Strategic Engineering
Designing Systems for an Uncertain Future
21st Century COE Program
System design: Paradigm Shift from Intelligence to Life
Keio University
June 10, 2006
Olivier L. de Weck
deweck@mit.edu
Assistant Professor of Aeronautics &
Astronautics and Engineering Systems
Olivier L. de Weck, 2006
Page 1
Motivation: Iridium Satellite System
'Motorola unveils new concept for
global personal communications:
base is constellation of low-orbit
cellular satellites',
Motorola Press Release on Iridium,
London, 26 June 1990.
‘Last week, Iridium LLC filed for
bankruptcy-court protection. Lost
investments are estimated at $5
billion.’
Wall Street Journal, New York, 18
August 1999.
Iridium Satellite
Millions of subscribers
US (forecast)
US (actual)
120
100
80
60
40
20
0
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Year


Difficult to properly size capacity of large system
Market assumptions can change when 7-8 years elapse
between conceptual design and fielding (1991-1998)
Olivier L. de Weck, 2006
Page 2
Outline
 Customization
of the F/A-18 Aircraft
 Introduction to Strategic Engineering
 Research Projects:
 Staged Deployment of Satellite Constellations
 Flexible Automotive Product Platforms
 Time Expanded Decision Networks (TDN)
 Engineering Education
Olivier L. de Weck, 2006
Page 3
Customization of the F/A-18 Aircraft
Olivier L. de Weck, 2006
Page 4
Mission (and Configuration) Change
Swiss Mission (1993)
U.S. Navy Mission (1978)
fighter and attack
aircraft carrier based
3000 flight hours
90 min average sortie
max 7.5g positive
~15 year useful life
interceptor
land based
5000 flight hours
40 min average sortie
max 9.0g positive
~30 year useful life
“Redesign”
(Switch)
Standard U.S. Navy F/A-18
C/D Configuration
Modified Swiss
F/A-18 C/D Configuration
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Page 5
F/A-18 Redesign Strategy
1.
2.
3.
4.
specify new Swiss mission usage spectrum
apply new spectrum to existing U.S. Navy Configuration
identify and prioritize “hot spots” that most need change
redesign and implement local changes at “hot spots”
Olivier L. de Weck, 2006
Page 6
F/A-18 Wing Carry-Through Bulkheads
Olivier L. de Weck, 2006
Page 7
F/A-18 Center Barrel Section
Y453
Y470.5
Y488
Wing
Attachment
74A324001
Olivier L. de Weck, 2006
Page 8
F/A-18 Center Fuselage Buildup (1)
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Page 9
Center Barrel Change Consequences
 Substitution


from Aluminum to Titanium
Intended Consequence:
- Increased fatigue life of individual components
from 3000  5000 hours
achieved
Unintended Consequences:
- Increased aircraft empty weight by ~O(100) lbs
- Shifted C.G. of aircraft by ~ O(1) inch
- Stiffened fuselage (1st bending mode) ~(0.1) Hz
- Rendered manufacturing processes obsolete
not expected or
wanted
Olivier L. de Weck, 2006
Page 10
F/A-18 Complex System Change
F/A-18 System Level Drawing
Fuselage
Stiffened
Original
Change
Flight Control
Software Changed
Manufacturing
Processes
Changed
Center of Gravity
Shifted
Gross Takeoff
Weight
Increased
Olivier L. de Weck, 2006
Page 11
F/A-18 Lessons Learned
 Changes
increased cost per aircraft by O(~$10M)
 Changing a system after its initial design is
 often required to accommodate new requirements
 expensive, and time-consuming if change was not
anticipated in the original design
 Change propagation
 some changes are local and remain local
 other changes start local, but propagate through the
system in complex, unanticipated ways
 switching costs include: engineering redesign cost,
change in materials, manufacturing changes,
change in operational costs
Olivier L. de Weck, 2006
Page 12
Introduction to Strategic
Engineering
Olivier L. de Weck, 2006
Page 13
What about the Future ?
Typical Engineering Design Mindset:
 “Give me a set of requirements today, a timeline and a
budget and I will design and deliver the best possible
product/system/project for you by tomorrow.”
 90% of thinking and design effort is spent on this
 But, in essence, we are always forecasting:
 what customers will require in 18 months
 what capacity our facility will need in 3 years
 what variants we will produce in 8 years
 how many missions we will fly in 12 years
 What if our forecast is wrong? (it usually is)
 Perhaps system will function technically ….
 But system will not deliver optimal value, or
architectural “lock-in” occurs, or it will fail financially if
its configuration is not easily changed

