Value-Driven Design 1 The VDD story (current edition) Magnitude of Losses Avoided by VDD –Reason for VDD Model of how losses occur How VDD avoids losses

Value-Driven Design 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.

Value-Driven Design 3 Time Performance designtestingproduction Cost +48% -5% Typical Cost Growth and Performance Erosion 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 net value initial performance limited by risk management Requirements Lost Value

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

Value-Driven Design 5 Engine Inlet StatusGradient Value Efficiency90%150,000135,000 Weight ,000 Manufacturing Cost Maintenance Cost Reliability ,450 Design Value$ 43,478 Maintainability ,652 Support Equipment Radar Cross-Section InfraRed Signature VDD Vision: Pervasive use of Optimization in Engineering Design Technical detail on distributed optimization can be found at What?

Value-Driven Design 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

Value-Driven Design 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 % might be typical loss

Value-Driven Design 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

Value-Driven Design 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 % loss on each attribute can explain 50% cost growth