What Drives Improvements in Cost and Performance?


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Nobel Laureate Robert Solow concluded that 85% of America’s productivity growth comes from innovation. But how can we characterize this innovation? One way we can characterize this innovation is through the improvements in cost and performance that technologies experience over time since many innovations are required for these improvements to occur. These slides investigate the rates of improvement for 33 different technologies and 52 dimensions of performance/cost and conclude that the drivers of these improvements can be placed in two categories: 1) creating materials (and their associated processes) that better exploit physical phenomena; and 2) geometrical scaling. For geometric scaling, some technologies experience improvements through increases in scale while a small number of technologies experience them through reductions in scale.

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What Drives Improvements in Cost and Performance?

  1. 1. Exploring the Design Mechanisms that Drive Improvements in Performance and Cost A/Prof Jeffrey Funk National University of Singapore Prof Christopher Magee MIT A summary of these ideas can also be found in 1) What Drives Exponential Improvements? California Management Review, May 2013 2) Technology Change and the Rise of New Industries, Stanford University Press, January 2013 3) Exponential Change: what drives it? what does it tell us about the future? http://www.slideshare.net/Funk98/exponential-change-what-drives-it-what-does-it-tell-us-about-the-future-14104827
  2. 2. Performance and Cost are Important (1) Necessary (but insufficient condition) for improvements in productivity (and value propositions)  Solow’s (1957) Nobel Prize winning research found that most growth comes from innovation  ◦ improvements in cost and performance for a technology is one measure of innovation ◦ faster rates of improvement directly impact on output-to input ratio of economic activities and thus on productivity growth
  3. 3. Performance and Cost are Important (2)  Large impact on diffusion via effect on profitability of users (Griliches, 1957; Mansfield, 1968) ◦ Greater profitability leads to faster rates of diffusion and the first users tend to be those with the greatest profitability ◦ In summary, improvements in cost and performance of new technologies impact on both the rate of diffusion and the level of the impact of the technology on productivity  Helps us ◦ implement better R&D policies ◦ understand when new technologies become economically feasible, which helps us solve global problems
  4. 4. But what drives improvements?  Predominant view is rather vague ◦ Changes in product design lead to improvements in performance and changes in process design lead to improvements in cost (Utterback, 1994; Adner and Levinthal, 2001) ◦ Novel combinations of components (Basalla 1988; Iansiti 1995) ◦ Costs fall as cumulative production grows in learning or experience curve (Wright 1936; Arrow 1962; Argote and Epple 1990; Ayres 1992), some argue as automated manufacturing equipment is introduced and organized into flow lines (Utterback, 1994)
  5. 5. Another View: Geometric Scaling     Building from various engineering literatures, some argue: changes in physical scale are important mechanisms for improvements Gold (1974, 1981) argued this phenomenon overlooked when cumulative production and thus learning curves are emphasized Lipsey et al (2005) focus on theoretical reasons for benefits from increases in scale, as does Winter (2008) Winter also discuses technologies that benefit from reductions in scale such as ICs and membranes. Winter calls for better understanding of scaling, impact on production functions, and thus drivers of cost and performance improvements
  6. 6. Methodology Looked for cost and performance data on wide variety of technologies; called trajectories by Dosi (1982); technologies are usually defined in terms of single concept/principle (Uttterback, 1994; Henderson and Clark, 1990)  Began with already possessed data  Found new data in  ◦ Social science archival publications giving quantitative data over time (Martino, 1971; Koh and Magee, 2006 and 2008) ◦ Scientific and engineering journals ◦ Google searches
