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Thinking About the Future of Complex
Technological Systems: Which
Technologies Should Shape their Designs?
Jeffrey L Funk
Division of Engineering and Technology Management
National University of Singapore
Presented at the 1st Asia-Pacific Conference on
Complex System Design and Management
Different Technologies have Different
Annual Rates of Improvement
0
5
10
15
20
25
-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 >42
Number of Technologies by Annual Rates of Improvement
Annual Rates of Improvement
Source: Nagy B, Farmer D, Bui Q, Trancik J 2013. Statistical Basis for Predicting Technological Progress. PLoS ONE 8(2): e52669. doi:10.1371/journal.pone.0052669NREL, 2013
67%: <9% per year
89%: <15% per year
How Should this Impact on the Design of
Complex Systems?
Complex systems are expected to last many decades, if not longer
They are composed of many technologies, often with different
rates of improvement
Rates of improvement impact on the tradeoffs inherent in
complex systems; tradeoffs between
different component technologies
cost and performance in an overall system
If the rates of improvement are rapid, the tradeoffs, i.e.,
economics, will change in a few years and even more so in a few
decades!
How Should this Impact on the Design of
Complex Systems? (2)
Rapid changes also enable new forms of designs
at the system and sub-system levels
complex systems should be flexible to accommodate such changes
But to design such flexibility, we must understand rates of
improvement and the potential changes that they might bring
But many ignore rates of improvement
Universities rarely discuss them, including sustainability courses
Inter-Governmental Panel on Climate Change emphasizes wind turbines
even as their reports claim rate of improvement is 2% per year over last
30 years
How can we Better Understand and Analyze
Potential Changes in System Design?
Understanding potential changes is like predicting the future
Which is obviously very difficult
These difficulties are compounded by cognitive biases (Nobel
Laureate, Daniel Kahneman)
People assess relative importance of issues, including new technologies
by ease of retrieving from memory
largely determined by what they see, read, and hear
Second, judgments and decisions are guided directly by feelings
of liking and disliking
Many people “like” some technologies and dislike others for emotional
and not logical reasons
Isn’t there a more deliberate and logical way?
Understanding rates of improvement can help us better understand when
new types of system designs might become economically feasible
Technologies must have some level of performance and price for
specific applications before they become economically feasible
Technologies that experience faster rates of improvement are more likely to
become economically feasible….
They are also more likely to become economically feasible for increasing number
of applications and thus diffuse…
And, they are more likely to impact on higher-level systems
But which technologies are experiencing rapid rates of improvement and
why?
Methodology
We used time series data that are reported in
scientific and engineering journals such as Nature, Science, Phys Status
Solidi and IEEE
annual reports by reputable scientific organizations such as International
Solid State Circuits Conference
archival social science publications
general technology and technology-specific web sites
Most data are from single sources and often single figures
Data was collected on multiple dimensions when possible
Data are for best laboratory results, commercialized products, and
some combinations of these two
Technologies Experiencing Rapid Rates of Improvements
(Information Transformation)
Technology Dimensions of measure Time Period Rate Per YearIntegrated Circuits Number of transistors per chip 1971-2011 38%
MEMS Number of Electrodes per Eye 2002-2013 46%
Drops per second for printer 1985-2009 61%
Organic Transistors Mobility 1994-2007 101%
Power ICs Current Density 1993-2012 16%
Carbon Nanotube
Transistors
1/Purity (% metallic) 1999-2011 32%
Density (per micrometer) 2006-2011 357%
Superconducting
Josephson Junctions
