Leveraging Fleetwide Digital Twin Technology to Facilitate Optimized Readiness and Mission Success

The partitioned distribution of logistics and supply chain activities throughout each branch of the Department of Defense has led to the use of several various information management systems. To combat consequences, such as the ripple and bullwhip effects, and to better meet the commercial sector�s capabilities, automation and digital twin technology can be leveraged to fuse data from all deployment sites into a centrally hosted source of truth. With a streamlined process incorporating deep machine learning (ML) and artificial intelligence (AI), this digital twin can be utilized to enable informed decisions guiding program success via optimized operational readiness and improved mission success. The current challenge is to leverage the terabytes of data generated by deployed, monitored systems to provide actionable information for all levels of users. The implementation of a cloud-based application performing data transactions, learning and predicting future states from current and past states in real-time, and communicating those anticipated states is an appropriate solution for the purposes of reduced latency and improved confidence in decisions. Decisions made from ML and AI application will improve the mission success and performance of systems, which will in turn improve overall cost/effectiveness of any program. The Systecon team constructs model-based, serialized digital twins across a system�s lifecycle and across logical/operational groupings of systems. This bi-directional coupling of data throughout the enterprise enables tactical, operational, and strategic decision support, detachable and deployable logistics services, and configuration-based automated distribution of digital technical and product data to enhance supply and logistics operations. As a complete solution, this approach significantly reduces program risk by allowing flexible configuration of data, data relationships, business process workflows, and early test and evaluation, especially budget trade-off analyses. The team�s AI visualizes influential data relationships, revealing what data (and what relationships within the data) matter most. The platform�s curated model library reduces the time, effort, and �trial and error� associated with model encoding, fitting, testing, and tuning. The platform also reduces risk by making models available through multiple API languages and options, ensuring ML/AI capabilities can be operationalized across multiple platforms and applications.