Technology Trajectories
Alstom Future Technical Leaders Program
Tuesday February 17, 2015
Prof. Olivier de Weck
deweck@mit.edu
1
Olivier de Weck
Professor of Aeronautics and Astronautics
and Engineering Systems
MIT School of Engineering
deweck@mit.edu
• Educational Background:
Dipl. Ing. in Industrial Engineering, ETH Zurich 1993
SM and PhD in Aerospace Systems Engineering, MIT, 2001
• Professional Experience: RUAG, McDonnell Douglas, NASA
• Research Focus: Strategic Engineering of Complex Systems:
How can they be designed with flexibility to evolve over time,
while exploiting commonality across projects?
• Work In: Aerospace Systems (NASA space exploration,
communications satellites), Automotive (GM), Oil & Gas Industry
(BP), Complex Electro-Mechanical Products (Xerox,…)
• Editor-in-Chief: Systems Engineering journal
2
3
3
Outline for this session
• Technological Progress
–
–
–
–
Functional Classification
S-Curve Model
Moore’s Law (Exponential Progress)
Pareto Shift Model
• Technology Trajectory / Scouting Exercise (45 min)
• Afternoon Break
• Technology Infusion Framework
– Delta Design Structure Matrix (DDSM)
– Delta Net Present Value (DNPV)
• Technology Infusion Exercise (45 min)
• Summary and Briefout
4
How to classify technologies?
An effective way to
classify technologies is
by the engineering
functions they perform:
Processes:
Transform, Transport,
Store
Operands:
Matter, Energy,
Information
Van Wyk, Rias J. 2004, Technology A Unifying Code, Stage Media
Group, Cape Town
3x3
Techno
Matrix
Transform
Process
Transport
Distribute
Store
House
Matter
Basic
Open
Furnace
(BOF)
Conveyor
Belt
High
Pressure
Tank
Energy
Info
Photovoltaic
Cells (PV)
Computer
Processor
Water
Cooled
Panel
Lithium
Ion
Polymer
Battery
(LiPo)
Internet
TCP/IP v4
Optical
Disk (CD)
5
Engineering Systems Classification
Broader Application to Functional Classification of Engineering Systems
Operand
Process
Matter
Energy
Transform
Process
Steel
Production
Wind Power
Generation
Transport
Distribute
Water
Management
Smart
Electric
Grids
Store
House
Money
Humans
Animals
Hospital
System
Scientific
Spacecraft
Air Transport
System
Batteries
Storage
Systems
Exchange
Trade
Control
Regulate
Information
Electronic
Medical
Records
Climate
Policy
Federal
Reserve
Senior Driver
Certification
adapted from Table 10
Magee C. and de Weck O. L., “Complex System Classification”, Fourteenth Annual International Symposium of the International
Council on Systems Engineering (INCOSE), Toulouse, France, June 20-24, 2004 – best paper award
6
Technological Progress – Example Steel Production
• Products and Processes improve over time due to three
main reasons
– Better exploitation (smarter operations)
– Incremental design improvements (optimization)
– Infusion and adoption of new technologies
Evolution of steel by process from 1955 to 1996. Adapted
from Fruehan.1 (Original source: International Iron and
Steel Institute.)
Infusion of new
technologies
Evolution of EAF performance from 1970 to 2000.
(Source: AISI Technology Roadmap.)
7
Progress in Aircraft Engines
UDF
CF6
Whittle
-
Better Materials (e.g. higher Temp limits)
Architectural Changes (1 stage  2 stage)
Optimization of Geometry (e.g. fan blades, BPR)
Etc …
8
GE90 Engine (on Boeing 777)
9
S-Curve
Pareto Shift
Time
Log (Perforamnce)
Time
Performance
Relative Value
Performance
Three Models of Technological Progress
Moore’s Law
Cost
Price
Time
10
S-Curve Model
• New technologies initially exhibit slow progress due to lack
of knowledge and experience
• Rapid progress in the middle of the lifecycle
• Slower progress at the end due to diminishing returns
é
ù
-t/t
1+
me
P(t) = a êb
+ cú
êë 1+ ne-t/t
úû
logistics growth function
1
P(t)
0.8
0.6
0.4
Logistics function with a=0.5, b=1,
c=1, m=-10, n=10, t=10
0.2
0
-50
0
50
100
Time t
Rogers, E. M., “Diffusion of Innovations”, The Free Press, A Division of Simon & Schuster Inc. 1st edition, 1962
11
Evidence for S-Curve for Solar Cells (NREL)
12
Moore’s Law (constant annual % progress)
Performance Relative to Year 1
100.00
Yearly improvement by a fixed % percentage leads to
exponential improvement over time
P(t) = Po (1+ p)
10% per year
t
15.86
10.00
baseline
4.31
5% per year
2.05
2.5% per year
1.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
13
Evidence for Moore’s Law?