1/Clock period 1990-2010 20%
1/Bit energy 1990-2010 10%
Qubit Lifetimes 1999-2012 142%
Bits per Qubit lifetime 2005-2013 137%
Photonics Number of Optical Channels 1983-2011 39%
Computers Instructions per unit time 1979-2009 36%
Instructions per time and dollar 1979-2009 52%
Quantum Computers Number of Qubits 2002-2012 107%
Technologies Experiencing Rapid Rates of Improvements
(Information Storage)
Technology Dimensions of
measure
Time Period Rate per Year
Magnetic Storage Recording density
(disks)
1991-2011 56%
Recording density
(tape)
1993-2011 32%
Cost per bit 1956-2007 33%
Flash Memory Storage Capacity 2001-2013 47%
Resistive RAM 2006-2013 272%
Ferro-electric RAM 2001-2009 37%
Phase Change RAM 2004-2012 63%
Magneto RAM 2002-2011 58%
Technologies Experiencing Rapid Rates of Improvements
Information Transmission, Materials and Biological Transformation
Technology
Domain
Sub-Technology Dimensions of
measure
Time
Period
Rate Per
YearInformation
Transmission
Last Mile Wireline Bits per second 1982-2010 48.7%
Wireless, 100 m Bits per second 1996-2013 79.1%
Wireless, 10 m 1995-2010 58.4%
Wireless, 1 meter (USB) 1996-2008 77.8%
Materials
Transformation
Carbon Nanotubes 1/Minimum Theoretical
Energy for Production
1999-2008 86.3%
Biological
Transformation
DNA Sequencing per unit cost 2001-2013 146%
Synthesizing per cost 2002-2010 84.3%
Cellulosic Ethanol Output per cost 2001-2012 13.9%
Technologies Experiencing Rapid Rates of Improvements(Energy Transformation and Transmission)
Technology
Domain
Sub-Technology Dimensions of
measure
Time
Period
Rate Per
YearEnergy Trans-
formation
Light Emitting
Diodes (LEDs)
Luminosity per Watt 1965-2008 31%
Lumens per Dollar 2000-2010 40.5%
Organic LEDs Luminosity per Watt 1987-2005 29%
GaAs Lasers Power/length-bar 1987-2007 30%
LCDs Square meters per dollar 2001-2011 11.0%
Quantum Dot
Displays
External Efficiency 1994-2009 79.0%
Solar Cells Peak Watt Per Dollar 2004-2013 21.0%
Photo-sensors
(Camera chips)
Pixels per dollar 1983-2013 48.7%
Light sensitivity 1986-2008 18%
Energy
Transmission
Super-conductors Current-length per dollar 2004-2010 115%
How can this Data be Used? To understand reasons for rapid improvements1
To analyze past1,2, current, or future3 changes in specific systems
To analyze changes in tradeoffs between different designs at
system level, sub-system level1,2
can this be better done with detailed cost models?
For future, to better understand when new forms of designs might become economically feasible3
To design systems that accommodate future design changes
To help students create new businesses that are based on new technologies or systems composed from new technologies
1 Funk, 2013, Technology Change and the Rise of New Industries, Stanford University Press; Funk J 2013 What Drives Exponential Improvements? California Management
Review 55(3): 134-152, Spring 2013: Funk J and Magee C 2015 Rapid Improvements with No Commercial Production: How do the improvements occur? Research Policy
2 Funk J 2009. Systems, Components, and Technological Discontinuities: The case of magnetic recording and playback equipment, Research Policy 38(7): 1079-1216.
3 Funk J and Magee C 2014. Exponential Change: What drives it? What does it tell us about the future?
MRI and CT
Scanners Laptops MP3 Players
Calculators Video Set-top boxes E-Book Readers
Digital Games Web Browsers Digital TV
Watches Mobile Digital Cameras Smart Phones
PCs Phones PDAs Tablet Computers
The Past: Increases in the Number of Transistors Make New
Forms of Electronic Products Economically Feasible
For more details, see Funk J, Technology Change and the Rise of New Industries, Stanford University Press 2013
slidehttp://www.slideshare.net/Funk98/how-is-technology-change-creating-new-opportunities-in-integrated-circuits-ics-and-electronic-systems
What’s
Next?
If we add magnetic disks, new displays,
glass fiber, lasers, and photo-sensors
The list of new systems becomes much longer
New forms of Internet content and applications have become possible over the last 20 years
More recently: Big Data, Cloud Computing, Social Networking
How did changes in tradeoffs impact on the emergence of these systems?
How are these changes impacting on current design choices?
