Strategic Engineering Designing Systems for an Uncertain Future

Olivier L. de Weck, 2006 Page 1

Strategic EngineeringDesigning Systems for an Uncertain Future

Olivier L. de [email protected]

Assistant Professor of Aeronautics & Astronautics and Engineering Systems

21st Century COE Program21st Century COE ProgramSystem design: Paradigm Shift from Intelligence to LifeSystem design: Paradigm Shift from Intelligence to Life

Keio UniversityKeio UniversityJune 10, 2006June 10, 2006

mailto:[email protected]


Motivation: Iridium Satellite System

Difficult to properly size capacity of large system Market assumptions can change when 7-8 years elapse

between conceptual design and fielding (1991-1998)

'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 Satellite0

20

40

60

80

100

120

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

YearM

illio

ns o

f sub

scrib

ers

US (forecast) US (actual)


Outline

Customization of the F/A-18 Aircraft Introduction to Strategic EngineeringResearch Projects:

Staged Deployment of Satellite Constellations Flexible Automotive Product Platforms

Time Expanded Decision Networks (TDN)Engineering Education


Customization of the F/A-18 Aircraft


Mission (and Configuration) Change

Standard U.S. Navy F/A-18 C/D Configuration

U.S. Navy Missionfighter and attackaircraft carrier based3000 flight hours90 min average sortiemax 7.5g positive ~15 year useful life

(1978)

Modified Swiss F/A-18 C/D Configuration

Swiss Missioninterceptorland based5000 flight hours40 min average sortiemax 9.0g positive ~30 year useful life

(1993)

“Redesign”

(Switch)


F/A-18 Redesign Strategy1. specify new Swiss mission usage spectrum2. apply new spectrum to existing U.S. Navy Configuration3. identify and prioritize “hot spots” that most need change4. redesign and implement local changes at “hot spots”


F/A-18 Wing Carry-Through Bulkheads


F/A-18 Center Barrel SectionY488

Y470.5Y453

WingAttachment

74A324001


F/A-18 Center Fuselage Buildup (1)


Center Barrel Change ConsequencesSubstitution from Aluminum to Titanium

Intended Consequence:- Increased fatigue life of individual components

from 3000 5000 hours 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

achieved

not expected orwanted


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


F/A-18 Lessons LearnedChanges 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 designChange 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


Introduction to Strategic Engineering


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 thisBut, 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


Traditional (Systems) EngineeringCustomerNeeds

Product SystemYtarget

SubsystemYtarget

ComponentsYtarget

Marketing

Systems Engineering

Subsystem Development

ComponentDesign

RequirementsDefinition Fielding/

Launch

ConceptualDesign

PreliminaryDesign

DetailedDesign

SystemSystemOperationOperation

ComponentTesting

SubsystemIntegration

FinalAssembly

SystemFunctional

Testing

ComponentsYactual

SubsystemYactual

SystemYactual

SystemValidation


Implicit Assumptions of TSEThe customer knows what his/her needs areThe requirements are known and time-invariantThe system or product can be designed as one

coherent whole and is built and deployed in one stepThere is only one system or product designed at onceThe system will operate in a stable environment as far

as regulations, technologies, demographics and usage patterns are concerned


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 gainedConstraints on capital expenditures and operating budgets frequently only

allow a “piecemeal” implementationOften multiple variants of a system must be designed and built, possibly

based on some common standardEnvironment 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)


Strategic EngineeringStrategic 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.


Strategic Engineering Framework

Time

Development Operations

(Stage 1)

- CDI – - Operate - – RDI – – Operate -

BaselineSystem

BaselineSystem

Development Operations

Space

Variant C

Variant B

…

Variant B

Variant C

Gen 2Baseline

Gen 2Baseline

…

Variant B2

Variant C2

…

…

Variant B2

Variant C2

…

(Stage 2)


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 optimal on `average’ across all future scenarios protected against the worst case scenario take advantage of the `best case’ scenario most flexible to adapt to any scenario

Interested in how to do 3. Strategic Engineering

robust

risk averseopportunistic

flexible


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 …?


de Weck Research Approach

Non-dimensionallifecycle analysis

Generic SystemModeling (OPM)