Olivier L. de Weck, 2006
Page 14
Traditional (Systems) Engineering
System
Validation
Customer
Needs
Marketing
Requirements
Definition
Product System
Ytarget
System
Functional
Testing
Systems Engineering
Conceptual
Design
System
Yactual
Subsystem
Development
Subsystem
Ytarget
Component
Design
Preliminary
Design
Components
Ytarget
Detailed
Design
Fielding/
Launch
Final
Assembly
Subsystem
Yactual
Subsystem
Integration
Components
Yactual
System
Operation
Component
Testing
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Implicit Assumptions of TSE
 The
customer knows what his/her needs are
 The
requirements are known and time-invariant
 The
system or product can be designed as one
coherent whole and is built and deployed in one step
 There
is only one system or product designed at once
 The
system will operate in a stable environment as far
as regulations, technologies, demographics and
usage patterns are concerned
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But reality tells us that …
 Customer
knows some of his/her needs but not all
 The true requirements often change after the system is
fielded and experience is gained
 Constraints on capital expenditures and operating budgets
frequently only allow a “piecemeal” implementation
 Often multiple variants of a system must be designed and
built, possibly based on some common standard
 Environment is not static, but dynamic






macro economic/budgetary changes (e.g. prime interest rate)
regulatory changes (e.g. new CAFÉ standards)
new technologies emerge (e.g. hydrogen fuel cells for cars)
demographic shifts (e.g. aging population in Western nations)
changing customer preferences (e.g. weighting of fuel economy)
disruptive events (natural, man-made)
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Strategic Engineering
 Strategic
Engineering is the process of designing
systems and products in a way that deliberately accounts
for customization and future uncertainties such that their
lifecycle value is maximized.
Olivier L. de Weck, 2006
Page 18
Strategic Engineering Framework
- CDI –
- Operate -
– RDI –
– Operate -
Time
Baseline
System
Baseline
System
Variant B
Variant B
Variant C
Variant C
Space
Variant B2
Variant B2
Variant C2
Variant C2
…
…
…
…
…
Development
Gen 2
Baseline
Gen 2
Baseline
Operations
(Stage 1)
Development
Operations
(Stage 2)
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Alternatives
1. Ignore the future and design for `optimal’ immediate
or short-term use (= TSE)
2. Come up with a `best guess’ of the most likely future
scenario and design to it (= forecasting + TSE)
3. Develop a range of potential future outcomes and
design such that the system will be
robust
 optimal on `average’ across all future scenarios
 protected against the worst case scenario risk averse
opportunistic
 take advantage of the `best case’ scenario
 most flexible to adapt to any scenario flexible
Interested in how to do 3.
Strategic Engineering
Olivier L. de Weck, 2006
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Strategic Engineering “Toolbox”
 Traditional
Systems Engineering Methods (QFD, DSM,…)
 Forecasting,
 Change Propagation Analysis
 System Architecting Principles, “Illities”
 Modularity, Flexibility, Scalability, Reconfigurability,…
 Real Options “in” Projects
 Standardization
 Product/System Platforms
 Staged Development and Deployment
 Optimization: Dynamic Programming, Multiobjective, …
… all these attempt to address part of the problem, when do these
methods apply, is there a unifying framework …?
Olivier L. de Weck, 2006
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de Weck Research Approach
theory
Non-dimensional
lifecycle analysis
Generic Lifecycle
Cost Modeling
Time-expanded
decision networks
Generic System
Modeling (OPM)
Meta-platforming
Comparative
Analysis
NASA: Launch
Vehicle Selection
& Evolution
NASA: Interplanetary Supply
Chain & Logistics
DARPA/AFRL:
Space Tug
Mission Scenarios
BP: Exploration
& Production
Standardization
Iridium and
Globalstar: Staged
Deployment
GM: Flexible
Automotive
Product Platforms
ARM: Hydrogen
Enhanced
Combustion Engine
BP: Commercial
Office Building
Staging
application
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Staged Deployment of Satellite
Constellations
 Funded
by Alfred P. Sloan Foundation
 Reference

