Abstract
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.
Highlights
Decision-makers have traditionally faced budgetary and workforce constraints that make it challenging to effectively maintain and repair buildings and infrastructure assets to ensure adequate performance
The results presented in the “Results” section illustrate the utility of a metric targeted to evaluate the technical performance of assets in order to augment, make, and validate manufacturer selection decisions
A performance-based metric should be incorporated as a criterion in the total cost of ownership assessments (Fig. 3)
Summary
Decision-makers have traditionally faced budgetary and workforce constraints that make it challenging to effectively maintain and repair buildings and infrastructure assets to ensure adequate performance. Infrastructure system life cycle costs have been estimated using TCO frameworks for facilities (Grussing 2014), roofing systems (Coffelt and Hendrickson 2010), stormwater systems (Forasté et al 2015), and pavements (Rehan et al 2018). These analyses provide an overview of the current body of knowledge regarding the use of life cycle cost evaluations for infrastructure systems and provide an excellent starting point to detail the costs associated with purchasing and operating infrastructure. Roda and Garetti (2014) aimed to fill this gap through the creation of a performance-driven TCO model for assets in the manufacturing industry (Roda et al 2020) This same methodology has not been applied to building systems or built infrastructure
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