The VDD story (current edition)

1. The VDD story (current edition) • Magnitude of Losses Avoided by VDD • Reason for VDD • Model of how losses occur • How VDD avoids losses

2. Magnitude of Losses Avoided by VDD • Lost performance, Cost growth, and Schedule Delay cause between $10 billion and $100 billion annual loss of value on large aerospace programs • Use of requirements for extensive attributes rather than VDD appears to explain much of this loss • Extensive Attributes include performance, weight, all forms of cost, reliability, survivability, maintainability, etc.

3. Requirements Lost Value initial performance limited by risk management Typical Cost Growth and Performance Erosion design testing production -5% net value +48% Cost Performance Time Mean cost growth estimated at 51% by Augustine based on 1970’s and 1980’s DoD projects; estimated at 45% by CBO in 2004 based on NASA projects

4. How VDD avoids losses - Distributed Optimization If you design the best components, you will realize the best system If each component is optimized, the overall system will be optimized Aircraft Systems Wing Design Cockpit Design Propulsion Systems Landing Gear Systems Avionics Systems Turbine Design Propulsion Control System Turbine Blade Heads-Up Servovalve Temperature FADEC Radar Design Design Design Design Sensor Design Display Design

5. Gradient What? VDD Vision: Pervasive use of Optimizationin Engineering Design Engine Inlet Status Value Efficiency 90% 150,000 135,000 Weight 700 -130 -91,000 Reliability 1500 2.3 3,450 Maintainability -340 -2,652 7.8 Maintenance Cost 500 -0.5 -250 Support Equipment 12 -15 -180 -1200 Radar Cross-Section 0.1 -120 InfraRed Signature 1.4 -50 -70 Manufacturing Cost 700 -1 -700 Design Value $ 43,478 Technical detail on distributed optimization can be found at http://www.dfmconsulting.com/opt.pdf

6. Model of how losses occur • Story told in 3 parts: One component with one attribute Many components, but still just one attribute Many components with many attributes Model described in “Adverse Impact of Extensive Attribute Requirements on the Design of Complex Systems,” presented at ATIO 2007 www.dfmconsulting.com/reqts.pdf

7. One Component, One Attribute (Weight) • Optimization: choose the design with minimum weight • Requirements: choose the design with maximum chance of weight < requirement • For a sufficiently complex component, these two designs are almost always different, and have different weights • Therefore, requirements results in a heavier design than optimization • VDD enables optimization, therefore leads to lower weight The model suggests that 7 - 10% might be typical loss

8. Multiple Components, One Attribute (Weight) • With one component, requirements increased weight, but increased the probability that weight < requirement • Therefore, the probability distribution of weight is skewed • When there is a system weight requirement, it is budgeted across components, and they deliver skewed weight distributions • However, the system weight is the sum of the component weights, so by the Central Limit Theorem, the system weight is not so skewed • As a result, Requirements increases system weight AND decreases the probability of meeting System Requirement compared to VDD VDD improves the chance of meeting requirements

9. Multiple Components, Multiple Attributes • Initially (late in preliminary design and early in detailed design) all extensive attributes get worse • Design changes are made to trade the loss to the least valuable attributes • cost, in particular • Inherent design change cycles also result in very large schedule impact This is only a model; We need data to verify and quantify the phenomena, BUT 7 - 10% loss on each attribute can explain 50% cost growth