de Weck, O.L., de Neufville R. and Chaize M., “Staged
Deployment of Communications Satellite Constellations in Low
Earth Orbit”, Journal of Aerospace Computing, Information, and
Communication, 1, 119-136, March 2004
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Page 23
Design (Input) Vector X
Design Space
Astrodynamics
Satellite
Design
Network
X1440=
Constellation Type: C
Polar, Walker
Orbital Altitude: h
500,1000,1500,2000
[km]
Minimum Elevation Angle: emin
2.5,7.5,12.5
[deg]
Satellite Transmit Power: Pt
200,400,800,1600,2400
[W]
Antenna Size: Da
1.0,2.0,3.0
[m]
Multiple Access Scheme MA:
MF-TDMA, MF-CDMA
[-]
Network Architecture: ISL
yes, no
[-]
C:
h:
emin:
Pt:
DA:
MA:
ISL:
'walker'
2000
12.5000
2400
3
'MFCD'
0
This results in a 1440
full factorial, combinatorial
co design space
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Objective Vector (Output) J


Performance (fixed)

Data Rate per Channel: R=4.8 [kbps]

Bit-Error Rate: pb=10-3

Link Fading Margin: 16 [dB]
Capacity


Cs: Number of simultaneous duplex channels
Cost

Lifecycle cost of the system (LCC [$]), includes:
-
Research, Development, Test and Evaluation (RDT&E)
-
Satellite Construction and Test
-
Launch and Orbital Insertion
-
Operations and Replenishment
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Multidisciplinary Simulator Structure
Constants
Input
p
x
Vector
Vector
msat
h, e min
Constellation
T, p
ISL
Spacecraft
nspot
Satellite
Network
msat
Launch
Module
LV
Cost
nGW
Link
Budget
LCC
Rs
Capacity
Cs
Pt , Da , MA
msat Satellite Mass
Number of Satellites
T
p Number of orbital planes
nspot Number of spot beams
nGW Number of gateways
LV Launch vehicle selection
Output
J
Vector
Note: Only partial input-output relationships shown
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Page 26
Governing Equations – Satellite Simulator
a) Physics-Based
Models
Energy per bit
over noise ratio:
Eb
PG r G t

N 0 kLspace Ladd.Tsys.R
(Link Budget)
b) Empirical
Models
msat  38  0.14 Pt  mprop 
0.51
(Spacecraft)
Scaling models
derived from
FCC database
Springmann P.N., and de Weck, O.L. ”A Parametric Scaling Model for Non-Geosynchronous
Communications Satellites”, Journal of Spacecraft and Rockets, May-June 2004
Olivier L. de Weck, 2006
Page 27
Traditional Systems Engineering
The traditional approach for designing a system considers
configurations (architectures) to be fixed over time.

