Strategic Engineering Nov 2006 v2strategic.mit.edu/docs/designunderuncertaintyv2.pdfWhat is...
Transcript of Strategic Engineering Nov 2006 v2strategic.mit.edu/docs/designunderuncertaintyv2.pdfWhat is...
© Olivier de Weck, November 2006 Page 1
Strategic EngineeringStrategic Engineering
Olivier L. de Weck, Ph.D.
Associate Professor of Aeronautics & Astronautics and Engineering Systems
Designing Systems for an Uncertain FutureDesigning Systems for an Uncertain Future
November 7, 2006
© Olivier de Weck, November 2006 Page 2
Strategic Engineering Topics
Three main research areas:
System Design under Uncertainty
Product Platforms and Commonality
Interplanetary Space Logistics
Acknowledgments
Dr. Afreen Siddiqi, Matt Silver, Monica Giffin, Gergana Bounova
today’s topic
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Outline
Motivation
Personal Experience (F/A-18 Program 1991-1997)
Framing the Research
Meta-Controls Framework
(Potential) Unifying Methods
Non-Homogenous Markov Chains
Time-Expanded Decision Networks
Ongoing and Future Work
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What is Strategic Engineering?
Strategic Engineering is the process of architecting and designing complex systems and products in a way that deliberately accounts for future uncertainty and context in order to avoid lock-in and maximize lifecycle value.
The extension of strategic thinking to the design and operation of complex engineering systems
Warfare [Sun Tzu 500 A.D., Carl von Clausewitz (1780-1831)]
Business [Michael Porter 1979, Arnoldo Hax 1996, others]
Engineering [now]
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Why is it important?Many large-scale complex systems suffer from “lock-in”,the inability to change/switch to a better configuration (or technology) despite superior solutions being known
Nuclear reactor technology [Cowan 1990]Economic aspects of lock-in [Arthur 1989, Arrow 2000]Impediment to technological evolution [Utterback 1974+]Political economic context [Nelson & Winter 1982]Impact on innovation [Henderson 1990; Christenson 1997]Causes are still being debated [Liebowitz 1985]Network externalities are important [Katz 1997, Witt 1997]Trade-off between operating slightly inefficient fielded technologies and developing new technologies [Sarsfield 2001]Political and organizational inertia [Puffert 2003]Recent AA/TPP S.M. Thesis on lock-in [Silver 2005]*
* incoming ESD PhD candidate, Jan 2007
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Examples and ConsequencesCommunications Satellite Constellations [Chaize et al. 2003]
Iridium Bankruptcy [1999, $5B], Globalstar similar fateOversized System based on optimistic market forecasts, could not easily adapt capacity, service, footprint …We’ve studied previously how to deploy them in stages
Automotive Platforms [Suh et al. 2005]
General Motors, e.g. Epsilon Platform [2003-2013+] Challenge in adapting platform to changing requirements over 10-15 year life: stretching chassis, incorporate new powertrain technologies, rigid tooling, too many constraints O($10B) commitments for design, factory layout, tooling,…
NASA Launch Vehicles [Silver et al. 2005]
NASA Space Shuttle Program [1972-present]Original traffic model called for ~ 50 flights/yearActual flight rate much smaller, technical issues, long turnaround times, high fixed cost, ~$4 billion/year
gasoline
Iridium780 km
hybrid
STS
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Key Concept: Switching Cost & Risk
Switching Cost summarizes the difficulty of changing a system’s configuration or state after it has been designed or fielded for operational use
Switching time
Switching risk
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Personal History: F/A-18 ExperienceU.S. Navy Version C/D
fighter and attackaircraft carrier based3000 flight hours90 min average sortiemax 7.5g positive
(1987) Swiss Versioninterceptorland based5000 flight hours40 min average sortiemax 9.0g positivedifferent avionics
(1993)
“Redesign”
(Switch)Approach
Apply new operational usage spectrum to existing configurationFind those locations in the system which do not complyApply selective, prioritized local redesign one-element-at-a-timeExample: Center barrel, wing-carry-though bulkheads Al Ti
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F/A-18 Center Barrel SectionY488
Y470.5Y453
WingAttachment
74A324001
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F/A-18 Complex System ChangeF/A-18 System Level Drawing
OriginalChangeFuselage
Stiffened
Manufacturing Processes Changed
Flight ControlSoftware Changed
Gross Takeoff Weight
Increased
Center of Gravity Shifted
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F/A-18 Lessons LearnedChanges increased cost per aircraft by O(~$10M)
encountered “surprises” along the way
Changing a system after its initial design isoften required to accommodate new requirementsexpensive, and time-consuming if change was not anticipated in the original design
Change propagationsome changes are local and remain localother 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, others …
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Key Research Questions
What causes lock-in and how can it be avoided?
