Limitations and Opportunities in Air Force Risk Assessments

Limited funding for facilities construction and sustainment within the U.S. military requires leaders to carefully identify, prioritize, and authorize projects. As a result, organizations must establish a common basis for comparing projects across their entire portfolio, such as a risk assessment.

By definition, risk is the product of probability and consequences. For the U.S. Air Force, facility risk is determined as a function of asset degradation (probability of failure) and facility importance (consequence of failure). And while the current risk assessment methodology is easily repeatable, it does invite uncertainty and bias due to subjectivity when assessing an asset’s condition and using risk matrices to prioritize facility importance.

The current methodology for risk assessment and project prioritization has the Air Force on track to have a $70 billion sustainment and repair backlog by 2047.

Potential improvements to the current system would ensure that resources are allocated to the highest risk projects first and ultimately benefit Air Force facility management practices. The suggested solutions are simple process updates that include increasing education, training, and data collection frequency, along with the use of a fuzzy logic model to eliminate the need for subjective data inputs.

RATING ASSET CONDITION

Currently, probability of failure is derived from the overall condition of a facility weighted by individual systems and asset conditions. Condition data are stored in BUILDER. Visual inspections provide direct condition ratings on a 1 to 100 point scale, where 100 is perfect condition and free from any defects, distresses, or signs of deterioration, and 1 is complete failure.

All assets are assessed on a five-year cycle. This allows for conditions to be tracked throughout their useful life. The Air Force’s consequence of failure score is valued as 60 percent of tactical Mission Dependency Index (MDI) and 40 percent of the project’s priority rank at the strategic level. MDI is a value that links facilities or assets to operational objectives to support risk-based decision making and give leadership a risk profile view.

Traditional categorical risk matrices translate “likelihood” and “severity” into corresponding risk priority levels. Risk matrices commonly use the basic properties of likelihood and severity, or variations such as probability and consequence of an event, to prioritize risks or aid in decision-making about accepting risk. MDI uses two primary survey responses as inputs: one, how fast would mission capabilities be impacted if the functional capabilities in a building were interrupted; and two, how difficult would it be for an installation to relocate or replicate functional capabilities if operations were interrupted?

Mission owners and facility occupants answer the questions to determine the risk to operations that each facility housed. Assets that received an MDI of less than or equal to 40 are reassigned a score between 1 and 40 based on Facility Activity Category grouping determined by the individual installation.

Because uncertainties exist in how condition data is collected and used to account for asset degradation, they must be considered and included in decision-making to ensure confidence in scoring decisions. The uncertainty in asset condition arises from the subjectivity of visual assessments and can propagate to other metrics like the prediction of asset degradation and overall expected service life. Deviations from asset deterioration rates result in assets degrading sooner than predicted, which requires repair and maintenance actions earlier in their budgeted or planned service. Uncertainty needs to be included with facility condition to relay possible variation in overall facility condition when using it as a parameter in project prioritization.

GETTING PRIORITIES RIGHT

Data-driven solutions are required to reliably assess risk in facility construction and sustainment projects. In the Air Force’s risk assessment model, uncertainty and bias diminish the objectivity of the method and provide a false sense of security that a data- driven solution is being employed. Steps should be taken to improve the data and methods used to calculate risk.

With these recommendations, the calculation of risk for facility projects within Air Force asset management would be improved. Ultimately, to implement these suggestions, continuing research is necessary for targeted and precise applications of these changes to provide accurate assessments to aid decision-makers.

Capt. Devin DePalmer, M.SAME, USAF, is Asset Management Instructor, Lt. Col. Justin Delorit, Ph.D., P.E., PMP, M.SAME, USAF, is Assistant Professor, and Christopher Chini, Ph.D., is Assistant Professor, Department of Systems Engineering & Management, Air Force Institute of Technology. They can be reached at devin.depalmer@afit.edu; justin.delorit@afit.edu; and christopher.chini@afit.edu.

Capt. Sarah Brown, M.SAME, USAF, is Chief, Operations Engineering, 99th Civil Engineer Squadron, Nellis AFB, Nev.; sarah.brown.41@us.af.mil.

[This article first published in the September-October 2021 issue of The Military Engineer.]