Designers look for a Pareto Optimal solution in the Trade Space
given a targeted capacity.
If actual demand is below
Lifecycle Cost [B$ FY 2002]

capacity, there is a waste
If demand is over the capacity,
market opportunity may be missed
1
10
Iridium actual
Iridium simulated
Globalstar actual
Globalstar simulated
Demand distribution
Probability density function
Pareto
Front
b
P a  D  b   f D ( )dD
waste
a
0  f D ( D) for all D
under
cap

0
10
3
10
4
5
6
10
10
10
Global Capacity Cs [# of duplex channels]
7
10

f D ( )dD  1

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Page 28
Staged Deployment
 Adapt
to uncertain demand with a staged deployment
strategy:
 A smaller, more affordable system is initially built
 This system has the flexibility to increase its capacity
if demand is sufficient and if the decision makers can
afford additional capacity
 Economic Advantage
 Some capital investments are deferred to later
 The ability to reconfigure and deploy the next stage
is a real option
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Step 1: Partition the Design Vector
Constellation Type: C
xflexible
xbase
Astrodynamics Orbital Altitude: h
Minimum Elevation Angle: emin
Satellite Transmit Power: Pt
Satellite
Design Antenna Size: Da
Network
Multiple Access Scheme MA:
Network Architecture: ISL
Stage II
Stage I
C:
h:
emin:
Pt:
DA:
MA:
ISL:
Rationale:
Keep satellites
the same and
change only
arrangement
in space
'walker'
2000
12.5000
200 W
1.5 m
'MFCD'
1=yes
xIbase = xIIbase
C:
h:
emin:
Pt:
DA:
MA:
ISL:
Olivier L. de Weck, 2006
'polar'
1000
7.5000
200 W
1.5 m
'MFCD'
1=yes
Page 30
Step 2: Search Paths in the Trade Space
Lifecycle cost [B$]
h= 400 km
e= 35 deg
Nsats=1215
h= 2000 km
e= 5 deg
Nsats=24
family
h= 400 km
e= 20 deg
Nsats=416
Constant:
Pt=200 W
DA=1.5 m
h= 800 km
e= 5 deg
Nsats=54
ISL= Yes
h= 400 km
e= 5 deg
Nsats=112
Total: 40 Paths
System capacity
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Step 3a: Model Uncertainty [GBM]
D - demand
Dt – time period
e- SND random variable
m, s - constants
DD
 mDt  se Dt
D
 DD 
2
var 

s
Dt

 D 
5
x 10
Demand [Nusers]
 DD 
E
 mDt

 D 
1.6
1.4
1.2
Geometric Brownian Motion Model
GBM model, Dt = 1 month,
Do = 50,000, m = 8% p.a., s = 40% p.a.
– 3 scenarios are shown
1
0.8
0.6
0.4 0
5
Time [years]
10
15

Demand can go up or down between two decision points

Infinitely many scenarios can be generated based on this model
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Step 3b: Binomial Lattice Model
u  es
Dt
d 1 u
euDt  d
p
ud
Discretized
Random
Walk
Total
p
25=32
scenarios
p
p
Sample
scenario
1-p (1-p)
P(i)  p 1  p 
k
Olivier L. de Weck, 2006
n k
Page 33
Step 4: Calculate cost of paths
 We
compute the costs of a
path with respect to each
Cap2
demand scenario
 We
then look at the
weighted average of every
allowable path for cost over
all scenarios
rule: We always
adapt to demand when
demand exceeds capacity
Cap1
 Decision
Costs
 The
costs are discounted:
the present value of LCC is
considered
Initial wait Deploy wait wait
deployment 2nd stage
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Page 34
Step 5: Identify optimal path
System Lifecycle Cost [B$]
10
1

For a given targeted
capacity, we
compare our
solution to the
traditional approach

Our approach
allows large savings
(30% on average)
A4
A3
Best Path
LCC of rigid design
2.01
Traditional design
A2
1.36
10
E [LCC(pathj)*]= Best
Deployment Strategy
A1
0
10
2
10
3
10
E[DLCC]=$650 million
value of real option
4
Capacity [thousands of users]
n

