How Predictive Cost Analysis Can Improve Asset Management

Imagine having the ability to accurately compare the costs of replacing an HVAC system now versus in a year. Consider the amount of money your organization could save using this kind of predictive cost analysis. That’s what agencies like the National Nuclear Security Administration are doing to better manage aging federal infrastructure.

In GovLoop’s recent webinar “Transforming Asset Management: Maximizing Your Budget With Predictive Data” attendees learned how NNSA is changing the way it quantifies and reports deferred maintenance and capital reinvestment needs. Incheol Pang, Program Manager of the BUILDER Sustainability Management System at NNSA’s Office of Infrastructure Planning and Analysis, shared how the agency is collaborating with industry to better manage its real property assets portfolio.

Pang was joined by David Lewek, Principal Consulting Engineer of RSMeans Data at Gordian, who shared how organizations like NNSA are taking advantage of the company’s asset management offerings.

Recently, the NNSA selected Gordian’s bid to update and maintain its BUILDER SMS database, which keeps track of condition and functionality assessments of the agency’s infrastructure assets. Pang said the NNSA worked with Gordian to map and configure RSMeans data (a predictive tool that uses more than 80,000 unit price line items to estimate costs) with the BUILDER database, and to enhance the catalog with NNSA-specific items.

BUILDER, a Defense Department-developed and government-owned system, projects entire lifecycles of each building component, from installation to failure point. Based on inspections, these data points can be adjusted to predict future conditions. “BUILDER provides unique opportunities for risk-based decision-making in [NNSA’s] facilities maintenance program,” Pang said.

He cited four challenges the NNSA looked to address with Gordian: an incomplete and inconsistent inventory, reactive facility maintenance practices (rather than preventative), the lack of a risk-based decision-making process and a lack of integration between databases.

Mike Bartoli, a member of Gordian’s Federal Solutions Team, explained that RSMeans data can be implemented in applications other than BUILDER. “You can really leverage that data in any other application where there’s a need in those systems to get more accurate costs,” he said.

At Gordian, the company uses four types of data to improve the effectiveness of construction-based predictive analysis, Lewek explained.

1. Economic indicators, like the S&P stock price index, new vehicle sales and commercial real estate price index.
2. Construction industry indicators, like crude oil prices, job openings in construction and the construction price of new single-family homes.
3. Material group indicators, like iron and steel exports and unfilled orders for primary metals.
4. Analytical information, like trends that tell how costs have changed in the past.

Lewek presented a boiler repair as an example. The RSMeans database shows how many years should pass between repairs or replacements, as well as up to 15 distinct tasks that would happen in a repair or replace scenario. Those items are included with labor hours, labor cost and material cost.

Construction cost data is simple in theory, Lewek said, but complex in application. The reason is so many market factors influence outcomes. To iron out its own strategy, Gordian has looked to how the health care, financial and technology industries have used predictive data analysis.

“Now we’re looking to the outside, to help shed more light for ourselves and for our customers,” Lewek said. “Hopefully we can support predictive maintenance, inform project timing and location and, ultimately, provide for better budgeting. That’s what we’re here for.”

He gave three reasons for using predictive technology and cost data:

1. Communicate compelling budget needs to decision makers. This is especially important for government agency employees, whose managers will be looking for solid cost benefits.
2. Improve visibility for real property asset risks. Agencies can keep a much tighter, more data-driven handle on the state of their infrastructures.
3. Deploy limited resources for the most impact. It’s important to make the most of what you have.