My student Major Sean Dorey (USAF), colleague Ricardo Valerdi and I were just announced as the winners of the 2012 Research Competition of the Defense Acquisition University for research Sean conducted last year under my supervision at LAI.
The award-winning paper that Sean co-authored with me and Ricardo Valerdi is titled “Enhancing cost realism through risk-driven contracting: Designing incentives fees based on probabilistic cost estimates”. You can download it here.
The research tackles one of the most persistent problems in managing large-scale engineering programs: How do you design monetary incentive structures that drive the right contractor behavior, without exposing them to unacceptable risk?
Simply speaking, the government has two main options: “Cost plus” contracts are pay-as-you-go contracts, where contractors bill engineering hours to the government. The government thus assumes all risks, mostly cost overrun risks, and the contractor is not necessarily incentivized for maximum efficiency and effectiveness. On the other extreme, in “fixed price contracts”, a specific deliverable is defined and a fixed amount of money paid. Now the contractor assumes all risks.
Both contract types are not ideal, as you want to create strong performance incentives, without exposing contractors to undue risks outside of their control. Excessive use of cost-plus contracts leads to large cost overruns, whereas excessive use of fixed price contracts would lead to an extreme risk aversion that would stifle innovation.
Sean’s approach takes two probability distributions as starting points for designing contracts that create the right mix of incentives and risk-balancing: a cost-probability distribution for the planned engineering program, as well as a fair profit probability distribution.
Developing cost probability distributions is a mature field that, with sufficient care, yields high quality results today.
Deriving fair profit probability distributions is even easier – look for example at rates of return of comparable companies and industries on the Dow Jones.
Sean’s contracting method now links the cost probability distribution to the profit probability distribution by an incentive fee curve: An incentive fee is designed that links each cost probability with its corresponding profit probability. If the 80th percentile cost for example is $100 million, and the 80th percentile profit (from high to low margin) is 1%, the incentive fee (or profit) for reaching $100 million cost is $1 million. If the contractor reaches the hypothetical 50th percentile cost of $90 million, and the 50th percentile profit is 6%, the incentive fee for achieving this level of performance would $5.4 million.
This method resolves several challenges:
- It eliminates the “risk of wishful thinking” during contracting, where contractors might be inclined to low-ball cost estimates to win contracts, and make up the difference with change orders.
- As these estimates would form the basis for the incentive fee and already include (some) risk of changing requirements, the higher the estimate, the more favorable the contract, but the less likely the chance to win it – a balancing feedback loop that creates an incentive to be as accurate as possible in the cost estimate.
Congratulations to Sean for a job well done!