  7. 7. Annual Rates of Improvement for Specific Technologies Technology Domain Energy Transformation Sub-Technology Dimensions of measure Time Period 1 Lighting 2 LEDs 3 Organic LEDs 4 GaAs Lasers 5 Photosensors 6 Solar Cells 7 Aircraft engine Light intensity per unit cost Luminosity per Watt Luminosity per Watt Power/length-bar Light sensitivity (mV/micrometer) Power output per unit cost Gas pressure ratio achieved Thrust per weight-fuel consumed Power of aircraft engine Energy transformed per unit mass Energy transformed per unit mass Energy transformed per unit volume Energy stored per unit volume Energy stored per unit mass Energy stored per unit cost Energy stored per unit cost Energy stored per unit mass Energy stored per unit cost Energy stored per unit mass Energy transported times distance Energy transported times distance per unit cost 1840-1985 1965-2008 1987-2005 1987-2007 1986-2008 1957-2003 1943-1972 1943-1972 1927-1957 1896-1946 1880-1993 1890-1997 1882-2005 1882-2005 1950-2002 1945-2004 1962-2004 1983-2004 1975-2003 1890-2003 1890-1990 8 Piston engines 9 Electric Motors Energy storage 10 Batteries 11 Capacitors 12 Flywheels 13. Energy Transport Improvement Rate Per Year 4.5% 31% 29% 30% 18% 16% 7% 11% 5% 13% 3.5% 2.1% 4% 4% 3.6% 4% 17% 18% 10% 10% 2%
  8. 8. Information 14 ICs (Microprocessors) Transfor15 MEMS Printing mation 16 Computers Number of transistors per chip/die 1971-2011 38% Drops per second for ink jet printer 1985-2009 61% Instructions per unit time 1945-2008 40% Instructions per unit time and dollar 1945-2008 38% 17 Liquid Crystal Displays Square meters per dollar 2001-2011 11% 18 MRI 1/Resolution x scan time 1949-2006 32% 19 Computer Tomography 1/Resolution x unit time 1971-2006 29% 20 Organic Transistors Mobility (cm2/ Volt-seconds) 1994-2007 101% Information 21 Magnetic Tape Storage Bits per unit cost 1955-2004 40% Bits per unit volume 1955-2004 10% 22 Magnetic Disk Bits per unit cost 1957-2004 39% Bits per unit volume 1957-2004 33% Bits per unit cost 1996-2004 40% Bits per unit volume 1996-2004 28% Bits per unit time 1858-1927 35% Bits x distance per unit cost 1858-2005 35% Coverage density, bits per area 1901-2007 37% Spectral efficiency, bits per unit bandwidth Bits per unit time 1901-2007 17% 1895-2008 19% 23 Optical Disk Information Transport 24 Wireline Transport 25 Wireless Transport
  9. 9. Living Organisms Materials/ Matter Other Biological transformation 29 Transport of humans/freight 30 Load Bearing 31 Magnetic 32 Machine Tools 33 Laboratory Cooling 26 Genome sequencing per unit cost 27 Harvest concentration of penicillin 28a U.S. wheat productivity (per input) 28b US wheat production per area Ratio of GDP to transport sector Aircraft passengers times speed Strength to weight ratio Magnetic strength Magnetic coercivity Accuracy Machining speed Lowest temperature achieved 1965-2005 1945-1980 1948-2009 1945-2005 1880-2005 1926-1975 1880-1980 1930-1980 1775-1970 1900-1975 1880-1950 35% 17% 1.3% 0.9% 0.46% 13% 1.6% 6.1% 8.1% 7.0% 6.3% 28% Sources, from top to bottom: (Nordhaus,1997; Azevedo, 2009; Sheats et al, 1996; Lee, 2005; Martinson, 2007; Suzuki, 2010; Nemet, 2006; Alexander and Nelson, 1973; Sahal, 1985; Koh and Magee, 2008; Wikipedia, 2013; Stasiak et al, 2009, Koh and Magee, 2006; Koomey, 2010; Economist, 2012; Kurzweil, 2005; Kalender, 2006; Shaw and Seidler, 2001; Dong et al, 2010; Koh and Magee, 2006; Amaya and Magee, 2008; NHGRI, 2012; Seth, Hossler and Hu, 2006; U.S. Department of Agriculture, 2012, Glaeser and Kohlhase, 2004: Martino, 1971; NAS/NRC, 1989; Ayres and Weaver, 1998; American Machinist, 1977; Martino, 1971)
  10. 10. Methodology - continued  Our initial analysis of the technologies was aimed at understanding the composition of a technology’s system ◦ i.e., “nested hierarchy of subsystems” (Tushman and Rosenkopf, 1992; Tushman and Murmann, 1998)  Then considered geometric scaling ◦ Examples of geometric scaling were searched for outside of chemical plants, furnaces, and smelters (since these have been empirically analyzed to some extent) ◦ For each instance of geometric scaling, type of geometrical scaling was identified and data on changes in scale and on cost/price for various levels of scale were gathered  This still left us with a large number of technologies whose improvements were not well explained
