Space Force - Mission Assurance Digital Twin (MADT)
Challenge ended
Description
U.S. Space Force mission assurance processes are often siloed and dependent on static, periodic assessments. This limits the ability to dynamically assess risk to critical space and joint missions in contested and congested domains. Decision-makers are left with incomplete, fragmented pictures of mission readiness, while critical data is scattered across hundreds of systems without a common model or master catalogue. Drawing from digital transformation in advanced commercial logistics and manufacturing—where “digital twins” model, predict, and optimize operations in real time—this challenge seeks to create the foundational architectural blueprint for a Mission Assurance Digital Twin (MADT). ONI, as the nonprofit intermediary, will post this challenge through the ONIX OTA to source and accelerate this vendor-agnostic framework, ensuring the solution is evidence-based, technically sound, and aligned to operational priorities.
Overall Objective
Deliver a comprehensive, vendor-agnostic Architectural Blueprint that de-risks all future MADT development by replacing assumptions with evidence-based analysis. This blueprint will define the data, relationships, and processes needed to model mission risk, enabling the government to develop precise, interoperable requirements for future acquisition and deployment across the Space Force enterprise.
Problem Statement
Current mission assurance processes lack a validated, data-driven architecture to integrate diverse information sources into a persistent, predictive risk assessment model. Data critical to modeling mission impact is fragmented across systems, documents, and organizations, with no common data model or standardized assessment methodology. Technical and programmatic steps to ingest and fuse legacy data, OPLANs, and live sensor feeds remain undefined, creating significant integration risk. Without this blueprint, the Space Force risks fielding siloed, non-interoperable solutions that require costly rework, delay critical decision-support capabilities, and leave warfighters vulnerable to emerging threats. Commanders will continue to make high-stakes decisions with incomplete or outdated information—reducing operational effectiveness and limiting the adoption of advanced analytics, AI/ML, and simulation technologies.