DATACRUNCH LAB, L. L. C. — Department of Defense SBIR Phase I: N204-A02
DATACRUNCH LAB, L. L. C. — SBIR Phase I award from Department of Defense.
- Amount
- $149,962
- Agency
- Department of Defense · Navy
- Program / Phase
- SBIR · Phase I
- Topic
- N204-A02
- Solicitation
- 20.4
- NAICS
- —
- Place of performance
- NC
- Period
- 2020-07-13 → 2020-12-14
Description
Situation: The success and safety of every mission depends on getting needed supplies, materials and equipment at a moment's notice. Unfortunately, external events like health emergency, natural disasters and wartime may make a system inaccessible. The system’s physical remoteness or inaccessibility, as well as lack of sufficient bandwidth for reliable communication, adds to the complexity and fragility of providing effective logistical support. The implication is that it is difficult to get the right logistics information, difficult to communicate it to the right people, difficult to determine the best response, and then difficult to deliver that response. DON is seeking modern tools and capabilities to improve the resiliency of the existing systems utilized for logistical support in order to forward deployed forces, not just material support, but ensured dynamic, reliable, and robust delivery of medical supplies, support and care. Long Term Vision: DataCrunch Lab proposes the development of an integrated data environment that would assist logistics personnel in getting the right information, communicating the information to the right people, determining the best response, and delivering that response. The proposed platform would aggregate data from different sources - sensors, field reports, maintenance records, etc. - to monitor systems under fragile environments (remote locations, limited bandwidth, disruption due to natural disasters, etc.). Currently, issues in these remote, fragile systems are only detected during routine inspection or maintenance. The innovation is focused on identifying anomalies and communicating the relevant contextual information needed to quickly service and maintain the remote systems. Our goal is to augment the logistics officer’s capability to determine the best response. We would develop a method to use sensor, machine learning, and data analytic technologies to quantify with confidence levels the current status of platforms and systems, and the logistic and maintenance needs.