Many asset intensive organizations face the perennial challenge of working with constrained budgets to achieve their asset management objectives. Quantification of the funding requirements for sustainment of their asset portfolios is often determined by a Capital Needs Assessment (CNA) that forecasts the unfunded liability facing the organization. The size of the gap between the organization’s means and its needs depends on several factors, including the age of the assets, the quality of maintenance, the local exposure conditions, and historical funding levels. Responsible stewardship of the organization’s funding gap requires a two-pronged strategy: the development of a compelling business case to advocate for additional funding and a prioritization scheme to defensibly support the annual cycles of appropriation of the limited resources to certain projects on certain assets.
This paper presents a criticality model and a case study of a Canadian university that implemented the model. The model draws upon a hybrid of statistical (quantitative) and empirical (qualitative) methods to achieve the means-and-needs analysis by evaluating the pre-existing estimate of the unfunded liability, determining whether a recalibration is required, and providing a prioritization schema to optimize distribution of the available funds. The statistical elements of the model reference a library of asset survivor curves to evaluate the local and global maxima for forecasting asset service life. This is coupled with facilitated workshops with the organization’s domain experts to elicit qualitative information to align the model to the institutions’ objectives such as on decision-making criteria and weightings for certain variables in the model. The output of the model is a recalibrated forecast of the unfunded liabilities that optimize the inevitable trade-offs that must be made in the face of a funding gap.
This paper was presented at the 2016 AMPeak Conference.