Visual Asset Management (VAM) software

Summary of the technology

Background

The value of large institutional assets including property, facilities, and equipment in North America is in the trillions of dollars. The effective management of the maintenance of such large capital asset platforms is very complex which makes it challenging to effectively allocate maintenance spending in an optimized way. The estimated maintenance and repair expenditure requirements in Canada are of the order of CDN$110.0 billion per year and in the U.S. such costs are of the order of US$800 billion per year. Given such significant annual spending, there is a constant demand for tools and services that can better allocate resources to asset maintenance activities.

University of Waterloo

Description of the invention

The VAM system offers several key novel differentiating capabilities from competitive tools. Firstly, VAM utilizes a small hand held computer tool to easily enter inspection data, including direct linking of electronic floor plans to photos taken from the site. This on-site data entry capability simplifies the data collection step and reduces the chances of data transcription errors associated with conventional paper based manual practices. The VAM system also utilizes a novel methodology to predict the condition of assets based on analyzing historical asset maintenance data. This feature offers the potential to significantly reduce the number of physical inspections required to survey overall asset condition and thus stands to reduce the end users inspection costs. The VAM system also utilizes sophisticated algorithms to enable budget allocation decisions based on optimization mathematics (that would previously require a supercomputer to calculate) that considers all combinations of asset condition state versus the costs to repair. This feature stands to either allow asset managers to spend less on repairs or to enable more repairs for the same budget amount. Both the condition prediction and budget allocation optimization capabilities are unique to VAM. Lastly, VAM is being developed to include elements of the LEED green initiative standard to address building sustainability and energy efficiency issues.

Advantages

  • Accurate and speedy on-site data entry and 3D visualization
  • Condition prediction methodology reduces number of inspections
  • Novel algorithms enable optimized budget spending

Potential applications

  • Maintenance and capital renewal decision support for mid-large organizations (e.g. school boards, universities, utility operators, property managers, mining, etc.)

Related Keywords

  • Artificial Intelligence (AI)
  • Software Technologies

About University of Waterloo

The University of Waterloo, renowned for its innovative spirit and co-op education model, excels in cutting-edge research across diverse fields, including advanced manufacturing, artificial intelligence, sustainability, and health technologies.

University of Waterloo

Never miss an update from University of Waterloo

Create your free account to connect with University of Waterloo and thousands of other innovative organizations and professionals worldwide

University of Waterloo

Send a request for information
to University of Waterloo

About Technology Offers

Technology Offers on Innoget are directly posted
and managed by its members as well as evaluation of requests for information. Innoget is the trusted open innovation and science network aimed at directly connect industry needs with professionals online.

Help

Need help requesting additional information or have questions regarding this Technology Offer?
Contact Innoget support