Value-Driven Design
Value-Driven Design
An Initiative to Move Systems Design
from Requirements to Optimization
1 February 2007
1
Value-Driven Design
Outline
• Value-Driven Design (VDD)
– Who?
– What?
– Why?
– How?
– What’s up?
2
Value-Driven Design
Who?
The World’s Forum for Aerospace Leadership
3
Value-Driven Design
What?
VDD Vision: Pervasive use of Optimization
in Engineering Design
Engine Inlet
Efficiency
Weight
Reliability
Maintainability
Maintenance Cost
Support Equipment
Radar Cross-Section
InfraRed Signature
Manufacturing Cost
Design Value
Status
Gradient
Value
90%
700
1500
7.8
500
12
0.1
1.4
700
150,000
-130
2.3
-340
-0.5
-15
-1200
135,000
-91,000
3,450
-2,652
-250
-180
-120
-70
-700
-50
-1
$ 43,478
Technical detail on distributed optimization can be found at http://www.dfmconsulting.com/opt.pdf
4
Value-Driven Design
Value-Driven Design = Optimization
Value
Evaluate
Optimizer
Objective Function
Attributes
(Weight, Eff., Cost)
Improve
Design
Optimization
Design Variables
(Length, Displacement)
Definition
Analysis
CAD System
Physical Models
Configuration
5
Value-Driven Design
Staus Quo: Requirements Flowdown
If each module
meets its requirements,
the overall system will
meet its requirements
Requirements Method
promises Functionality
Aircraft Systems
Wing Design
Propulsion Systems
Cockpit Design
Landing Gear Systems
Turbine Design
Turbine
Blade
Design
Propulsion Control System
Temperature
Sensor Design
FADEC
Design
Servovalve
Design
Avionics Systems
Radar Design
Heads-Up
Display Design
6
Value-Driven Design
VDD Vision: Distributed Optimal Design
If each component
is optimized,
the overall system
will be optimized
Aircraft Systems
Wing Design
Propulsion Systems
If you design
the best components,
you will realize
the best system
Cockpit Design
Landing Gear Systems
Turbine Design
Turbine
Blade
Design
Propulsion Control System
Temperature
Sensor Design
FADEC
Design
Servovalve
Design
Avionics Systems
Radar Design
Heads-Up
Display Design
7
Value-Driven Design
Why?
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
8
Value-Driven Design
1 - Optimization Finds a Better Design
Requirements
Increasing
Score
< $30 M unit mfg cost
< 30,000 lbs. weight
Cost
(0,0)
Weight
Traditional Spec Method
Cost
Limit of
Feasibility
Best
(0,0)
Weight
Optimal Design
9
Value-Driven Design
2 - Preference Conflicts Lead to Loss of Value
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
10
Value-Driven Design
Conflicts: Folding in Attribute Space
Value A
Design Potential
Requirements Method
Distributed Optimal Design
Value B
11
Value-Driven Design
3 - Requirements Cause Performance Erosion
Preliminary Design
Requirements Allocation
Detailed Design
Requirement
Expectation
Requirement
Avoid
Risk
Rudder Weight
Rudder Weight
Prefer
Risk
Rudder Weight
Targets cause performance erosion and cost growth
12
Value-Driven Design
Requirements
Typical Cost Growth and Performance Erosion
design
testing
production
-5%
net value
+44%
Cost
Performance
initial
performance
limited by risk
management
Lost Value
Time
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
13
Value-Driven Design
Lost Value on Large Air Platform Programs
Lower Bound Lost Value (2006 $ billions)
Constant Value
(minimum)
Diminishing Returns
F-22
160
30
JSF
30
60
All estimates assume current performance = original promise
F-22
1985
# aircraft
Unit cost
delay
$
750
95
JSF
today
178
200
10
2006 $ million
years
# aircraft
Unit cost
delay
1992
today
3,000
$ 44
2,400
60 2006 $ million
2 years
14
Value-Driven Design
How?
Distributed Optimal Design
• Extensive Variables
• Design Attribute Spaces
• Composition Function
• Objective Function
• Linearization and Decomposition
15
Value-Driven Design
Extensive Variables
Composition
Function
Performance,
Cost, and -ilities
16
Value-Driven Design
• Coordinate Axes are Design Attributes
• Different Space for
Unit Profit
Design Attribute Spaces
– Whole Product: x1, x2, ... xm
– Each Component: yk1, yk2, ... ykn (describes component k)
• Super attribute space composed of all attributes of all
r
Intake Manifold
components: z = [y11, y12, ... y21, ... ypn]
Weight
Cost
r
r
Life
• x describes whole product; z describes all
Intake Valve
components
Weight
r
z
6.0
12.0
20000.0
Cost
Efficiency
0.1
2.0
0.9
Cylinder Head
Weight
Cost
Efficiency
Life
0.5
42.0
0.9
10000.0
17
Value-Driven Design
The Composition Function
r = r
• For distributed optimization, x h (z)
– h is the composition function
r
r
• Extensive attributes in z affect x collectively
– no other attributes matter for global optimization
• Example elements:
...
Weightchassis
 component
+
Weightengine = Weighttractor
+
1
Weighttransmission
MTBFtractor
 system
model
=