What are important exogenous uncertainties?
how can they be classified, modeled, considered during design, mitigated (=risks), or taken advantage of (=opportunities)?
What are components of switching costs?
How can switching costs be lowered?
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Meta-Controls View
UncertaintyModeling
DecisionModeling
System ChangeModeling
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Uncertainty Modeling
Domains:Operational Environment
- terrain, weather,…Market (Demand)
- customers, competition
Economic- interest rates, prices
Technology- disruptive
Regulatory- e.g. CAFÉ,
Political/Policy- Funding
TechniquesDiffusion Models
- GBMLattice Models
- binomial, trinomial…Scenario PlanningDelphi [RAND 1959]Times Series Forecasting
0 5 10 150.40.60.81
1.21.41.6
x 105
Time [years]
Dem
and
[Nus
ers]
Geometric Brownian Motion Model
GBM model, Δt = 1 month, Do = 50,000, μ = 8% p.a., σ = 40% p.a. – 3 scenarios are shown
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Decision Modeling
When to switch to a new system/configuration?
Real Options “in” Projects [de Neufville, others…]MAUA (Multi-Attribute Utility Analysis)Staged Development and DeploymentSpiral Approaches/Rapid PrototypingOptimization: Stochastic Programming, Dynamic Programming, Multiobjective, …Controls-inspired Approaches
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System Change ModelingTraditional Systems Engineering Methods (QFD, DSM,…)
Change Propagation Analysis [Clarkson, Eckert 2004]
System Architecting Principles, “Illities”Modularity, Flexibility, Scalability, Reconfigurability,…
Product/System Platforms [Meyer 1996, Simpson 2001 ..]
… all these attempt to address part of the problem, when do these methods apply, is there a unifying framework …?
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Is there a unifying framework?
Maybe
Non-Homogenous Markov Chains
Time-Expanded Decision Networks
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Markov Modeling - I
π n( )= π 0( )Φ 0,n( )= π 0( )Pn
In the most general case, for k operational states thereare k(k-1) reconfiguration states
π i =1
1− pi− i
p j− jij=1
k
∑ π j , j ≠ i
For n→∞:
π jj=1
k
∑ +p j− ji
1− p ji− jij=1
k
∑ π ji=1
k
∑ =1, i ≠ j
In the Time Homogeneous Case:
Operational State Reconfiguration State
P n( )= P ∀n
Ref: Afreen Siddiqi, PhD Thesis, 2006
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Markov Modeling - II
( ) ( ) ( ) ( ), 1 1 , 1m n P m P m P n m nΦ = + − ≤ −L
For many reconfigurable systems, the state transition probabilities can be assumed to be conditioned on some external time varying process:
In the Non - Homogeneous Case:
pi− j n( )= f u n( ),i, j( )i* = argmaxJ u,S( )pm−mj > pm−mk, J j > Jk > Jm
State selection can be based on how sharply the performances differ
Path of PSV across varying soil conditions
Drawbar pull as PSV travels over varying terrainOpportunity stuck on Mars (NASA/JPL)
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Application of NHMC – Meta-Controls
Emerging Technologies supporting Reconfigurability
‘Skate board’ chassisDrive-by-wireReconfigurable wheelsSMART carsFPGAs
Siddiqi A., de Weck O., Iagnemma K., “Reconfigurability in Planetary Surface Vehicles: Modeling Approaches and Case Study”, Journal of the British Interplanetary Society (JBIS), 59, 2006
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Time-Expanded Decision NetworksWe developed concept of time expanded decision networks, to formalize effect of lock-in and identify opportunities for flexibility
3.) Create a Time Expanded Network based on the Static Network
5) Modify System Configurations to exploit easier Switching
