Proactive Risk Monitoring Using Predictive Analytics

In a pilot study effort in 2013, the Secretary of Defense for Manufacturing and Industrial Base Policy (ODASD (MIBP)) developed a methodology that goes beyond legacy reactive and program-centric frameworks for assessing industrial base risk. This proposed effort leverages the pilot studys work with the fragility and criticality (FaC) assessment to develop a predictive and proactive tool to assist in the analysis and categorization of industrial base risks. The risk mitigation tool will use advanced data warehouses to ingest identified sources of relevant data and will employ unsupervised deep learning algorithms to identify fragility and criticality patterns in the dataset. Other untapped sources of data will be automatically gathered by the tool to assess industrial base risk. This innovative way of predicting supply risks will be extremely efficient and accurate and will require less direct human interaction than the traditional FaC process. UTC/UDRI team includes a commercialization partner, capable of providing FASI-G (Fleet Automotive Support Initiative - Global) data and transitioning this technology to several targeted Department of Defense (DoD) platforms. Our technical team includes members with relevant experiences in advanced machine learning algorithms and data analytics.Approved for Public Release | 17-MDA-9219 (31 May 17)