  11. 11. Methodology - continued  Second mechanism is engineers and scientists create (or improve existing) materials to better exploit underlying physical phenomena ◦ This often involved simultaneously creating new processes for producing them (Stobaugh 1988; Morris et al 1991; Olsen, 2000; Linton and Walsh 2008, Magee 2012)   Word “create” is used because scientists and engineers often create materials that do not naturally exist (as opposed to finding them) and in doing so must also create the processes Improvements often involve new “classes” of materials and not just modifications to existing materials
  12. 12. Methodology - continued  Data on cost and performance improvements was collected ◦ time series ◦ specific moments in time  Performance improvements from creating materials were almost always in form of a time series graph ◦ that included names of materials  For scaling, looked for data for a single moment in time in order to isolate impact of changes in scale, which was found for most technologies
  13. 13. Methodology - continued  Each technology was assigned to one of two mechanisms (and to identify important component technology changes) ◦ even though many benefited from both mechanisms  We also note that these two mechanisms are attempt at categorizing complex set of changes and that each mechanism is by itself complex and in specific instances is enabled or accompanied by other technical knowledge
  14. 14. Outline of Results   Creating materials (and their associated processes) that better exploit physical phenomena Geometrical scaling ◦ Reductions in scale: e.g., integrated circuits (ICs), magnetic storage, MEMS, bio-electronic Ics ◦ Increases in scale: e.g., larger production equipment, engines, oil tankers  Some technologies directly experience improvements while others indirectly experience them through improvements in “components” ◦ Computers and other electronic systems ◦ Telecommunication systems
  15. 15. Items 1, 2, 3: involve lighting
  16. 16. Other Evidence for Lighting   Full quote for LEDs from Azevedo et al, 2009: “In 1962, Holonyak, while with General Electric’s Solid- State Device Research Laboratory, made a red emitting GaAsP inorganic LED [27]. The output was very low (about 0.1 lm/W), corresponding to an efficiency of 0.05% [27]. Changing materials (toAlGaAs/GaAs) and incorporating quantum wells, by 1980, the efficacy of his red LED had grown to 2 lm/W, about the same as the first filament light bulb invented by Thomas Edison in 1879. An output of 10 lm/W was achieved in 1990, and a red emitting light AllnGaP/GaP-based LED reached an output of 100 lm/W in 2000 [27]. In 1993, Nakamura demonstrated InGaN blue LEDs [28]. By adding additional indium, he then produced green LEDs and, by adding a layer of yellow phosphor on top of the blue LED, he was able to produce the first white LED. By 1996, Nichia developed the first white LED based on a blue monochromatic light and a YAG down-converter.” Quote for Organic LEDs: “The next few years should see major advances in this area, and the availability of a much wider array of durable materials and processes than currently exist for the device designer.” (Sheats et al, 1996).
  17. 17. Item, 20, Organic Transistors Note the different material classes and the improvements for each of them Huanli Dong , Chengliang Wang and Wenping Hu, High Performance Organic Semiconductors for Field-Effect Transistor, Chemical Commununications, 2010,46, 5211-5222
  18. 18. Different Classes of Materials were found for Many Technologies Technology SubDomain Technology Dimensions of Different Classes of Materials measure Energy Transformation Light intensity per unit cost Lighting LEDs Luminosity per Group III-V, IV-IV, and II-VI semiconductors Organic LEDs Watt Small molecules, polymers, phosphorescent materials Solar Cells Energy storage Information Transformation Living Organisms Materials Candle wax, gas, carbon and tungsten filaments, semiconductor and organic materials for LEDs Power output per unit cost Silicon, Gallium Arsenide, Cadmium Telluride, Cadmium Indium Gallium Selenide, Dye-Sensitized, Organic Batteries Energy stored Lead acid, Nickel Cadmium, Nickel Metal Hydride, per unit volume, Lithium Polymer, Lithium-ion mass or cost Capacitors Carbons, polymers, metal oxides, ruthenium oxide, ionic liquids Flywheels Stone, steel, glass, carbon fibers 2/ Organic Mobility (cm Polythiophenes, thiophene oligomers, polymers, Transistors Volt-seconds) hthalocyanines, heteroacenes, tetrathiafulvalenes, perylene diimides naphthalene diimides, acenes, C60 Biological U.S. corn output Open pollinated, double cross, single cross, biotech GMO transformation per area Load Bearing Strength to Iron, Steel, Composites, Carbon Fibers weight ratio Magnetic Strength Steel/Alnico Alloys, Fine particles, Rare earths Coercivity Steel/Alnico Alloys, SmCo, PtCo, MaBi, Ferrites, Couldn’t find different classes for GaAs lasers and for photosensors