ComparativeAnalysis

Iridium and Globalstar: Staged

Deployment

applicationapplication

GM: Flexible Automotive

Product Platforms

Meta-platformingTime-expandeddecision networks

BP: Exploration& Production

Standardization

NASA: Launch Vehicle Selection

& Evolution

ARM: Hydrogen Enhanced

Combustion Engine

BP: CommercialOffice Building

Staging

NASA: Inter-planetary Supply Chain & Logistics

Generic LifecycleCost Modeling

DARPA/AFRL: Space Tug

Mission Scenarios

theory


Staged Deployment of Satellite Constellations

Funded by Alfred P. Sloan FoundationReference

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


Design (Input) Vector X

Constellation Type: C

Orbital Altitude: h

Minimum Elevation Angle: min

Satellite Transmit Power: Pt

Antenna Size: Da

Multiple Access Scheme MA:

Network Architecture: ISL

Design SpacePolar, Walker

500,1000,1500,2000 [km]

2.5,7.5,12.5 [deg]

200,400,800,1600,2400 [W]

1.0,2.0,3.0 [m]

MF-TDMA, MF-CDMA [-]

yes, no [-]

This results in a 1440full factorial, combinatorial

co design space

Astro-dynamics

SatelliteDesign

C: 'walker' h: 2000 emin: 12.5000 Pt: 2400 DA: 3 MA: 'MFCD' ISL: 0

X1440=

Network


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


Multidisciplinary Simulator Structure

Constellation

SatelliteNetwork

LinkBudget

Spacecraft CostLaunchModule

Capacity

InputVector

ConstantsVector

OutputVector

x p

J

satm

Note: Only partial input-output relationships shown

min,h

,T p nGWspotn

sR sCLCC

, ,t aP D MA

ISL

satm

LV

satm Satellite MassT Number of Satellitesp Number of orbital planes

spotn Number of spot beamsnGW Number of gatewaysLV Launch vehicle selection


Governing Equations – Satellite Simulator

a) Physics-Based Models

b r t

0 space add. sys.

E PG GN kL L T R

Energy per bit over noise ratio:

(Link Budget)

b) Empirical Models

(Spacecraft)

0.5138 0.14sat t propm P m

Scaling modelsderived 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


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.

103 104 105 106 107100

101

Global Capacity Cs [# of duplex channels]

Life

cycl

e C

ost [

B$

FY

200

2]

Iridium simulatedIridium actual

Globalstar simulatedGlobalstar actual

Pareto Front

undercap

If demand is over the capacity, market opportunity may be missed

( )dD 1Df

Demand distributionProbability density function

0 ( ) for all Df D D

( )db

Da

P a D b f D

If actual demand is below capacity, there is a waste

waste


Staged DeploymentAdapt 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


Step 1: Partition the Design VectorConstellation Type: C

Orbital Altitude: h

Minimum Elevation Angle: min

Satellite Transmit Power: Pt

Antenna Size: Da

Multiple Access Scheme MA:

Network Architecture: ISL

Astro-dynamics

SatelliteDesign

C: 'walker' h: 2000 emin: 12.5000 Pt: 200 W DA: 1.5 m MA: 'MFCD' ISL: 1=yes

Network

xflexible

xbase

Rationale:Keep satellitesthe same andchange onlyarrangement

in space

Stage I C: 'polar' h: 1000 emin: 7.5000 Pt: 200 W DA: 1.5 m MA: 'MFCD' ISL: 1=yes

Stage II

xIbase xII

base=


Step 2: Search Paths in the Trade Space

Constant:Pt=200 WDA=1.5 mISL= Yes

Life

cycl

e co

st [B

$]

System capacity

h= 2000 km= 5 degNsats=24

h= 800 km= 5 degNsats=54 h= 400 km

= 5 degNsats=112



family

Total: 40 Paths


Step 3a: Model Uncertainty [GBM]

Demand can go up or down between two decision points Infinitely many scenarios can be generated based on this model

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, t = 1 month, Do = 50,000, = 8% p.a., = 40% p.a. – 3 scenarios are shown

D - demandt – time period

- SND random variable - constants

D t tD

DE tD

2var D tD


Step 3b: Binomial Lattice Model

p

1-pSample scenario

1

t

u t

u ed u

e dpu d

DiscretizedRandomWalk

Total

25=32scenariosp

(1-p)

p

( ) 1 n kkP i p p


Step 4: Calculate cost of paths We compute the costs of a

path with respect to each demand scenario

We then look at the weighted average of every allowable path for cost over all scenarios