Atomic
Quartz
Mechanical
Sun
14
Progress for Computation
Computing
MIPS = Million Instructions per Second
15
Progress for Energy Technologies
16
Notes on Technology Progress
• Koh, Heebyung, and Christopher L. Magee. "A functional
approach for studying technological progress: Extension
to energy technology." Technological Forecasting and
Social Change 75.6 (2008): 735-758.
• Key Observations
– Lower rates of progress for energy technology over the entire
period: 19–37% annually for Information Technology and 3–13%
for Energy Technology.
– Substantial variability of progress rates is found within given
functional categories for energy compared to relatively small
variation within any one category for information technology. The
strongest variation is found among capability progress among
different energy types.
– More challenging data recovery and metric definition for energy
as compared to information technology.
17
Technology Lifecycle Model
•
•
•
•
Classical Model for single technology is S-Curve Model
Progress in functions however is exponential (Moore’s Law)
Is there a conflict between the S-Model and Moore’s Law?
No ! Can be explained by switching to new technologies
New
Technology
performance
penetration
adoption
Old
Technology
time
Rogers, E. M., “Diffusion of Innovations”, The Free Press, A Division of
Simon & Schuster Inc. 1st edition, 1962
18
Annual Progress % for Energy Technologies
p
19
Technology affects multiple KPIs:
Pareto Front Shift
System objective J1 (e.g. cost)
Pareto
Shift
Baseline
Capabilities of
existing product
or process
C1
C2
new Pareto
Frontier
C4
C3
utopia
point
Objective
Space with
New Technology
Infused (concepts
C1…C4)
System objective J2 (e.g. emissions)
20
A bit of history: Vilfredo Pareto
•
Born in Paris in 1848 to a French Mother and Genovese
Father
Graduates from the University of Turin in 1870 with a
degree in Civil Engineering
•
– Thesis Title: “The Fundamental Principles of Equilibrium in
Solid Bodies”
•
While working in Florence as a Civil Engineer from 18701893, Pareto takes up the study of philosophy and politics
and is one of the first to analyze economic problems
with mathematical tools.
In 1893, Pareto becomes the Chair of Political Economy at
the University of Lausanne in Switzerland, where he
creates his two most famous theories:
•
– Circulation of the Elites
– The Pareto Optimum
21
• “The optimum allocation of the resources of a society is not
attained so long as it is possible to make at least one
individual better off in his own estimation while keeping
others as well off as before in their own estimation.”
• Reference: Pareto, V., Manuale di Economia Politica, Societa
Editrice Libraria, Milano, Italy, 1906. Translated into English by
A.S. Schwier as Manual of Political Economy, Macmillan, New
York, 1971.
21
Example: Jet Engines vs. Turboprops
1.2
Gulfstream G-II
Relative Value
1.1
Sabreliner 60
Sabreliner 40
Gulfstream G-I
Hawker 400
1.0
HFB Hansa Jet
Jetstar 6
Viscount 810
Falcon 20
Super Convair
Fairchild F-27
Fairchild FH 227
Dart Herald
Premise:
New Technologies Cause
Pareto-Front Shifts !