  19. 19. Outline of Results   Creating materials (and their associated processes) that better exploit physical phenomena Geometrical scaling ◦ Reductions in scale: e.g., integrated circuits (ICs), magnetic storage, MEMS, bio-electronic ICs ◦ Increases in scale: e.g., larger production equipment, engines, oil tankers  Some technologies directly experience improvements while others indirectly experience them through improvements in “components” ◦ Computers and other electronic systems ◦ Telecommunication systems
  20. 20. Geometric Scaling Relationship between the technology’s core concepts (Dosi, 1982), physical laws and dimensions (scale), and effectiveness  Or as others describe it: the “scale effects are permanently embedded in the geometry and the physical nature of the world in which we live (Lipsey, Carlaw, and Bekar, 2005) 
  21. 21. Item 14: “Intel, which has maintained this pace for decades, uses this golden rule as both a guiding principle and a springboard for technological advancement, driving the expansion of functions on a chip at a lower cost per function and lower power per transistor, by shrinking feature sizes while introducing new materials and transistor structures.” www.intel.com/content/www/us/en/silicon-innovations/moores-law-technology.html)
  22. 22. (Item 26) http://www.genome.gov/sequencingcosts/
  23. 23. Reductions in Scale: DNA Sequencing  Importance of scale can be seen by reading highly cited papers such as “Genome sequencing in micro-fabricated high-density pico-liter reactors” (Margulies, 2005) and “Toward nano-scale genome sequencing” (Ryan et al, 2007) ◦ “The ability to construct nano-scale structures and perform measurements using novel nano-scale effects has provided new opportunities to identify nucleotides directly using physical, and not chemical, methods.”  In fact, just the titles of these papers are fairly suggestive.
  24. 24. Outline of Results   Creating materials (and their associated processes) that better exploit physical phenomena Geometrical scaling ◦ Reductions in scale: e.g., integrated circuits (ICs), magnetic storage, MEMS, bio-electronic ICs ◦ Increases in scale: e.g., larger production equipment, engines, oil tankers  Some technologies directly experience improvements while others indirectly experience them through improvements in “components” ◦ Computers and other electronic systems ◦ Telecommunication systems
  25. 25. Improvements from Increases in “Geometric” Scale (year in parentheses) Technology SubTechnology Dimensions of Scale Production Equipment Liquid Crystal Displays Substrate Size Engines Steam Engine Horsepower Marine Engine Electricity Increases in Scale Small 0.17 m2 (1997) 1.4 m2 (2003) 10 (1800) Large 2.7 m2 (2005) 5.3m2 (2008) 20 (1800) 2.3 (2010) 225 (2010) Amount of Cost Reduction Dimension Equipment* cost per area Price per horsepower Amount 88% 36% 2/3 74% Generation 1000s of Watts 100,000 (1928) 600,000 (1958) Capital cost per Watt 59% Transmission Voltage 10,000 Volts (1880) 790,000 Volts (1965) Price per distance 2% per year or >99.9% Final cost of electricity 1000s of Watts 93 (1892) 1.4 million (1969) Price per kilowatt hour > 99.9% 38.5 (2010) 40 (2010) 132 (2012) 265 (2010) 170 (2010) 853 (2012) Capital cost per ton 59% 40 (2007) 220 (2007) Transpor Oil Tankers Capacity in tation 1000s of Equipment Freight Vessels tons Aircraft Number of Passengers 52% Capital cost per passenger 14% Fuel usage per passenger 48% Sources (from top to bottom): (Keshner and Arya, 2004; DisplaySearch, 2010; von Tunzelman, 1978; Honda, 2010; Hirsh, 1989; Koh and Magee, 2008; UNCTD, 2006; Airbus 2012 List Prices; Wikipedia, 2012; Morrel, 2007)
  26. 26. Items 7 and 8, Engines Note scaling on left and pictures of steam engine, modern day equivalent (steam turbine), and 90,000 HP marine engine Cost of cylinder or piston is function of cylinder’s surface area (πDH) Output of engine is function of cylinder’s volume 2 (πD H/4) Result: output rises faster than costs as diameter is increased
  27. 27. Outline of Results   Creating materials (and their associated processes) that better exploit physical phenomena Geometrical scaling ◦ Reductions in scale: e.g., integrated circuits (ICs), magnetic storage, MEMS, bio-electronic ICs ◦ Increases in scale: e.g., larger production equipment, engines, oil tankers  Some technologies directly experience improvements while others indirectly experience them through improvements in “components” ◦ Computers and other electronic systems ◦ Telecommunication systems