Decision rule: We always adapt to demand when demand exceeds capacity

The costs are discounted: the present value of LCC is considered

Costs

Initial deployment

Cap1

Cap2

Deploy

2nd stage

wait waitwait


Step 5: Identify optimal path

102

103

104

100

101

Capac ity [thousands of users]

Syst

em L

ifecy

cle

Cost

[B$]

1.36

2.01

Best Path

A1

A2

A3

A4

For a given targeted capacity, we compare our solution to the traditional approach

Our approach allows large savings (30% on average)

Traditional designLCC of rigid design

E[LCC]=$650 millionvalue of real option

E [LCC(pathj)*]= Best Deployment Strategy

1

( )j

ni

j i pathi

E LCC path p LCC scenario


Takeaway from Satellite Project

Stage A1Stage A121 satellites

3 planesh=2000 km


5 planesh=800 km


8 planesh=400 km


Flexible Automotive Product Platforms

sponsored by General Motors 2003-2005Suh E.S., de Weck O.L., Chang D., “Flexible Product

Platforms: Framework and Case Study”, Research in Engineering Design, submitted Nov.2, 2005


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?

Platform ~ 10-15 year life

Model 3-4 years Model 3-4 years Model 3-4 years


Typical Vehicle Architecture (Platform) – General Motors

• 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

Unique

Carryover Modified

Common

“Platform”


Change Propagation Analysis Design Automotive Platforms

to accommodate future changes in styling and demand of individual variants

Identify flexible elements Developed 7-step process

BIW Change Propagation Network

Key Design Variables

Body-in-White Platform


Embed Flexibility

Flexible Lower Rear Passenger Compartment

Flexible/Unique Upper Passenger Compartment

Common Lower Front Passenger Compartment

L48

H122W27

H50

Inflexible BIW Design Flexible BIW Design

Body Outer Panel

Body Inner Panel

Common

Unique

Flexible

Unique

Unique

Common

Unique

Critical Components (Example)

(Blanking)

*Assume it meets quality, manufacturing, and safety requirements


Cost of Design Alternatives

Design Inflexible BIW Flexible BIW

Component Fabrication Inflexible Flexible

BIW Assembly Line Inflexible Flexible

H122

L48

H5

W27

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

Above Belt Line Length Change Frequency Chart

Normalized Profit

.000

.006

.011

.017

.023

0

143

286

429

572

5.00 7.25 9.50 11.75 14.00

25,000 Trials 24,973 Displayed

Forecast: Profit Differenct (Inflexible - Flexible


Takeaway Automotive PlatformsProduct 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


Wrap-Up


Time-expanded Decision Networks

Period 1 Period 2 Period N

state node chance node decision node

wait

switch

wait

switch

…

start

end


Path Optimization in TDN

Period 1 Period 2 Period N

…

start

end

For each uncertain scenario, find the optimal path through the TDN

example max NPV, min LCC, …


Principles of Strategic EngineeringA rigid design will be optimal (max NPV) if future events unfold

exactly as forecastedA 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


Strategic Engineering MapDegree of NPVUncertainty

RelativeSwitchingCosts

FlexibleDesign

“we can adapt”

StrategicallyRedesign

“we are betting the farm”

RobustDesign

“we will be ok no matter what”

Optimize forExpected

Requirement

“we know what’s coming”

C/LCCr

E[NPV]


Degree of NPVUncertainty

RelativeSwitchingCosts

wirelesssensor

networks

highwayinfrastructure

consumerproducts

communicationsatellites

automotiveplatforms

Future Work: Where do various systems fall ?

water supplysystem

commercialaircraft

C/LCC

?

E[NPV]


The migration of strategic thinking

Warfare

Management

Engineering

Army Firm System/Product

~500 A.D.Sun Tzu The Art of War

Carl von Clausewitz (1780-1831)

since ~1960sMichael E. Porter

Competitive Strategy: Techniques for

Analyzing Industries and Competitors

since 2000?

target domain:


Last Slide

Engineering Education

Future Work Strategic Engineering in additional Industries Comparative and Non-dimensional Analysis

- Focus on TDN

Teaching Pedagogy Courses Dissemination Outlets

Eng. Systems Studies Satellite Constellations (exits) Automotive Platforms (new) Oil & Gas Exploration (new)

Active Learning City Planning Game (exists) Auto Market Simulator (new) Others (TBD)

OpenCourseWare http://ocw.mit.edu

Engineering Systems Learning Center http://i2i.mit.edu

16.810 (U) Eng. Design & Rapid Proto.