0.9
0.8
0.7
attribute weights used to calculate RVI:
Maximum Speed
0.20
Cabin Volume/Pax
0.09
Available Seat-Miles
0.10
Large Jets
Midsize Jets
Light Jets
Heavy Turboprops
Medium Turboprops
Customers want:
- better performance
- lower cost
- or a combination of
the two
0.6
0.0
0.5
1.0
1.5
2.0
2.5
1970 B/CA Base Price (US$, millions)
3.0
some prices CPI adjusted
Downen, T., “A Multi-Attribute Value Assessment Method for the Early Product Development Phase with Application to the Business Airplane Industry”, PhD
thesis, Engineering Systems Division, Massachusetts Institute of Technology, February 2005
22
How do the theories of technological
progress apply to Alstom?
•
•
•
•
•
•
•
Turnkey power plants (steam, gas, combined cycle, add-ons)
Carbon Capture and Storage (CCS)
Boilers- Turbo generators- Hydro generators- Air preheaters
Power plant chemistry
Power automation and control systems
Turbines (steam, gas, nuclear, industry as well as blades)
Environmental control systems- Services and maintenance for
thermal power plants built by Alstom or other OEM’s
Source: http://www.alstom.com/switzerland/products-and-services/power-generation/
23
Technology Scouting Exercise (45 min)
Question 1
What technologies are
key to Alstom today and
tomorrow?
Question 2
How can these be
classified in the 3x3?
Question 3
Pick 1-2 at your table
and apply the technology
models (S, Moore, Pareto)
Use Post-It Notes
Work alone for 5-10 min
Discuss with group ~ 20 min
Briefout for 15 min
3x3
Techno
Matrix
Matter
Energy
Info
Transform
Process
Transport
Distribute
Store
House
24
Afternoon Break
25
Technology Infusion Analysis (TIA)
• Some blockbuster products are clean sheet designs
– Even they typically reuse existing lower-level components
• However, most products and processes evolve from predecessors
– Infusion of new technologies over time  technology improvements
– Increase value of product or process to customers and firm
– Carefully manage resources and risks during R&D and transition of
technology to full scale production
• Need for a formal Technology Infusion Analysis
[Post-reading] Suh. E.S., Furst M.R., Mihalyov K.J, de Weck O., “Technology Infusion
for Complex Systems: A Framework and Case Study”, Systems Engineering, 13 (2),
186-203, Summer 2010
26
Position of TIA in R&D Cycle: Phase 2
PHASE 4
Selection Criteria
Superior, Flexible, Mature
and Robust Technologies
Technology Incorporated
into Product Program
Steps 16a,b to 18
Steps 19 and 20
TECHNOLOGY SELECTION
TECHNOLOGY TRANSFER AND
INTEGRATION
Robust Alternative System Architectures
Robust Alternative Hardware Concepts
Robust System Functions and Parameter Settings
System Failure Modes, Interrelations and Probabilities
PHASE 3
Steps 11a,b to 15a,b
System
Constraints &
Environment
ROBUSTNESS DEVELOPMENT
AND ANALYSIS
Pre-Robust Alternative Concepts
PHASE 2
Steps 7a,b and 10a,b
Selection Criteria
CONCEPT EVALUATION &
SELECTION
Critical System Functions and Parameter Settings
Critical Failure Modes, Interrelations and Probabilities
Alternative System Architectures
Alternative Hardware Concepts
System
Constraints &
Environment
Steps 6a,b and c
Steps 8a,b to 9
CONCEPT GENERATION
CONCEPT ANALYSIS &
ENHANCEMENT
PHASE 2A
PHASE 2B
Integrated Strategy Map
Strategic Houses of Quality
PHASE 1
Steps 1 to 5
INTEGRATED TECHNOLOGY
STRATEGY
Resources Allocation & Risk Evaluation
Selection Criteria
System Constraints & Environment
Business Strategy
Market and Product Program Data
Techology and Technological Capabilities Data
Schulz, A.P., Clausing D.P. , Fricke E., Negele H.,
“Development and Integration of Winning
Technologies as Key to Competitive Advantage”,
Systems Engineering, Vol. 3, No. 4, pp. 180-211, 2000
27
Why do companies care about
a Technology Infusion Analysis Process?
• Evolve products from earlier products to keep up or ahead of competition:
- Technology refreshes are desired but often developed as prototypes “in isolation”
- Alternatives will exist. Need to assess the invasiveness and effort associated with
alternatives as well as potential value of each early in exploration phase
- Budget constraints require prioritization.