Example of Changing Tradeoffs in Design
High cost of ICs and computers in the 1960s and 1970s
meant that most computers were shared
Many expected diffusion to occur through remote use of computers
As the cost of ICs dropped in the 1970s and 1980s, the fast
response time from PCs outweighed the low utilization of
PCs
Further reductions in cost of ICs and improvements in
display performance caused tradeoffs to change in favor of
portable computers
Tradeoffs for computers continue to change as cost and
performance of ICs and other electronic components rise
For more details, see Funk J, Technology Change and the Rise of New Industries, Stanford University Press 2013
slidehttp://www.slideshare.net/Funk98/how-is-technology-change-creating-new-opportunities-in-integrated-circuits-ics-and-electronic-systems
Changing Tradeoffs in Design of Electric Vehicles
Low energy storage density of batteries (1/25th of gasoline) makes it hard for
electric vehicles to have same range, weight, cost of conventional vehicles
Improvements in “components” are changing tradeoffs involved with electric vehicles
Faster rate of improvement for components in vehicle charging (power electronics 16% and other ICs 30-40%) stations than in batteries (5% per year) changes the tradeoffs towards a dense system of charging stations
Rates of improvement for power electronics and ICs also improve economics of wireless charging, which reduces set-up time for vehicles and maintenance cost for charging stations
For more details, see: http://www.slideshare.net/Funk98/it-and-transportation-systems http://www.slideshare.net/Funk98/microgrids-electric-vehicles-and-wireless-charging
Changing Tradeoffs in Public Transportation
Continued improvements in GPS and smart mobile phones (both cheaper and better) are changing the economics of
Buses
Bike sharing
Understanding bus arrival times and locations becomes easier with GPS and smart phones
Facilitates use of buses
Also managing bus routes
Smart phones help us find bike stations and borrow bikes
Helps integrate bike and train transport
Also determining station locations and redistributing bikes
For more details, see: http://www.slideshare.net/Funk98/it-and-transportation-systems
http://www.slideshare.net/Funk98/how-is-technology-change-creating-new-opportunities-in-integrated-circuits-ics-and-electronic-systems
http://www.slideshare.net/Funk98/how-is-technology-change-creating-new-opportunities-in-humancomputer-interfaces
Changing Tradeoffs in Automated Vehicles
Falling cost of ICs, lasers, MEMS (about 30% to 40% per year)
will make Automated Vehicles (AVs) 90% cheaper in 10 years
Dedicating roads or lanes in roads to AVs would dramatically
increase their benefits
Higher road capacity, faster travel time and thus better fuel
efficiency
Lower costs for traffic police, auto insurance, ambulance,
emergency vehicles
Dedicating roads to AVs would reduce technical requirements
Cars could rely more on wireless communication, magnetic stripes
and other inexpensive sensors than on LIDAR
For more details, see: http://www.slideshare.net/Funk98/it-and-transportation-systems http://www.slideshare.net/Funk98/smart-infrastructure-for-autonomous-vehicles
http://www.slideshare.net/Funk98/dedicated-roads-for-autonomous-vehicles http://www.slideshare.net/Funk98/autonomous-vehicles-28513504
Sustainability is a Design Problem!
Better product (and process) designs for systems are needed for
sustainability
Better designs for transportation, electric vehicle, and other systems
Technology change and improvements in components is key enabler of better
system design
Even solar cells require better designs
Installation is biggest percentage of total cost
How can we design solar cells for easier installation/implementation?
Just implementing IPCC’s approved list of technologies with subsidies
is misguided
Let’s help students understand rapidly improving technologies and
their impact on design tradeoffs in higher level systems
Can we do more Detailed Cost Analyses?
For example, can we simulate transportation systems and the impact of reduced costs of information technologies on design of transportation systems?
These simulations must consider
Composition of system
Current costs of each component and their rates of improvement
Impact of these component costs on design of system
Extent of improvements before system becomes economically feasible
Students should be doing this in order to understand when new designs become economically feasible in future
Rate of Improvement
Exte
nt
of
Impro
vem
ent
Needed
Small
Large
Slow Fast
For the Future: When Will New Technologies or New
Systems Composed of Them Become Economically Feasible?
Now or Probably
Very Soon
Probably
Never
Within
5 to 15 Years?
Within 5-15
Years?
My Research and Teaching Analyzes Economic
Feasibility of Many Technologies
Integrated circuits (ICs) and electronic systems
Internet of Things, Sensors, MEMS and Bio-electronic ICs
Lighting and Displays
Information Technology for Transportation Systems
Human Computer Interfaces
Nanotechnology
Superconductivity
Solar cells, wind turbines
Telecommunication
These analyses are available here: http://www.slideshare.net/Funk98/presentations
Students have Done Further Analyses
on More Than 50 Technologies
New forms of electronic systems: smart homes, robotic exoskeletons, smart grid, 3D
scanners, eye tracking, pico-projectors, wireless charging of phones and vehicles, 3D Holography, Ink Jet Printers, light field image sensors, wearable computing, solar gliders for telecom, quantum computers
health care: Bioprinters, bionic eyes, wearable health care devices, bio-sensors
displays: flexible OLEDs, conformal electronics, virtual retinal, transparent displays
materials: carbon-nanotubes, aerogels, superconductors, bio-luminescence, membranes
transportation and energy: automated vehicles, dedicated roads for automated vehicles, electric vehicles, wireless charging, smart grid, commercial drones
These analyses are available here: http://www.slideshare.net/Funk98/presentations
My Analyses and Student Analyses
Helps students understand
the tradeoffs in systems between
New forms of designs
Different technologies
when new technologies or new forms of designs might become economically feasible
which technologies will form the basis for new businesses
Many will argue that you can’t predict the future
But all actions assume predictions
And assuming no improvements is clearly a worse prediction than assuming faster improvements in some technologies than in other technologies
What do these Analyses tell us about Future?