SDM Program ESD.34 Sys Architecture ESD.36 Sys Project Mgt

Graduate Courses 16.888 Multi Sys Des Opt ESD.71 Eng Sys Analysis

Others MIT Professional Institute Seminars, Workshops,

Age

19

25

35


Backup Charts


F/A-18 Change Propagation Network

Object Process Diagram (OPD)


Local Change to affect crack growthParis Law:

Metal Fatigue

8 10 12 14 16 18 20 220

0.1

0.2

0.3

0.4

0.5

Stress Amplitude [ksi]

Initi

al C

rack

Len

gth

a o [i

nch]

Isoperformance Curve: Requirement=CGL=Nc: 25000

Parameter Bounding Box

Performance Jz = Nc =25000 cycles to failure

2a

w=6”

Center Cracked

Panel

stress

0 0.5 1 1.5 2 2.5x 10

40

0.5

11.5

2

2.5

3

Load cycles N [-]

Critical Load Number of Cycles N

max

min R=0

Cra

ck le

ngth

a [i

nch]

mda C KdN

sec aK aw

C=4e-9m=3.5


F/A-18 Avionics Suite


Existing Big LEO SystemsIridium 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 CapacityGlobal Capacity Cs

1,100 duplex channels72,600 channels

2,500 duplex channels120,000 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

IndividualIridium Satellite

IndividualGlobalstar Satellite


Satellite System Economics

,1

,1

1100

365 24 60

T T

ops ii

T

s f ii

kI CCPF

C L

Lifecycle cost

Number of billable minutes

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 subscribersAverage user activity [min/y]Operational system life [y]

365 24 60min1.0

u u

sf

N ACL

CPFIkopsCsCfLuNuA

1,200 [min/y]uA

TNumerical Example:

0.20 [$/min]CPF

3 [B$]I 5 [%]k 300 [M$/y]opsC

100,000 [#ch]sC 63 10uN 0.068fL

15 [y]T

But with 50,000uN

12.02 [$/min]CPF Non-competitive


Strategic Building Architecture

Source: J. Fernandez , MIT

BP ExplorationHeadquarters, Aberdeen,

Scotland


BenchmarkingBenchmarking is the process of validating a simulation

by comparing the predicted response against reality.Benchmarking Result 1: Simultaneous channels of the

constellation

020,00040,00060,00080,000

100,000120,000140,000

1 2

Iridium and Globalstar

Num

ber o

f si

mul

tane

ous

chan

nels

of

the

cons

tella

tion

actual or planned

simulated

Iridium Globalstar

Benchmarking Result 3: Satellite mass

0.0200.0400.0600.0800.0

1,000.01,200.01,400.0

1 2 3 4

Iridium, Globalstar, Orbcomm, and SkyBridge

Sate

llite

mas

s (k

g)

actual or planned

simulated

Iridium Globalstar Orbcomm SkyBridge

Benchmarking Result 2: Lifecycle cost

0.00

1.00

2.00

3.00

4.00

5.00

6.00

1 2

Iridium and Globalstar

Life

cycl

e co

st (b

illio

n $)

actual or planned

simulated

Iridium Globalstar

Benchmarking Result 4: Number of satellites in the constellation

0

10

20

30

40

50

60

70

1 2 3 4

Iridium, Globalstars, Orbcomm, and SkyBridge

Num

ber o

f sat

ellit

es in

the

cons

tella

tion

actual or planned

simulated

Iridium Globalstar Orbcomm SkyBridge


Average Vehicle Models per Platform

0

1

2

3

4

5

6

2002 2003 2004 2005 2006 2007 2008 2009

Year

Mod

els/P

latfo

rm DCXFordHondaToyotaVW

Source: Price Waterhouse Coopers, 2003

Platform Leverage Increases

Documents

Strategic Engineering Designing Systems for an Uncertain Future