SBIR Phase II: Big Data Analytics for Facility Operations and Management

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project results from improving the efficiency in facilities management (FM) of institutional and commercial buildings by enabling a streamlined transition to efficient, proactive operations using the power of big data analytics. This provides an opportunity to reduce estimated $78.5 Billion - $127.3 Billion in waste due to reactive maintenance per year in the US commercial facilities market alone. A data driven, proactive approach provides a unique opportunity that enable facilities managers to assess as-is conditions of assets, avoid non value-add activities and plan maintenance tasks to avoid failures and shutdown. This will contribute towards transforming a traditional industry to an advanced data-driven one. It will also enable significant reduction in the disruptions caused to occupants due to failures in facilities. Given that Americans spend 85-90% of their time indoors and any disruptions caused by facilities directly impact their qualities of lives, the broader societal impact of reducing failures in facilities is significant. This Small Business Innovation Research (SBIR) Phase II project intends to research, develop and demonstrate the feasibility of using big data analytics and machine learning to transform facilities operations and maintenance decisions. Owners and operators of the over five million commercial and institutional buildings in the United States are faced with the challenges of managing aging and crowded building infrastructure. They waste between 30% and 40% of resources by operating in a predominantly inefficient, reactive mode. This project targets development of computational mechanisms that automatically analyze integrated building information to identify patterns that lead to actionable insights that help reduce non value-add activities and improve resource utilization in FM daily operation and planning. By combining advanced machine learning technologies with existing building information modeling (BIM) resources, the company is proposing to develop high-impact, statistical and visual methods for optimizing the decision-making abilities of facility managers and with that, the performance of critical facilities infrastructure and maintenance crews. The results of this research will include algorithms and methods to normalize heterogeneous building data, detect patterns and anomalies, from which actionable insights can be derived with domain knowledge, and generate qualitative and quantitative output appropriate for improved decision making in managing commercial facilities.