Cost Estimation for Risk: Are Contractors Really Overpricing?

How data-driven parametric price and cost estimation can mitigate the need for vendors to overcharge fixed-price contracts.

In a May 2023 report, CBS News uncovered repeated instances of defense contractors overcharging the Department of Defense (DoD). DoD would often negotiate fixed-price contracts with a reasonable 12% – 15% profit, but Pentagon analysts would later discover overcharges that boosted profit to 40% or more.

On May 24, 2023, five US Senators directed the DoD to investigate these findings in a letter to the Secretary of Defense. They noted that almost half of the FY 2024 budget “will go to private contractors, underlining the importance of reining in this out-of-control price gouging.†The senators linked the overcharging to the long-standing issue of the DoD’s inability to accurately audit their finances, leaving them vulnerable to the significant fraud reported by CBS.

Several issues are at play in these findings, including the inability of the DoD to perform audits, which could uncover the overpricing actions related to fixed-price contracts. Additionally, the ability to adequately negotiate fixed-price contracts, including a fair and sufficient pricing variance to counter the risks associated with a fixed-price offer, could help to ensure that both the government and contractors do not overestimate the cost of new DoD acquisitions.

Use of Parametric Cost Estimating to Mitigate Overpricing

Parametric cost estimating uses statistical relationships between historical costs and other variables. Parametric estimating is widely used as a quantitative approach to determine the expected cost (“should costâ€) based on historical data and is an established method in several project management frameworks, such as the Project Management Institute’s (PMI) Project Management Body of Knowledge (PMBOK). Parametric models, coupled with historical contract pricing data, can provide contract price negotiators with defensible, data-driven prices for various materials and services. For example:

• When a government buyer receives a proposal for goods or services, program analysts match the proposal details to historical price and technical data for completed purchases of similar goods and services. These completed purchases include any additional charges after the initial award of the historical contract, thus carrying with them a final price informed by variances in the amount ultimately paid to the vendor.
• Engineers, cost analysts, or pricing analysts then use the historical data to configure parametric models to estimate the likely “should cost†of the pending transaction. This uses predictive formulas discovered through regression analysis or other forecasting techniques, greatly increasing the accuracy of “should cost†models over historical comparisons alone.
• The key drivers for the cost estimating relationships reflect the data for similar completed transactions, providing a “should cost†range that includes variable prices, consistent with the estimated confidence levels of the model.
• Negotiators advocating a “should cost†price at the mean confidence level suggested by the data will be negotiating a price that balances the likelihood the price is too low (underpaying the vendor) with the likelihood the price is too high (overpaying the vendor).

Utilizing Parametric Cost Estimation Tools to Drive Better Value

A well-estimated “should cost†value, driven by historical data and supplemented with parametric cost models built using years of data and algorithmic development, will mitigate the need for vendors to submit additional charges to cover unforeseen costs not considered in the negotiation. These algorithms and the associated data are not a pipe dream and can be made available to both government pricing analysts, and contractor cost engineers to build better, smarter cost models for all future systems development. Commercial-Off-The-Shelf software lowers the barrier to entry for the use of parametric cost modeling into any organization’s budgeting process. Using these predictive tools, the government can potentially avoid contractor overpricing, and contractors can more easily and effectively build risk into FFP estimates to ensure the best possible price to the government.

Contributed by Unison

To learn more about how Unison Cost Engineering supports Government “Should Cost†efforts, please visit Unison Cost Engineering