• Many firms face these same challenges
- Can we define a reasonable, repeatable, and consistent approach?
- Can we assess options EARLY in the process?
- Can we compare several alternatives QUICKLY?
- Can we Rank or array alternatives based on early estimates of
- Non-Recurring Engineering Cost
- Customer Value, Internal Customer Value
- Market, Technology Uncertainty
- Can we identify integration problems sooner?
28
Technology Infusion Focus:
Cost, Performance, Value
•Three aspects of each alternative must be examined early in
development cycle:
– Cost/Effort and estimation uncertainty associated with
Technology Development and Infusion into a host product,
platform or process
– Effect on Performance that the technology has on product /
process functional attributes both internal and customer visible
– Customer reaction and uncertainty of the market to changes in
customer visible functional attributes
•Result: Expected value impact over time (in $’s)
– Technology driven ΔNPV (expected value, standard deviation)
29
Technology Infusion Framework
D
Step
2
DDSM
3
Technology
Infusion
Identification
Baseline
System DSM
Performance and
Cost Models
1
Baseline
Product
Value V(g)
5
TI
Effort 4
6
Modified
Product
Value
V(Dg)
D
7
E[DNPV]
Revenue
Impact
Risk-Return
Curve for
Technology
s[DNPV]
Technology
Infusion
Evaluation
Probabilistic
NPV Analysis
10
8
Cost
Impact
9
30
D
Step
DDSM
3
2
Industry Case Study
Xerox iGen3 Technology Infusion
Technology
Infusion
Identification
Baseline
System DSM
Performance and
Cost Models
1
Baseline
Product
Value V(g)
5
TI
Effort
6
Modified
Product
Value
V(Dg)
D
7
E[DNPV]
Revenue
Impact
Risk-Return
Curve for
Technology
s[DNPV]
Technology
Infusion
Evaluation
Probabilistic
NPV Analysis
8
Cost
Impact
10
9
ESVm
ROS
Charger
iGen3 Digital Printing Press
ESVy
Light
shock
lamp
ESB cleaner
ESVc
Pre-clean
ESVk
ATA
ETACs (3)
Pre-clean
MOB
Transfer
Fuser
Auto Density Correction
Technology
• High-end digital printing market is very competitive market space, where demand
for lower operating cost and superior image quality is key to product survival.
• To improve performance metrics mentioned above, an Auto Density Correction
Technology could be infused into current Xerox iGen3 Digital Production System.
• Is it worthwhile?
31
4
Design Structure Matrix (DSM) captures
detailed connectivity
Controller
Valve
Pump
Motor
Filter
Controller
1
7
1
Pump
Motor
Filter
Valve
Pump
Controller
Sample System
1
1
15
Valve
1
1 3
7 15
Filter
Motor
1
Number
Type
Flag
0
No Connection 0
1
Mechanical
1
2
Flow
3
3
Information
7
4
Energy
15
• DSM captures connectivity of
components => architecture
• Track different flows for
through the system
1 3
1
1
Key
15
DSM
See also DSM session by Prof. Steve Eppinger
• DSM provides analysis
capability not present in a
schematic alone
32
D
Step
iGen3 Baseline Design Structure Matrix (DSM)
“Step 1: Baseline System DSM”
DDSM
3
2
Technology
Infusion
Identification
Baseline
System DSM
Performance and
Cost Models
1
Baseline
Product
Value V(g)
5
TI
Effort
6
Modified
Product
Value
V(Dg)
D
E[DNPV]
Revenue
Impact
Risk-Return
Curve for
Technology
s[DNPV]
Technology
Infusion
Evaluation
Probabilistic
NPV Analysis
8
Cost
Impact
10
9
GUI
Feeder
Software
Print Engine
Image Path
Stacker
Print Engine
Media Path
Legend
1 Physical connection
Mass flow
2
Energy flow
3
Information flow
4
Key
p
t
a
o
d
HV
LV
5, …
m
h
Print Engine
Marking Path
Print Engine
Control Path
Print Engine
Frame
1 2
3 4
Paper
Toner
air (purified / ready for use)
Ozone
Dirt
High Voltage
Low Voltage
DC Voltage
Mechanical energy (translation, rotation, etc…)
Heat energy (Fuser only)
Base iGen DSM
Total number of DSM Elements
Total number of physical connections
Total number of mass flow connections
Total number of energy flow connections
Total number of information flow connections
Number of Base DSM cells
Number of non-empty cells
Sparsity (Nonzero Fraction NZF)
84
572
45
167
165
27972
1033
0.037
33
7
4
Infused Technology – ΔDSM captures Changes
Impact of Technology Infusion on Current System
