The Organic Industrial Base Dashboard (OIBD) solution, to be demonstrated at Red River Army Depot (RRAD), addresses the current inability to make strategic, informed energy-resilient investment decisions in facilities based on the multifaceted, siloed data, sources currently utilized to manage infrastructure, operations, and projects. With OIBD, the Department of Defense (DoD) will have a holistic energy and infrastructure analysis platform that incorporates data from a variety of existing tools in many different formats. The OIBD will be a tailored solution that both base-level and headquarters (HQ) personnel can engage with to understand historical energy and infrastructure impacts of facility projects and operations. Currently, DoD installations are at various stages of smart meter implementation and connectivity, delaying the ability to look at building and installation wide energy consumption and infrastructure operations. OIBD will integrate legacy and modern meter information as well as maintenance, operations, work order, environmental, occupancy, mission, equipment, and production data (a priority for Army Materials Command (AMC) DoD sites). The compilation of these important installation-specific data points will render a capability that Energy Managers and HQ leadership will use as a strategic energy and infrastructure decision-support tool, resulting in a clear understanding of power and holistic infrastructure requirements to meet production goals; a critical need for DoD and AMC sites like RRAD.
OIBD is built and iterated with DoD-site input through the integration of artificial intelligence (AI) technologies involving advanced data analytics and machine learning techniques into an integrated tool suite and data processing pipeline, which culminates in a user-friendly decision-support dashboard that displays information at the appropriate level of abstraction for each user. The data processing pipeline is built from a cohesive set of components utilizing an array of open-source software systems that make up the environment called Data Ops platform. OIBD analytics components utilize machine learning techniques to build models that will help predict future data events through the recognition of patterns in the data, helping to drive insights and decision support. OIBD includes intelligent facility benchmarking, utility forecasting, novel weather normalization methods, and additional features to better predict a building’s performance using a combination of installation Metadata (e.g. size, age, floors, location, usage, plant replacement value, etc.) along with other manually and/or automatically collected data, including daily meter data. Metadata will be collected from sources like – Engineer Research and Development Center's (ERDC)’s Virtual Testbed for Installation Mission Effectiveness (VTIME), a central data repository. These capabilities provide personnel with tools that allow them to quantify building performance, account for seasonal weather impacts, predict future facility energy usage, expenditures, evaluate project outcomes based on data, and quickly determine effective plans of action. Risks are limited with this solution as additional hardware is not needed and existing computers and personnel will be sufficient for ongoing OIBD use.
Decision Support Tools developed will have benchmarking, machine learning, advanced data analytics, and predictive analysis. In addition, production planning benefits will be realized through cost and quality analysis. OIBD will provide timely, accurate and actionable information for RRAD's production Directorates, Commander, Tank-automotive and Armaments Command (TACOM), and AMC. OIBD will help the AMC sites execute capital planning for energy and infrastructure projects, addressing relevant Informational Technology challenges such as secure data sharing and communication, as well as, Operations Security (OPSEC) and TEMPEST compliance. The team will be available for monitoring energy efficiency, resiliency and reliability to build Army readiness and sustaining mission-critical objectives.