Request for Information (RFI): Training & Simulation

The U.S. Army Materiel Command Contracting Command Orlando is issuing a Request for Information on Training & Simulation seeking industry feedback by August 15, 2026, regarding modern integrated Live, Virtual, and Constructive (LVC) environments structured through layered architecture to support multidomain operation training, testing, and experimentation. The RFI is not a solicitation and does not constitute an obligation to procure; responses will not be considered binding offers and no contracts will be awarded based on submissions.

The Army is developing a Characteristics of Need (CoN) framework for a Global Training Environment that consolidates fragmented training systems (I-MILES, CCTT, JLCCTC) into a modular, standards-based LVC capability. The envisioned architecture comprises four integrated components: a Sensor/Instrumentation Layer collecting organic platform data, a Transport Layer leveraging the Unified Network for high-fidelity data movement, a Core Services Layer providing data fusion and geospatial services, and an Interoperability/Application Layer using standardized protocols (DIS, HLA, TENA).

The RFI requests responses addressing ten specific questions covering feasibility assessments, architecture frameworks, current and future technology capabilities, open standards approaches, cybersecurity implementation, operational hardware/software reuse, exportability to Joint Services and Mission Partners, LVC marketplace development approaches, and recommendations for missing elements in the draft CoN and taxonomy. Responses must be submitted to david.s.cannon.civ@army.mil by June 8, 2026 (note: the RFI document states June 8 as the deadline, conflicting with the August 15 posted due date), in MS Word or PDF format, maximum 16 standard pages. Proprietary information must be clearly designated; responses requiring Non-Disclosure Agreements will not be accepted. The CoN documents are living frameworks subject to 90-day review cycles based on technological observations and lessons learned through experimentation.