SBIR-STTR Award

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 systems 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. 0xA0 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. 0xA0 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 officers 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.

Benefit:
The system proposed by DataCrunch Lab should result in a number of tangible benefits over existing approaches in the industry: Improved resiliency Increased productivity Improved visibility on the health of assets monitored By enabling every asset to self-monitor and self-diagnose, the system, as a whole, will be more resilient. 0xA0 In the face of external events that severely impact communication channels, assets will be able to communicate critical current needs to a central entity. 0xA0 This central entity, in turn, will determine the asset's present and future needs both in terms of material and personnel. 0xA0 Integrating DataCrunch Lab's solution into commercially available 0xA0 enterprise asset management solutions will enable the support of automated responses to the needs reported by assets. Organizations that incorporate the technology hereby proposed will see an improvement in their logistical support system. 0xA0 The underlying infrastructure will continuously look for early indications of failures, and proactively preempt the conditions that could result in equipment downtime. 0xA0 As a result, these institutions will experience an increase in productivity by optimizing the availability of their assets. Augmenting the core functionality of existing asset management technology with the proposed data environment, 0xA0 the solution will result in improved visibility into the health of the assets being monitored. 0xA0 Different views and dashboards will communicate the status of the equipment to the different stakeholders, and seamlessly provide the controls to the right people to allow them to effect necessary interventions, at the right time. The proposed technology would have potential commercial applications 0xA0 for 0xA0 asset-intensive industries that need to maintain assets in remote environments such as 0xA0 Energy and Utilities, Government and Defense, Manufacturing, and Transportation and Logistics, among others. 0xA0

Keywords:
asset management, asset management, Artificial Intelligence, Machine Learning, Digital Logistics, Dynamic Data Sharing, Edge Computing, remote monitoring, disaster response