Value-Driven Design 1 1 February 2007 Value-Driven Design An Initiative to Move Systems Design from Requirements to Optimization

Value-Driven Design 2 Outline Value-Driven Design (VDD) –Who? –What? –Why? –How? –What’s up?

Value-Driven Design 3 The World’s Forum for Aerospace Leadership Who?

Value-Driven Design 4 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 5 Analysis Evaluate Definition Design Variables (Length, Displacement) Attributes (Weight, Eff., Cost) Configuration Value Design Optimization Value-Driven Design = Optimization Improve Objective Function Optimizer Physical Models CAD System

Value-Driven Design 6 Staus Quo: Requirements Flowdown 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 Requirements Method promises Functionality Propulsion Systems If each module meets its requirements, the overall system will meet its requirements

Value-Driven Design 7 VDD Vision: Distributed Optimal Design 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 8 Three Reasons for VDD 1 - Optimization finds a better design 2 - Preference conflicts lead to clear loss of value 3 - Requirements cause performance erosion on cost growth Why?

Value-Driven Design Optimization Finds a Better Design Requirements < $30 M unit mfg cost < 30,000 lbs. weight Cost Weight (0,0) Best Cost Weight (0,0) Increasing Score Traditional Spec MethodOptimal Design Limit of Feasibility

Value-Driven Design 10 Brake Material+ $11, lbs. Rudder- $10, lbs. Net Impact+ $ 1, lbs. Differences in revealed values within a design team lead to choices that, taken together, are clearly lose-lose 2 - Preference Conflicts Lead to Loss of Value

Value-Driven Design 11 Design Potential Distributed Optimal Design Requirements Method Value A Value B Conflicts: Folding in Attribute Space

Value-Driven Design 12 Rudder Weight Requirement Expectation Avoid Risk Prefer Risk Preliminary Design Requirements Allocation Detailed Design 3 - Requirements Cause Performance Erosion Targets cause performance erosion and cost growth

Value-Driven Design 13 Time Performance designtestingproduction Cost +44% -5% Typical Cost Growth and Performance Erosion Mean cost growth estimated at 43% 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 14 Lost Value on Large Air Platform Programs Constant ValueDiminishing Returns (minimum) F JSF30 60 Lower Bound Lost Value (2006 $ billions) All estimates assume current performance = original promise F today # aircraft Unit cost$ $ million delay10years JSF 1992today # aircraft3,0002,400 Unit cost$ $ million delay2years

Value-Driven Design 15 Distributed Optimal Design Extensive Variables Design Attribute Spaces Composition Function Objective Function Linearization and Decomposition How?

Value-Driven Design 16 Extensive Variables Composition Function Performance, Cost, and -ilities

Value-Driven Design 17 Coordinate Axes are Design Attributes Different Space for –Whole Product: x1, x2,... xm –Each Component: yk1, yk2,... ykn (describes component k) Super attribute space composed of all attributes of all components: = [y11, y12,... y21,... ypn] describes whole product; describes all components Unit Profit Horsepower Reliability  x  z  z  z Intake Manifold Weight6.0 Cost12.0 Life Intake Valve Weight0.1 Cost2.0 Efficiency0.9 Cylinder Head Weight0.5 Cost42.0 Efficiency0.9 Life Design Attribute Spaces

Value-Driven Design 18 For distributed optimization, –h is the composition function Extensive attributes in affect collectively –no other attributes matter for global optimization Example elements:   xhz   z  x Weight chassis Weight transmission Weight engine = Weight tractor  component  system model 11 MTBF tractorcomponent   The Composition Function

Value-Driven Design 19 Objective Function (Value Model) The objective function is for the whole system  x   We want local objective functions, v j for components j = 1 to n such that when     vyvyyjxxx jj   **      xx *  An optimum point is where for all  x  x * That is, when the components are optimized, the product is optimized

Value-Driven Design 20 Objective Function with Local Attributes Since value = and, then value, a function of local attributes This gives us global value in terms of local attributes, but does not give an independent objective function for each component For independence, we must linearize Thus each component has its own goal    x   xhz     hz     hz 

Value-Driven Design 21 Validity of Linearization Given smoothness of and h, the linear approximation is reasonable for small changes (< 10% of whole system value) near the preliminary design 

Value-Driven Design 22 Start with a reference design (preliminary design) with attributes x* and z* Generate the Taylor expansion of around z* : O 2 represents second order and higher terms that we can ignore in the vicinity of z* Without O 2, the Taylor series is linear Linearizing the Objective Function      hz         hz xJzzO x h z       * * * * 2    hz 

Value-Driven Design 23 Solving the Taylor Expansion is the gradient of J h is the Jacobian Matrix of h:       xxxx 1234,,,,                                        x z x z x z x z x z x z x z x z x z x z x z x z x z x z x z x z p p p mmmm p                               

Value-Driven Design 24 Solving the Taylor Expansion           hzhz x x z zz i i j i m j p jj              ** 11 Objective functions are used for ranking—they are not changed by the addition or subtraction of a constant. Thus, the expression above can be simplified by dropping all terms that use the constant z*:        hz x x z z i i j i m j j p             11 Linear objective functions have the property that can be maximized by maximizing each z j term or any group of z j terms independently 

Value-Driven Design 25 Component Optimization For a group of z j ’s that correspond to a single component, we can relable them y 1 though y n and determine the component objective function (in the vicinity of the preliminary design):      component i i k x i m k n k x x y y             * 11

Value-Driven Design 26 Value landscape in parameter space Value landscape in property space Analysis SearchEvaluate Definition Objective Function Optimizer $ Parameters (Length, Displ.) Properties (Weight, Eff., Cost) Configuration Value Physical Models Design Drawing “But you can’t DO that!”

Value-Driven Design 27 Component Design Value is Commensurate with System Design Value Partial Derivatives of the Objective Function Implementing Distributed Optimal Design 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

Value-Driven Design 28 Near Term VDD Activity Building a Research Community –Workshop at MIT 26 Apr 2007 –VDD advocacy at Lockheed Martin and Boeing –VDD advocacy at NASA, OSD, and NSF –Connected with AFIT, Georgia Tech, Illinois, MIT, Purdue, Stanford Dissemination –One session at ATIO 2006, two sessions at ATIO 2007 –Professional short course –Publish book (collection of papers) Department of Defense VDD Guidebook –The Systems Engineering office in the Office of the Secretary of Defense has requested prototype work, perhaps led by universities What’s up?

Value-Driven Design 29 Value-Driven Design - Conclusion By relying on optimization and abandoning quantitative requirements, we will design large systems with tens of $billions greater value