No end to Moore’s Law and improvements in MEMS and Bio-Electronic ICs
In combination with the Internet, this is enabling
More Big Data Analysis, Cloud computing, Social Networking
Internet of Things and Home Automation
Better control of all systems (logistics, factories)
For health care
Computer assistants for doctors
Mobile phones become the center for health care
Drug delivery, Bionic eyes, Exoskeletons, Cyborgs
DNA sequencers and synthesizers change drug and materials development
What do these Technologies tell us about Future? (2)
Human computer interfaces
Better displays including more flexibility and more responsive to touch and voice
Augmented reality, Virtual reality, Wearable computing
Food and agriculture
better sensors for global value chains of food (and other products)
prescriptive planting and laser leveled fields
Energy
Rapid reductions in cost of solar cells (but not in installation costs)
Materials
Ultra-thin materials with high strength-to weight ratios
Nano-particles and fibers
Carbon nanotubes for electronics and other applications
What do these Technologies…. (3) Energy
Did any of the technologies mentioned on the last two slides
surprise you?
But what about energy?
Wind turbines: 2% rate of improvement per year over last 30 years
Li-ion batteries: 5% rate of improvement over last 20 years
Even non-module (installation) costs for solar cells only fall 4% per year
How can these slow rates of improvement make a meaningful
contribution to sustainability when they are still far from
competitive with existing technologies?
Yet the IPCC and sustainability organizations focus on these
technologies
Energy: New Systems to Consider and Analyze
More efficient logistics for humans and freight through better computers and
RFID tags
Smart lighting (and heating) systems that combine LEDs with motion sensors
and other ICs can reduce lighting and heating costs
Smart water and other systems through better sensors reduce water usage
Better sensors for smarter agriculture, aquaculture, and food logistics can
increase availability of food
Smart grid (through better Internet) can enable more charging points and a
greater frequency of vehicle recharging and thus reduce need for battery
storage capacity
Better digital and power (MOSFETS) ICs and thin film coils reduce cost of
wireless charging and thus further facilitate frequent recharging and need
for battery storage capacity in vehicles
Energy: New Systems to Consider and Analyze (2)
In combination with existing and improved mobile phones, cheaper and
better GPS can help vehicles find charging points, cities improve bus
service, and commuters increase their use of public transportation
Existing and better mobile phones can also facilitate the sharing
economy including the sharing of bicycles to overcome crowded
parking lots in cities
Roads dedicated to autonomous vehicle (through improvements in ICs,
MEMS, and lasers) can increase road capacity and fuel efficiency
Improvements in superconductors lead to longer and more efficient
energy transmission and better generators, motors, and transformers
Overall smarter cities that use less energy, water, and other resources
through better sensors
Conclusions
Understanding rates of improvement can help us design
better systems
Systems that benefit from technologies with rapid
improvements
Systems that can be upgraded with the new technologies
Let’s help students design their future
Lets give students the data and methods to design their
future
Appendix
Quantity (Q)
Price (P)
q
p
Diffusion often starts in segments/users that are willing to pay more
for products and services than are other segments/users
Demand
Curve
Supply Curve
Typical movement of
supply curve over time Typical
movement
of demand
curve over
time
Quantity (Q)
Price (P)
q
p
Maximum Threshold of Price: the maximum price that the market will
pay for a new technology
Demand
Curve
Supply Curve
Typical movement of
supply curve over time Typical
movement
of demand
curve over
time
Quantity (Q)
Performance
(P)
q
p
Sometimes, diffusion starts in segments/users that have
lower performance expectations than other segments/users
Supply
Curve
Demand
Curve
Quantity (Q)
Performance
(P)
Minimum Threshold of Performance: the minimum performance
the market will accept for a new technology
Supply
Curve
Demand
Curve
Typical
movement
of supply
curve over
time
0
2
4
6
8
10
12
14
0 2 4 6 8 10 12
Figure 2. Number of Chemical Technologies by Annual Rates of Improvement
Annual Rates of Improvement
0
2
4
6
8
10
12
14
16
18
20
-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14
Annual Rates of Improvement
Figure 3. Number of Non-Chemical
Technologies by Annual Rates of Improvement
Focusing on Technologies with Annual Rates Under 15%