Technology
ESVm
ROS
Charger
ESVy
Light
shock
lamp
ΔDSM
ESB cleaner
ESVc
1
1
2
Pre-clean
3
4
5
ESVk
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Pre-clean
4
B
4
4
B
4
4
1
B
1
1
9
4
1
10
4
1
11
4
1
12
Transfer
Fuser
27
B
4
8
ATA
26
4
7
MOB
25
B
4
6
ETACs (3)
24
B
4
1
13
4
1
14
4
1
15
4
1
16
4
17
15
84
17.86%
33
572
5.77%
0
45
0.00%
7
167
4.19%
32
87
165
19.39%
Total TII
8.42%
C
20
1
B
21
B
22
1
3
1
3
23
24
27
TI
1
19
26
Subtotal Base DSM
B
18
25
Count
5
1
9
20
0
13
0
0
0
3
0
4
17
0
15
87
Technology Invasiveness Index
New component/subsystem
Eliminated component/subsystem
Component redesign
New physical connection
Eliminated physical connection
Modified physical connection
New mass flow connection
Eliminated mass flow connection
Modified mass flow connection
New energy flow connection
Eliminated energy flow connection
Modified energy flow connection
New information flow connection
Eliminated information flow connection
Modified information flow connection
Total
Technology Invasiveness Index
1
1
4
1
4
1
4
1
4
1
4
1
4
1
4
1
4
A
4
1
3 4
1
3 4
A
1
1
1
3 4
A
4
4
3 4
1
3
1
1
A
1
1
A
4
TI is the unweighted ratio of actual
changes over possible changes
Complete ΔDSM for Auto Density Correction Technology
N2 N2
- captures all changes made to basic system to infuse the technology
- count number of cells in baseline DSM affected by technology
- compute technology invasiveness index (between 0 and 100%)
- also estimate non-recurring effort (engineering hours and cost)
TI 
 DDSM
i 1 j 1
ij
N1 N1
 DSM
i 1 j 1
ij
TI (Technology) ~= 8.5%
34
Estimating the Value of Technology
actual attribute j
critical attribute
Value of an individual attribute
  f  f 2   f  f 2 
C
I
j
I

v f j   
2
2
  fC  f I    f 0  f I  


Ideal attribute

weighting
exponent
baseline attribute
Value of a product = aggregation of individual attribute values
bottom-up value
V  f1 , f 2 ,..., f m   Vov  f1  v  f 2  ...v  f m   DV  f1'   ...  DV  f k'   DCown
baseline value
cost of
ownership
optional value
Value of a product as perceived by the market
top-down value
Demand for product i
Number of competitors
Value of product i
Vi 
Price elasticity in market
Total demand in market
N Di  DT 
 Pi
K N  1
Price for product i
Cook, Harry E., “Product Management:
Value, quality, cost, price, profit and,
organization”, Aug 31, 1997
35
D
Step
DDSM
3
Estimating the Value of Technology
2
Technology
Infusion
Identification
Baseline
System DSM
Performance and
Cost Models
1
Baseline
Product
Value V(g)
5
TI
Effort
6
Modified
Product
Value
V(Dg)
D
7
E[DNPV]
Revenue
Impact
Risk-Return
Curve for
Technology
s[DNPV]
Printing System Attribute Value Curve
1.01
Technology
Infusion
Evaluation
Probabilistic
NPV Analysis
8
10
Cost
Impact
9
Current
Current
product
Product
1.00
Vi/Vo
0.99
0.98
New target
New Target
with infused
with Infused
technology
Technology
0.97
0.96
0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1
Attribute Values
• Newly infused technology will improve key performance attributes of iGen3
• It will also result in field operating cost reduction due to attributes improvement
• Improved performance and reduced operating costs
o increases initial sales volume
o reduces lifetime operating costs
• Cost-benefit ΔNPV analysis is needed to estimate lifetime profit/loss
36
4
Net Present Value (NPV)
• Measure of present value of cash flows in different periods in the
future
• Cash flow in any given period discounted by the value of a dollar
today at that point in the future
– “Time is money”
– A dollar tomorrow is worth less today since if properly invested, a
dollar today would be worth more tomorrow
• Rate at which future cash flows are discounted is determined by the
“discount rate” or “hurdle rate”
– Discount rate is equal to the amount of interest the investor could
earn in a single time period (usually a year) if s/he were to invest
in an equally risky investment
DNPV is the difference between the NPV with the current system
and the system with the new technology infused.