4.) Create Operational Scenarios, Evaluate Optimal Paths
1.) Design Set of feasible System Configurations
2.) Quantify Switching Costs and Create a Static Network
Silver M.R., de Weck O.R., “Time Expanded Decision Networks: A Framework for Designing Evolvable Complex Systems , Systems Engineering, SE-061003 (under review)
exit with optimal initialconfiguration and switching embedded
( , )SWC C B
A
B
C ( , )SWC A C
( , )SWC C A
( , )SWC B A
( , )SWC A B
( , )SWC B C
0=SWC 0=SWC
0=SWCStep 1Step 2
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Time-expanded Decision Networks (TDN)Expand static network in timeModel operations arcs and switching arcsChance Nodes and Decision NodesCost Elements identified
( )DC A
1( )F vC C D+
T1 T2 T3
1 2
7 8
13 14
3 4
9 10
15 16
5 6
11 12
17 18
S-19 Z-20
A
B
C
( , )SWC A B
( , )SWC A C
2( )F vC C D+ 3( )F vC C D+
( , )SWC A B
( , )SWC A C
Beginningof Lifecycle
Endof Lifecycle
T time periods
( )DC A
1( )F vC C D+
T1 T2 T3
1 2
7 8
13 14
3 4
9 10
15 16
5 6
11 12
17 18
S-19 Z-20
A
B
C
( , )SWC A B
( , )SWC A C
2( )F vC C D+ 3( )F vC C D+
( , )SWC A B
( , )SWC A C
Beginningof Lifecycle
Endof Lifecycle
T time periods
( )1
( )( , , )
1
T Fi Vi jLCi Di j
j
C C DC D T r C
r=
+= +
+∑
Design and Ops Costs
Switching Costs( ) [ ]( , , ) ( , ) ( , )SW O D RC A B N c N C B A C A B= × Δ + Δ
Step 3
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Scenarios and Path Optimization
Time Period Scenario T1 T2 T3 T4 Scenario Description
1 1 0 0 1 0 0 0 0
Low Moon; No Mars
5 5 0 0 2 0 0 0 0
High Moon; No Mars
1 1 0 0 3 0 0 1 1
Low Moon; Low Mars
3 3 0 0 4 0 0 1 1
High Moon; Low Mars
2 1 0 0 5 0 0 1 2
High-Low Moon; Low-High Mars
5 5 5 0 6 0 0 5 5
High Moon; Hi Mars
3 2 1 7 0 1 2 3
Fade from Moon to Mars
1 1 0 0 8 0 0 3 3
Low Moon; High Mars
0 0 0 0 9 0 0 1 1
No Moon; Low Mars
0 0 0 0 10 0 0 5 5
No Moon; High Mars
Step 4
Major advantage over traditional decision trees !
Step 5Evaluate optimal paths through TDN:
-acyclic network-topological ordering-reaching algorithm (find shortest path)
Method Scaling
2 2N C T= × × +# of nodes
# of paths
optimal path solvable in
# Tpaths C=
2( 1) ( 2)M T C C T= − × + × +
# of arcs
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Application of TDN to Launch VehiclesHow to choose launch vehicle configurations for space exploration when future traffic model is uncertainDemand at the campaign level vehicle demandEffort started under NASA funded Draper/MIT CER effort 2004-2005, continued development after that
4 initial configurations consideredPlot
Shortest Path; Discounted LCCOptimization
Discounted TEN
Time Expanded Network
Yearly Variable Cost/VehicleLV Cost
LV properties: Name, Capacity,
FRC, Shroud SizeLV Define
Schedule (IMLEO/yr)Traffic Model
Switching Cost Matrix; DDT&E
Cost; Fixed Cost, Rec. Cost;
Time Horizon Moon/Mars IMLEO Mission Schedule
Manual Input (Excel)
Step 1
Step 2
Step 3
Step 4
Step 5Plot
Shortest Path; Discounted LCCOptimization
Discounted TEN
Time Expanded Network
Yearly Variable Cost/VehicleLV Cost
LV properties: Name, Capacity,
FRC, Shroud SizeLV Define
Schedule (IMLEO/yr)Traffic Model
Switching Cost Matrix; DDT&E
Cost; Fixed Cost, Rec. Cost;
Time Horizon Moon/Mars IMLEO Mission Schedule
Manual Input (Excel)
Step 1
Step 2
Step 3
Step 4
Step 5
Model Development
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TDN Launch Case Study - ResultsInitially: only two launch vehicles are ever used
1:SDV-A(80mt) = large, 2:EELV+(62 mt)=smallNo switching during lifecycle observed (= “lock-in”)Idea: gradually reduce switching cost to:
see if/when switching will occurwhat switches are selected most often?how valuable is it?
