This document summarizes a presentation on using predictive analytics to determine fair and reasonable pricing. It discusses background on "should cost" pricing analysis used by the Department of Defense, different pricing methodologies including open market research, past data analysis, and parametric estimating. The presentation describes challenges pricing analysts face and case studies where parametric models were used to determine pricing for various components. It provides results of the case studies and lessons learned, recommending collaboration between pricers and cost analysts and the use of applications to make the pricing process easier. Next steps discussed are on-site proof of concepts and briefing pricing leadership.

1. JUNE 19 – 21, Bethesda, MD

2. Predictive Power of “Should Pricing”
Joe Bauer, Solutions Consultant
PRICE Systems, LLC

3. Agenda
 Background
 Pricing Methodologies
 Predictive Analytics
 Case Studies
 Lessons Learned
 Next Steps

4. Background
 “Should Price” analysis developed by DoD; embedded in FAR
 Assists contracting dept in determining fair & reasonable price
 Involves determining product price based on:
– Materials
– Labor
– Overhead
– Profit
– Escalation
– Competition
– Quantity purchased
– Market supply
– Market demand
– Obsolescence
– Mission requirements
– Other financial factors

5. Pricing Methodologies
 Three methods for determining fair and reasonable price:
– Open market research
– Data analysis of past procurement
– Parametric estimating using predictive analytics
“What are others paying?” “What have I paid before?” “What should I pay?”

6. Pricing Environment
 Pricers are contracting pros…not estimators or engineers
 “Fair and reasonable price” determination can be 30-45 days
 Workload ranges from paper clips to space ships
 Limited access to technical and programmatic data
 Overarching belief that government pays too much
 Any parametric model better be simple, fast and accurate!

7. Predictive Analytics
 Key consideration: Am I paying a
fair and reasonable price?
 Preferred method is market based
pricing (what others pay)
 Parametric estimating using
predictive analytics is a valid
alternative when market based info
is unavailable

8. Parametric Modeling
 Generate cost or price based on technical / programmatic
description of deliverable
What is it?
Material type?
Technology type?
How heavy?
How many?
Translate technical and
programmatic inputs
into cost model inputs

9. Case Studies
 Aircraft solenoid valve
 Oil pump gear
 Air data computer
 Auxiliary power unit (2 types)
 Inertial measurement unit
 Missile clock oscillator
 Aircraft memory server
 Data loading kit
 Diesel engine and shipping
container
 Aircraft engine component (2 types)

10. Results

11. Inputs Affected
 Labor rates
 Quantities
 Weights
 Technology
 Production line considerations
Average time per
case study ~
1 hour

12. Lessons Learned
 Pricer and coster parallels
– Same end goal
– Same challenges regarding data
– Obvious need for collaboration
 Applications exist to make your job easier
 There is an entire community of experts on your side

13. Next Steps
 Onsite “proof of concept”
– Wright-Patterson AFB (Dayton, OH)
– DCMA CIG (Indianapolis, IN)
 Capability briefing to pricing leadership
 Address nuances of pricing versus costing activities

14. Mr. Joe Bauer is a Solutions Consultant with
PRICE Systems. He is the primary technical
focal point for Air Force customers, several
defense contractors in the US, and
government agencies in Canada. Joe earned
a Master of Science degree in Cost Analysis
from the Air Force Institute of Technology in
2009. He earned an MBA in 2005. Joe is also
a Certified Cost Estimator / Analyst (CCEA)
with the International Cost Estimating and
Analysis Association (ICEAA).
Joe Bauer 937-258-7187
PRICE Systems, LLC. Joe.bauer2@pricesystems.com
Questions?