37
Net Present Value (NPV)
T
Ct
NPV  
t
t 0 (1  r )
1500
500
29
27
25
23
21
19
17
15
13
11
9
7
5
3
0
1
Cashflow, Pt [$]
1000
Cashflow
DCF (r=12%)
-500
-1000
-1500
Program Time, t [yrs]
38
38
D
Step
Technology Cost Benefit Analysis: ΔNPV
DDSM
3
2
Technology
Infusion
Identification
Baseline
System DSM
Performance and
Cost Models
1
Baseline
Product
Value V(g)
5
TI
Effort
4
6
Modified
Product
Value
V(Dg)
D
7
E[DNPV]
Revenue
Impact
Risk-Return
Curve for
Technology
s[DNPV]
Start of the
production
New Technology
Discounted Cash Flow
Technology
Infusion
Evaluation
Probabilistic
NPV Analysis
10
8
Cost
Impact
9
Post-sale
cost saving
1.50
Cash Flow (Normalized)
1.00
0.50
0.00
1
2
3
4
5
6
7
8
9
10
11
12
Discounted
Cash Flow
-0.50
-1.00
-1.50
-2.00
Product
development
Years
End of the
production
• Nominal ΔNPV can be estimated using upfront development cost
and recurring variable cost and savings.
• Monte Carlo simulation performed to estimate range of ΔNPV,
given uncertainty in future demand of the technology infused
product and post-sale cost savings
New Technology has positive DNPV !
39
Technology Infusion Framework
D
Step
2
DDSM
3
Technology
Infusion
Identification
Baseline
System DSM
Performance and
Cost Models
1
Baseline
Product
Value V(g)
5
TI
Effort 4
6
Modified
Product
Value
V(Dg)
D
7
E[DNPV]
Revenue
Impact
Risk-Return
Curve for
Technology
s[DNPV]
Technology
Infusion
Evaluation
Probabilistic
NPV Analysis
10
8
Cost
Impact
9
40
Alstom Technology Infusion
Exercise (45 min)
1. Select a particular type of power plant
(energy source) at your table
2. For this type of plant make a
simplified DSM (no more than 10x10)
3. Identify a new potential new
technology of interest to Alstom
4. Where does the technology affect the
DSM (DDSM)?
5. Discuss what would be needed for a
full Technology Infusion Analysis as a
group. Estimate roughly the DNPV
6. Briefout
41
Readings for this session
• [Pre-reading] Koh, Heebyung, and Christopher L. Magee. "A
functional approach for studying technological progress: Extension
to energy technology." Technological Forecasting and Social
Change 75.6 (2008): 735-758.
• [Post-reading] Suh. E.S., Furst M.R., Mihalyov K.J, de Weck O.,
“Technology Infusion for Complex Systems: A Framework and Case
Study”, Systems Engineering, 13 (2), 186-203, Summer 2010
• Van Wyk, Rias J. 2004, Technology - A Unifying Code, Stage Media
Group, Cape Town
• Magee C. and de Weck O. L., “Complex System Classification”,
Fourteenth Annual International Symposium of the International
Council on Systems Engineering (INCOSE), Toulouse, France, June
20-24, 2004
• C.P. Manning and R.J. Fruehan,” Emerging Technologies for Iron
and Steelmaking“, JOM, 53 (10) pp. 20-23, 2001
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