting from specific operational states
TECHNOLOGY SOLUTION
Leverage TB of data generated by deployed, monitored systems to provide actionable information for commanders, maintainers, logisticians, and program managers
Develop a suite of algorithms for accurate pattern detection through a combination of classification algorithms (decision trees, language processing algorithms, etc.)
Customize a cloud-based application that:
Performs data transactions (ingests state variables)
Learns from past states
Predicts future (system) states in real-time
Communicates anticipated states
Identifies problematic states with specific regime parameters