E  LCC ( path j )    pi LCC scenarioipath j
i 1

Olivier L. de Weck, 2006
Page 35
Takeaway from Satellite Project
 Identified
best initial configuration, as well as potential
growth stages
Stage A1
21 satellites
3 planes
h=2000 km
Stage A2
50 satellites
5 planes
h=800 km
Stage A3
112 satellites
8 planes
h=400 km
Previous work focused on optimal coverage for static
requirements only, arrive at very different solution
 Requires extra upfront investment (e.g. extra fuel,
tunable antenna patterns), technical details remain

Olivier L. de Weck, 2006
Page 36
Flexible Automotive Product
Platforms
 sponsored
by General Motors 2003-2005
 Suh E.S., de Weck O.L., Chang D., “Flexible
Product Platforms: Framework and Case Study”,
Research in Engineering Design, submitted
Nov.2, 2005
Olivier L. de Weck, 2006
Page 37
Research Context & Questions
Sharp increase in number of models (variants) offered in the U.S.
automotive market [Detroit News, Jan 2005]:
 1947: 33
 1990: 198
 2009: 277 (estimate)
 Sales volumes per variant drop on average
 Market fragmentation
 Platform strategy adopted by most manufacturers
 Many uncertainties:
- Styling & performance preferences shifting, regulations, new
technologies  future sales volumes are uncertain
- How to design platforms to be flexible to respond to future
developments?

Model 3-4 years
Model 3-4 years
Model 3-4 years
Platform ~ 10-15 year life
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Page 38
Typical Vehicle Architecture (Platform) – General Motors
Unique
Carryover
Modified
Common
“Platform”
• Traditional product platform concept:
• Unique Elements: Variant-specific customized elements
• Common Elements: Commonly shared elements among product family
• Rise of new elements class
• Flexible (“Cousin”) Elements: Elements used (with modification) in more than
one variant to satisfy variant-specific requirement
Olivier L. de Weck, 2006
Page 39
Change Propagation Analysis
 Design
Automotive Platforms
to accommodate future
changes in styling and
demand of individual variants
 Identify
BIW Change Propagation Network
flexible elements
 Developed
7-step process
Body-in-White
Platform
Key Design Variables
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Page 40
Embed Flexibility
W27
*Assume it meets quality,
manufacturing, and safety
requirements
Flexible/Unique Upper
Passenger Compartment
H122
H50
L48
Flexible Lower Rear
Passenger Compartment
Common Lower Front
Passenger Compartment
Inflexible BIW Design
Critical Components
(Example)
Flexible BIW Design
Unique
Unique
Body Outer Panel
Common
Unique
Unique
Body Inner Panel
Common
Flexible
(Blanking)
Olivier L. de Weck, 2006
Page 41
Cost of Design Alternatives
Design
Inflexible BIW
Flexible BIW
Component Fabrication
Inflexible
Flexible
BIW Assembly Line
Inflexible
Flexible
Design
Inflexible BIW
Flexible BIW
Initial Investment (Line + Tooling)
100.00
134.17
Refurbish Cost (Every 5 Years)
10.58
17. 99
Switch Cost (Styling Only)
31.99
5.35
Switch Cost (Styling + Length)
42.33
5.51
Length Change
Above Belt Line
H122
H5
W27
Forecast: Profit Differenct (Inflexible - Flexible
25,000 T rial s
Frequency Chart
24,973 Displayed
.023
572
.017
429
.011
286
.006
143
L48
.000
0
5.00
7.25
9.50
11.75
14.00
Normalized Profi t
Olivier L. de Weck, 2006
Page 42
Takeaway Automotive Platforms
 Product





