IoT News - ARC's IoT full-stack technology to be introduced in the Next Generation Squad Weapon (NGSW) program


Groundbreaking U.S.-Based IoT Company Begins Data Integration with USMC Maintenance Systems.

Armaments Research Company, Inc. (ARC) announced today a contract award intended to further scale its conditions-based maintenance platform for U.S. Marine Corps (USMC) crew-served weapons.

This new award will integrate ARC’s flagship product with USMC enterprise maintenance system data to gain deeper sustainment insights, create a seamless user experience, and implement resilient system security.

This task order is part of the 5-year, $60-million Small Business Innovative Research (SBIR) Phase III contract with the U.S. Department of Defense (DoD) and General Services Administration (GSA) to develop systems for the Joint All Domain Command and Control (JADC2) project portfolio. JADC2’s goal is to connect sensors from each military service into one, integrated network.

The project expands upon Task Order 2 with the USMC, which ARC introduced last October. Task Order 2 focused on developing a turnkey predictive maintenance software system that empowers units to anticipate, plan and take proactive steps for events such as parts repair or failure before they occur, ensuring reliability and safety during training and operations. This platform will now be bolstered by enterprise maintenance data to better detect non-age-related weapons issues and empower end users to take action.

“The USMC has consistently expressed its desire for an integrated maintenance solution rather than an application end users must manage separately,” said Michael Canty, ARC’s CEO.

“This project represents a meaningful step toward building this weapons maintenance capability into end users’ natural sustainment rhythm.”

ARC adapted its state-of-the art, Internet-of-Things (IoT) sensor to crew-served weapons platforms to collect, synthesize, and communicate diagnostics for units to assess the overall health of their platforms. The data was used to develop machine-learning (ML) algorithms to detect when a component of the weapon may fail or when the weapon system requires maintenance.


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