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

Status Gradient Value

Efficiency 90% 150,000 135,000Weight 700 -130 -91,000

Manufacturing Cost 700 -1 -700

Maintenance Cost 500 -0.5 -250

Reliability 1500 2.3 3,450

Design Value $ 43,478

Maintainability 7.8 -340 -2,652

Support Equipment 12 -15 -180Radar Cross-Section 0.1 -1200 -120

InfraRed Signature 1.4 -50 -70

VDD Vision: Pervasive use of Optimizationin Engineering Design

Technical detail on distributed optimization can be found at http://www.dfmconsulting.com/opt.pdf

What?

Value-Driven Design

5

Analysis

Evaluate

Definition

Design Variables(Length, Displacement)

Attributes (Weight, Eff., Cost)

Configuration

Value

Design Optimization

Value-Driven Design = Optimization

ImproveObjective Function Optimizer

Physical Models CAD System

Value-Driven Design

6

Staus Quo: Requirements Flowdown

Turbine Design

TurbineBlade

Design

Propulsion Control System

TemperatureSensor Design

FADECDesign

ServovalveDesign

Wing Design Cockpit Design

Avionics Systems

Radar Design Heads-UpDisplay Design

Landing Gear Systems

Aircraft Systems

Requirements Methodpromises 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

TurbineBlade

Design

Propulsion Control System

TemperatureSensor Design

FADECDesign

ServovalveDesign

Wing Design Cockpit Design

Avionics Systems

Radar Design Heads-UpDisplay 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

9

1 - Optimization Finds a Better Design

Requirements

< $30 M unit mfg cost

< 30,000 lbs. w

eight

Cost

Weight(0,0)

Best

Cost

Weight(0,0)

Increasing Score

Traditional Spec Method Optimal Design

Limit of Feasibility

Value-Driven Design

10

Brake Material + $11,000 - 90 lbs.Rudder - $10,000 + 190 lbs.

Net Impact + $ 1,000 + 100 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

Val

ue A

Value B

Conflicts: Folding in Attribute Space

Value-Driven Design

12

Rudder Weight Rudder Weight Rudder Weight

Requirement

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

Per

form

ance

design testing production

Cos

t

+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 Value Diminishing Returns(minimum)

F-22 160 30

JSF 30 60

Lower Bound Lost Value (2006 $ billions)

All estimates assume current performance = original promise

F-22

1985 today

# aircraft 750 178Unit cost $ 95 200 2006 $ milliondelay 10 years

JSF

1992 today

# aircraft 3,000 2,400Unit cost $ 44 60 2006 $ milliondelay 2 years

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

Uni

t Pro

fit

Horsepower Relia

bilit

y

x

z

z

z

Intake ManifoldWeight 6.0Cost 12.0Life 20000.0

Intake ValveWeight 0.1Cost 2.0Efficiency 0.9

Cylinder HeadWeight 0.5Cost 42.0Efficiency 0.9Life 10000.0

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:

x h z

z

x

Weightchassis

Weighttransmission

Weightengine

. . .

+

+= Weighttractor

component systemmodel

1 1

MTBF MTBFtractor component

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, vj for components j = 1 to n

such that when v y v y y j x x xj j * *

x x* An optimum point is where for all

xx*

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

x h z h z

h z

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* :

• O2 represents second order and higher terms that we can ignore in the vicinity of z*

• Without O2, the Taylor series is linear

Linearizing the Objective Function

h z

h z x J z z Ox h z

* *

* *2 h z

Value-Driven Design

23

Solving the Taylor Expansion

• is the gradient of

• Jh is the Jacobian Matrix of h:

x x x x1 2 3 4

, , , ,

x

z

x

z

x

z

x

zx

z

x

z

x

z

x

zx

z

x

z

x

z

x

z

x

z

x

z

x

z

x

z

p

p

p

m m m m

p

1

1

1

2

1

3

1

2

1

2

2

2

3

2

3

1

3

2

3

3

3

1

1 2 3

Value-Driven Design

24

Solving the Taylor Expansion

h z h zx

x

zz z

i

i

ji

m

j

p

j j

* *

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*:

h zx

x

zz

i

i

ji

m

jj

p

11

Linear objective functions have the property that can be maximized by maximizing each zj term or any group of zj terms independently

Value-Driven Design

25

Component Optimization

For a group of zj’s that correspond to a single component, we can relable them y1 though yn and determine the component objective function (in the vicinity of the preliminary design):

component

i

i

k xi

m

k

n

kx

x

yy

*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

Status Gradient Value

Efficiency 90% 150,000 135,000Weight 700 -130 -91,000

Manufacturing Cost 700 -1 -700

Maintenance Cost 500 -0.5 -250

Reliability 1500 2.3 3,450

Design Value $ 43,478

Maintainability 7.8 -340 -2,652

Support Equipment 12 -15 -180Radar Cross-Section 0.1 -1200 -120

InfraRed Signature 1.4 -50 -70

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