Platforms ….
“Bandwidth” can be increased by carefully
embedding flexibility in the design
Key is to propagate exogenous, functional
uncertainties into design variables and find
critical physical components
Critical components are those that are change
multipliers, or whose change would cause
large switching costs
Design for flexibility might cause larger upfront
investment and larger variable costs
Crossover between rigid and flexible design
as a f(uncertainty) typically occurs
Olivier L. de Weck, 2006
Page 43
Wrap-Up
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Page 44
Time-expanded Decision Networks
wait
wait
switch
switch
state node
chance node
decision node
start
end
…
Period 1
Period 2
Period N
Olivier L. de Weck, 2006
Page 45
Path Optimization in TDN
For each uncertain scenario, find the optimal path through the TDN
max NPV, min LCC, …
example
start
end
…
Period 1
Period 2
Period N
Olivier L. de Weck, 2006
Page 46
Principles of Strategic Engineering
A
rigid design will be optimal (max NPV) if future events
unfold exactly as forecasted
 A robust design can minimize the standard deviation of
outcomes (reduce risk), but will usually also lower the
expected NPV and max achievable NPV
 The larger the degree of uncertainty, the more valuable
flexibility will be. Flexible designs can increase the
E[NPV], while limiting downside and maximizing upside
 The larger the switching costs from one configuration to
another the more likely that the current system will be
 continued due to “architectural lock-in”, despite
operational sub-optimality
Olivier L. de Weck, 2006
Page 47
Strategic Engineering Map
Degree of NPV
Uncertainty
s
E[NPV]
“we are betting the farm”
Flexible
Design
Strategically
Redesign
“we can adapt”
Optimize for
Expected
Requirement
“we know what’s coming”
Robust
Design
“we will be ok no matter what”
Relative
Switching
Costs
DC/LCCr
Olivier L. de Weck, 2006
Page 48
Future Work: Where do various systems fall ?
Degree of NPV
Uncertainty
?
s
communication
satellites
E[NPV]
commercial
aircraft
wireless
sensor
networks
automotive
platforms
consumer
products
water supply
system
highway
infrastructure
Relative
Switching
Costs DC/LCC
Olivier L. de Weck, 2006
Page 49
The migration of strategic thinking
Warfare
~500 A.D.
Sun Tzu The Art of War
Carl von Clausewitz
(1780-1831)
Management
since ~1960s
Michael E. Porter
Competitive Strategy:
Techniques for
Analyzing Industries
and Competitors
Engineering
since 2000?
target domain:
Army
Firm
System/
Product
Olivier L. de Weck, 2006
Page 50
Last Slide

Engineering Education
Teaching Pedagogy
Eng. Systems Studies
16.810 (U)
Satellite Constellations (exits)
Automotive Platforms (new)
Oil & Gas Exploration (new)
Eng. Design & Rapid Proto.
Active Learning
City Planning Game (exists)
Auto Market Simulator (new)
Others (TBD)

Courses
Graduate Courses
16.888 Multi Sys Des Opt
ESD.71 Eng Sys Analysis
SDM Program
ESD.34 Sys Architecture
ESD.36 Sys Project Mgt
Age
Dissemination Outlets
19
OpenCourseWare
http://ocw.mit.edu
25
35
Engineering Systems
Learning Center
http://i2i.mit.edu
Others
MIT Professional Institute
Seminars, Workshops,
Future Work

Strategic Engineering in additional Industries

Comparative and Non-dimensional Analysis
-
Focus on TDN
Olivier L. de Weck, 2006
Page 51
Backup Charts
Olivier L. de Weck, 2006
Page 52
F/A-18 Change Propagation Network
Object Process Diagram (OPD)
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Page 53
Local Change to affect crack growth
smax
stress Ds
Isoperformance Curve: Requirement=CGL=Nc: 25000
Ds
smin
Parameter Bounding Box
0.5
R=0
Performance
Jz = Nc =25000
cycles to failure
0.4
Critical Load Number of Cycles N
Crack length a [inch]
3
w=6”
Initial Crack Length ao [inch]
Center
Cracked
Panel
2a
a 
DK  Ds  a  sec 