T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
11 12
19 20
27 28
5 6
13 14
21 22
29 30
7 8
15 16
23 24
31 32
S-33 T-34
FR+VR(D )SC(A1,A2)
DEV
(A1)
T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
11 12
19 20
27 28
5 6
13 14
21 22
29 30
7 8
15 16
23 24
31 32
S-33 T-34
FR+VR(D )SC(A1,A2)
DEV
(A1)
0460088486718from 4
8900088486718from 3
8900460000from 2
8900460022850from 1
to 4to 3to 2to 1Switching
Costs
Switching cost matrix
SDV-A (80 mt)
EELV+ (62 mt)
© Olivier de Weck, November 2006 Page 26
TDN Launch Case Study – Results (cont.)T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
11 12
19 20
27 28
5 6
13 14
21 22
29 30
7 8
15 16
23 24
31 32
S-33 T -34
)
1
2
3
4T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
11 12
19 20
27 28
5 6
13 14
21 22
29 30
7 8
15 16
23 24
31 32
S-33 T -34
)
1
2
3
4T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
11 12
19 20
27 28
5 6
13 14
21 22
29 30
7 8
15 16
23 24
31 32
S-33 T -34
)
1
2
3
4T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
11 12
19 20
27 28
5 6
13 14
21 22
29 30
7 8
15 16
23 24
31 32
S-33 T -34
)
1
2
3
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T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
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27 28
5 6
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7 8
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S-33 T -34
)
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T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
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19 20
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5 6
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7 8
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)
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4T1 T2 T3 T4
1 2
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25 26
3 4
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5 6
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7 8
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S-33 T- 34
)
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T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
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19 20
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5 6
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21 22
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7 8
15 16
23 24
31 32
S-33 T -34
)
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4T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
11 12
19 20
27 28
5 6
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21 22
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7 8
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23 24
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S-33 T -34
)
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T1 T2 T3 T4
1 2
9 10
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25 26
3 4
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19 20
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5 6
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21 22
29 30
7 8
15 16
23 24
31 32
S- 33 T-34
)
1
2
3
4
SwitchingCost
CSW(3,1)
$4B
$1B
$2B
$3B
Scenario 8 Scenario 10 Scenario 7 Scenario 5T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
11 12
19 20
27 28
5 6
13 14
21 22
29 30
7 8
15 16
23 24
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S-33 T -34
)
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T1 T2 T3 T4
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T1 T2 T3 T4
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9 10
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25 26
3 4
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7 8
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S-33 T -34
)
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4T1 T2 T3 T4
1 2
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17 18
25 26
3 4
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13 14
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7 8
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S-33 T -34
)
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T1 T2 T3 T4
1 2
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25 26
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7 8
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S-33 T -34
T1 T2 T3 T4
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9 10
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25 26
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5 6
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7 8
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S-33 T -34
)
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4T1 T2 T3 T4
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3 4
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S-33 T -34
)
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T1 T2 T3 T4
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S-33 T -34
T1 T2 T3 T4
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S-33 T -34
)
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4T1 T2 T3 T4
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S-33 T -34
)
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T1 T2 T3 T4
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T1 T2 T3 T4
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T1 T2 T3 T4
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)
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T1 T2 T3 T4
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T1 T2 T3 T4
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)
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)
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T1 T2 T3 T4
1 2
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17 18
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3 4
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S-33 T- 34
T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
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19 20
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5 6
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7 8
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S-33 T- 34
)
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25 26
3 4
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5 6
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T1 T2 T3 T4
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9 10
17 18
25 26
3 4
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5 6
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)
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T1 T2 T3 T4
1 2
9 10
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25 26
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5 6
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7 8
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)
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T1 T2 T3 T4
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17 18
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3 4
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T1 T2 T3 T4
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9 10
17 18
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3 4
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5 6
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)
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1 2
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T1 T2 T3 T4
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5 6
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T1 T2 T3 T4
1 2
9 10
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3 4
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)
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T1 T2 T3 T4
1 2
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)
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T1 T2 T3 T4
1 2
9 10
17 18
25 26
3 4
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5 6
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7 8
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T1 T2 T3 T4
1 2
9 10
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25 26
3 4
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5 6
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7 8
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SwitchingCost
CSW(3,1)
$4B
$1B
$2B
$3B
Scenario 8 Scenario 10 Scenario 7 Scenario 5
As switching cost is reduced switching becomes more frequentScenario 8 most interestingMax savings $0.6B/$2.8BGuideline for embedding flexibility in 3:EELV+ and how much it is worth
Life Cycle Cost - Average and Sc. 08
15 16 17 18 19 20 21 22
SC_1000 SC_2000 SC_3000 SC_4000 SC_5000 SC_Normal
Switching Cost EELV(3) – SDV-A (1)
LCC
($B
illio
n)
Average
Scenario 08
redu
ce s
witc
hing
cos
t
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Real Options and TDN(Real) Options are “simply” switching cost reducersCan model calls or puts as separate configurationsNeed to find optimal exercise rules, since TDN finds shortest paths assuming perfect knowledge of futureOptimal balance between upfront cost, and later switch cost
cash in
buy stockhold stock
buy call hold call
buy otherinvestment hold
invest-ment
cash outexercise
callsell stock
Expiration
don’texercise
sell otherinvestment
European Call
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Degree of OutcomeUncertainty
RelativeSwitchingCosts
wirelesssensor
networks
highwayinfrastructure
(some) consumerproducts
communicationsatellites
automotiveplatforms
System Landscape
water supplysystems
commercialaircraft
ΔC/LCC
σNPV
E[NPV]
volatileflexible
volatileinflexible
stableinflexible
stableflexible
“Lock-in” Quadrant
embedflexibility
facilitateswitching
designrobustly
avoidswitching
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Emerging PrinciplesFlexibility is a relative, not absolute system property.
