SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to help owners and operators of commercial and institutional buildings to improve resource allocation by analyzing data from built infrastructure to enable smarter decision-making supported by detailed, measureable, real-time knowledge. By automatically integrating building information that is stored using various software applications and formats, this innovation enables owners and facilities managers to efficiently search for information and respond to emergency and failures, and proactively plan for operation and maintenance tasks. This innovation also applies artificial intelligence to automatically conduct big data analysis and identify opportunities to improve energy efficiency and operating performance of assets and indoor environment. Organizations can not only save operating budget by reducing equipment failures and energy waste, but also improve the quality of life and productivity for occupants.

This Small Business Innovation Research (SBIR) Phase I project is aimed at developing middleware technology to automatically integrate and analyze both structured and unstructured data from facilities design and operations. Facilities maintenance and operating is the longest phase in the life-cycle of buildings, accounting for more than 60% of the total cost of ownership. Owners and facilities managers are faced with the challenges of efficiently managing aging and crowded building infrastructure to extend the life of assets and control costs. However, fragmented and under-analyzed building information results in most maintenance work being conducted reactively to address problems that have already caused significant loss or waste. The vision of this innovation is to develop a fully commercialized software package to enable facilities managers to be more proactive in improving building occupant comfort, aligning limited resources where they have the most significant impact, and reducing wasted energy through optimized mechanical controls. This project aims to demonstrate the conceptual feasibility of using big data analytics and machine learning to revolutionize facilities operating and maintenance decisions. The results from this applied research will include algorithms and methods to combine structured data with field collected unstructured data into qualitative and quantitative output appropriate for improved decision making.

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.