Facility managers are often faced with building system procurement or replacement decisions, requiring them to select a system from among competitive manufacturers. Total cost of ownership (TCO) criteria, informed by built assets in operation in the manager's portfolio, provides some information to select the right asset manufacturer. However, managers must also consider technical performance to complete a more comprehensive analysis. Performance can be calculated using asset parameters like condition, age, and variation in condition to aid in TCO assessments. Leveraging past research and approaches, performance is calculated using an additive model that scales each parameter using a standard normalization technique and employs weighting factors to account for decision-maker input. Data from 20 Air Force installations across the US and two asset types are analyzed, showing the utility of a performance metric. This analysis shows that as manufacturer diversity in portfolios decreases, performance increases for most of the asset types modeled. This paper presents a new performance metric that can be used as an additional criterion in TCO models to build a more robust decision framework.
The Department of Defense has adopted the BUILDER application which will act as a single enterprise solution to performing facility condition assessment on their 2.2 billion square foot facility footprint. Alternate methods to performing on-site facility condition assessments need to be investigated to reduce the amount of engineering or subject matter expert resources required for a facility inspection. This study proposes several alternate methods including (1) the use of remote sensing devices to collect facility component distress information, (2) the use of building occupants to assess non-equipment components within a facility, (3) the use of maintenance professionals to assess equipment components while performing preventative maintenance actions, and (4) the use of frequent pattern data mining techniques to make future condition assumptions on associated components.
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