There are two types of flexibility: operational (inner loop), configurational (outer loop).
Flexibility only makes sense in the context of specified uncertainty, which might lead to specific classes of changes in the future. General flexibility does not exist.
Fully reconfigurable systems are those systems that exhibit the highest degree of flexibility where the switching costs between a finite set of configurations (states) has been reduced to nearly zero.
Given an uncertain future operating environment, there exists anoptimal degree of flexibility that will balance reduction in switching costs with upfront system design/build complexity and ops cost.
Real options are switching cost reducers.
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Some Recent Relevant PublicationsSurvey Papers
de Neufville R., de Weck O., Frey D., Hastings D., Larson R., Simchi-Levi D., Oye K., Weigel A., Welsch A., et al, “Uncertainty Management for Engineering Systems Planning and Design”, Monograph, 1st Engineering Systems Symposium, M.I.T., March 29-31, 2004.
Haberfellner R., de Weck O.L., “Agile SYSTEMS ENGINEERING versus engineering AGILE SYSTEMS”, INCOSE 2005 - Systems Engineering Symposium, Rochester, NY, July 10-15, 2005
Research Papersde 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 (3), 119-136, March 2004
de Weck O.L., Suh E.S., “Flexible Product Platforms: Framework and Case Study”, DETC2006-99163, Proceedings of IDETC/CIE 2006 ASME 2006 International Design Engineering Technical Conferences, September 10-13, 2006, Philadelphia, Pennsylvania USA, submitted to Research in Engineering Design (in revision)
Siddiqi A., de Weck O., Iagnemma K., “Reconfigurability in Planetary Surface Vehicles: Modeling Approaches and Case Study”, Journal of the British Interplanetary Society (JBIS), 59, 2006
Hauser D., de Weck O.L., “Flexible Parts Manufacturing Systems: Framework and Case Study”, Journal of Intelligent Manufacturing, 2006 (accepted, to appear mid-2007)
Silver M., and de Weck O., “Time-Expanded Decision Network Methodology for Designing Evolvable Systems”, AIAA-2006-6964, 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Portsmouth, Virginia, 6 - 8 Sep 2006, submitted to Systems Engineering
© Olivier de Weck, November 2006 Page 31
Thesis ResearchMonica Giffin (SDM student, Raytheon), supported by Gergana Bounova (AA PhD candidate, networks)
Analyze Change Propagation Processes on an Actual ProgramComplex technical system at RaytheonChange and Configuration Management: regulate changes in systemWhat happens when one change unexpectedly causes another?
Database of 41,000 Requested Changes:~500 individuals named in requests~9 years project lifeDetailed design → implementation, test & sell-offChanges requested involved hardware, software, documentation
ApproachBuild underlying system mapMine database in an automated fashionCreate change propagation networks and compare to system mapFind change propagation patterns, multipliers, opportunities for flexibility, better architecture, better change management processes…?
Ongoing Work: Change Propagation
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Change Requests Written per Month
0
300
600
900
1200
1500
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93
Month
Num
ber W
ritte
n
Change Request Generation
[Eckert, Clarkson 2004]
Discovered new changepattern: “inverted ripple”
component design
subsystem design
systemintegrationand test
bug fixes
major milestonesor managementchanges
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Network plot of largest change network in the dataset, with 2579 associated change requests.Created by Gergana Bounova
Data from Monica Giffin (SDM)
Change Propagation Network
© Olivier de Weck, November 2006 Page 34
Future Work
Uncertainty Characterization (jump-diffusion = continuous change + discrete events superimposed)
Demonstrate benefits and challenges of reconfigurable systems [NSF Proposal ARES-CI, teaming with NU, Penn State, CMU]
Gather more data on real world large scale systems [e.g. MIT-Portugal program]
Change propagation in complex systems (Raytheon, others…)
Empirical/Field work on system evolution over time (a la Whitney Subway study)
More information on web: http://strategic.mit.edu