w


da
 C DK m
dN
Paris
Law:
Metal Fatigue
0.3
2.5
2
1.5
1
0.1
0.5
00
C=4e-9
m=3.5
0.2
0.5 1
1.5
Load cycles N [-]
2
2.5
x 10
4
0
8
10
12
14
16
Stress Amplitude D s
Olivier L. de Weck, 2006
18
[ksi]
20
22
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F/A-18 Avionics Suite
Olivier L. de Weck, 2006
Page 55
Existing Big LEO Systems
Iridium
Globalstar
Time of Launch
1997 – 1998
1998 – 1999
Number of Sats.
66
48
Constellation Formation
polar
Walker
Altitude (km)
780
1414
Sat. Mass (kg)
689
450
Transmitter Power (W)
400
380
Multiple Access Scheme
Multi-frequency –
Time Division Multiple
Access
Multi-frequency –
Code Division Multiple
Access
Single Satellite Capacity
Global Capacity Cs
1,100 duplex channels
72,600 channels
2,500 duplex channels
120,000 channels
Type of Service
voice and data
voice and data
Average Data Rate per
Channel
4.8 kbps
2.4/4.8/9.6 kbps
Total System Cost
$ 5.7 billion
$ 3.3 billion
Current Status
Bankrupt but in
operation
Bankrupt but in
operation
Individual
Iridium Satellite
Individual
Globalstar Satellite
Olivier L. de Weck, 2006
Page 56
Satellite System Economics
Lifecycle cost
T
CPF 
T
k 

I 1 
   Cops ,i
 100 
i 1
T
 C  365  24  60  L
i 1
s
f ,i
Number of billable minutes
Numerical Example:
I  3 [B$]
k  5 [%]
Cops  300 [M$/y]
T  15 [y]
Cs  100,000 [#ch]
Nu  3 106
Au  1,200 [min/y]
CPF
I
k
Cops
Cs
Lf
Nu
Au
T
CPF  0.20 [$/min]
L f  0.068
Cost per function [$/min]
Initial investment cost [$]
Yearly interest rate [%]
Yearly operations cost [$/y]
Global instant capacity [#ch]
Average load factor [0…1]
Number of subscribers
Average user activity [min/y]
Operational system life [y]
Nu  Au


L f  min  365  24  60  Cs

1.0

But with Nu  50,000
CPF  12.02 [$/min]
Non-competitive
Olivier L. de Weck, 2006
Page 57
Strategic Building Architecture
BP Exploration
Headquarters, Aberdeen,
Scotland
Source: J. Fernandez , MIT
Olivier L. de Weck, 2006
Page 58
Benchmarking
Benchmarking is the process of validating a simulation
by comparing the predicted response against reality.
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
Benchmarking Result 2: Lifecycle cost
Lifecycle cost (billion $)
Number of
simultaneous channels
of the constellation
Benchmarking Result 1: Simultaneous channels of the
constellation
actual or planned
simulated
1 Iridium
6.00
5.00
4.00
actual or planned
3.00
simulated
2.00
1.00
0.00
2 Globalstar
1 Iridium
Iridium and Globalstar
Iridium and Globalstar
Benchmarking Result 4: Number of satellites in the constellation
actual or planned
simulated
Iridium
1
Globalstar
2
Orbcomm
3
SkyBridge
4
Iridium , Globalstar, Orbcom m , and
SkyBridge
Number of satellites in the
constellation
Satellite mass (kg)
Benchmarking Result 3: Satellite mass
1,400.0
1,200.0
1,000.0
800.0
600.0
400.0
200.0
0.0
2 Globalstar
70
60
50
40
actual or planned
30
simulated
20
10
0
Iridium
1
Globalstar
2
Orbcomm
3
SkyBridge
4
Iridium , Globalstars, Orbcom m , and SkyBridge
Olivier L. de Weck, 2006
Page 59
Platform Leverage Increases
Average Vehicle Models per Platform
Models/Platform
6
5
DCX
4
Ford
3
Honda
2
Toyota
VW
1
0
2002 2003 2004 2005 2006 2007 2008 2009
Year
Source: Price Waterhouse Coopers, 2003
Olivier L. de Weck, 2006
Page 60