A Data-Driven Approach to In-Transit Visibility

Military operations and logistics are inextricably linked. The importance of In-Transit Visibility (ITV) capability for reliable tracking and delivery of logistical items has well been recognized across Department of Defense (DoD) logistics programs. The current state-of-the-art solution to ITV employs a Radio-Frequency Identification (RFID) network, which comprises a large number of read-and-write stations and hence requires high cost and intensive manpower. Motivated by past experiences on air traffic management and machine learning, Optimal Synthesis Inc. (OSI) proposes an alternative approach that exploits existing variety of data sources, a good example of which is the IDE/GTN Convergence (IGC) that is a unified data service across DoD cargo movement and tracking. The approach combines machine learning techniques with the estimation and prediction methodologies for tracking the cargo movement and predicting the time-of-arrival in each mission leg. By performing on-line risk monitoring, a decision support tool that generates automated alerts for human intervention is also developed using a stochastic decision framework. The proof-of-concept demonstration is planned in Phase I, and the software prototype is planned to be developed in Phase II for functional demonstration.