1
MTBFcomponent
18
Value-Driven Design
Objective Function (Value Model)
The objective function is p (x ) for the whole system
r
()
r
An optimum point x * is where p xr *  p(xr ) for all xr
We want local objective functions, vj for components j = 1 to n
()
()
such that when v j yr *  v j (yr )"yr "j  p xr *  p(xr )"xr
That is, when the components are optimized, the product is optimized
19
Value-Driven Design
Objective Function with Local Attributes
r
r = r
p
(
)
• Since value = x and x h (z), then
r
=
p
value
(h(z)), 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
r
• For independence, we must linearize p(h(z))
• Thus each component has its own goal
20
Value-Driven Design
Validity of Linearization
Given smoothness of p and h, the linear
approximation is reasonable for small
changes (< 10% of whole system value)
near the preliminary design
21
Value-Driven Design
Linearizing the Objective Function
• Start with a reference design (preliminary
r
r
design) with attributes x* and z*
r
• Generate the Taylor expansion of p(h(z))
r
around z* :
(
)
r*
r
r
r r*
h
z
p
p(h(z)) = ( ( ))  p(x) r *  J h rz *  z  z  O 2
x
• O2 represents second order and higher
r terms
that we can ignore in the vicinity of z*
• Without O2, the Taylor series is linear
22
Value-Driven Design
Solving the Taylor Expansion
•
p
is the gradient of
 p p p p


,
,
,
, 

 x1 x 2 x 3 x 4

p
• Jh is the Jacobian Matrix of h:
 x1

 z1
 x 2
 z
 1
 x 3
 z
 1
 
 x m
 z
 1
x1
z 2
x 2
z 2
x 3
z 2

x m
z 2
x1
z 3
x 2
z 3
x 3
z 3

x m
z 3





x1 

z p 
x 2 
z p 
x 3 
z1p 

 
x m 
z p 
23
Value-Driven Design
Solving the Taylor Expansion
( ( ))
r
r*
p(h(z))  p h z 
 m p x i 
*


z
z



   x i z j  j j
j=1 i =1
p
(
)
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*:
r
p h(z) 
 m p x i 

z j



x
z


i
j


j=1 i =1
p
( )  
Linear objective functions have the property that p can be
maximized by maximizing each zj term or any group of zj terms
independently
24
Value-Driven Design
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):
p component
 m p x i
   

x i y k
k =1 i =1
n

y k
 * 
x
25
Value-Driven Design
“But you can’t DO that!”
Value
Evaluate
Objective Function
$
Search
Optimizer
Properties (Weight, Eff., Cost)
Parameters (Length, Displ.)
Analysis
Physical Models
Value landscape
in parameter space
Configuration
Definition
Design Drawing
Value landscape
in property space
26
Value-Driven Design
Implementing Distributed Optimal Design
Partial Derivatives
of the Objective
Function
Engine Inlet
Efficiency
Weight
Reliability
Maintainability
Maintenance Cost
Support Equipment
Radar Cross-Section
InfraRed Signature
Manufacturing Cost
Design Value
Status
Gradient
Value
90%
700
1500
7.8
500
12
0.1
1.4
700
150,000
-130
2.3
-340
-0.5
-15
-1200
135,000
-91,000
3,450
-2,652
-250
-180
-120
-70
-700
-50
-1
Component
Design Value is
Commensurate
with System
Design Value
$ 43,478
27
Value-Driven Design
What’s up?
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
28
Value-Driven Design
Value-Driven Design - Conclusion
By relying on optimization and abandoning
quantitative requirements, we will design large
systems with tens of $billions greater value
29