  28. 28. Item 16, Computers Note the similar levels of improvements between 1960 and 2000 (about 7 orders of magnitude) Source: ICKnowledge, 2009; Koh and Magee, 2006) As one computer designer argued, by the late 1940s computer designers had recognized that “architectural tricks could not lower the cost of a basic computer; low cost computing had to wait for low cost logic” (Smith, 1988)
  29. 29. Items 18 and 19, MRI and CT  Improvements in MRI and CT were driven by improvements in computers and they were driven by improvements in ICs Quote by Trajtenberg (1990)  Quotes from Kalendar, 2006  ◦ “However, it was not until the advent of microelectronics and powerful mini-computers in the early seventies, the early seventies, coupled with significant advances in electro-optics and nuclear physics, that the revolution in imaging technologies started in earnest. Computed Tomography scanners came to epitomize this revolution and set the stage for subsequent innovations, such as………..and the wonder of the eighties, Magnetic Resonance Imaging” ◦ “Computed tomography became feasible with the development of modern computer technology in the 1960s”
  30. 30. Item 25, Wireless Transport Note reductions in feature sizes, which were needed for new cellular systems
  31. 31. Discussion/Conclusion  Most observed improvements can be categorized into two mechanisms: ◦ 1) creating materials (and their processes) to better exploit their physical phenomena ◦ 2) geometric scaling   Some technologies directly realize improvements through these two mechanisms while higher-level “systems” indirectly benefit from improvements in “components” Of 33 different technologies and 52 dimensions of performance, these mechanisms explain improvements for 31 technologies and 50 dimensions ◦ the exceptions are laboratory concentration of penicillin and laboratory cooling
  32. 32. Summary Statistics Mechanism Specific Technologies in Table 1 by Item Number Number of Technologies Creating Materials 1, 2, 3, 4, 5, 6, 10, 11, 12, 20, 28, 30, 31 Scale Reduction 14, 15, 21, 22, 23, 26 6 Scale Increase 7, 8, 13, 17, 29 4 Component improvement 9, 16, 18, 19, 24, 25, 32 7 Components benefit from 14 9, 32 2 16, 18, 19, 24, 25 5 creating materials Components benefit from reductions in scale Components benefit from 0 increases in scale Other, Unknown Total 27 (Penicillin), 33 (Laboratory Cooling) 2 33
  33. 33. Summary Statistics  Creating materials  Reductions in scale  Increases in scale ◦ Lighting (1,2,3), GaAs Lasers (4), Photosensors (5), Solar Cells (6), battery (10), capacitor (11), flywheel (12), organic transistors (20), crop yields (28b), magnetic materials (30, 31) ◦ Through components: Electric Motors (9), machine tools (32) ◦ ICs (14), MEMS (15), magnetic storage (21-22), optical storage (23), DNA sequencing (26) ◦ Through components: Computers (16), MRI (18), CT (19), wireline (24), wireless (25) ◦ engines (7, 8), LCDs (17), energy transmission (13), transport (29)
  34. 34. Creating Materials      Leads to orders of magnitude improvements when scientists and engineers create new forms of materials and do this with new processes Sometimes these improvements involve new classes of materials We identified new classes of materials for all of the “material creation” technologies except two of them (photosensors, lasers) Without these new classes, the range of improvements might well be reduced below those achieved and documented earlier Improvements done mostly in laboratories, not in factories
  35. 35. Geometric Scaling Impacts on some technologies through both reductions and increases in scale  In both cases, large changes in both product and process design were implemented with each increment requiring non-trivial redesigns  Reductions in scale provide a mechanism for rapid rates of improvements in ICs, magnetic storage, MEMS, and DNA sequencing equipment  ◦ involved better processes that often involve completely new forms of equipment and materials ◦ new equipment usually developed and implemented in labs ◦ rapid improvements in many higher-level “systems” were achieved through improvements in ICs and other components that benefit from reductions in scale
  36. 36. Relationship with Learning (1)  Results provide a deeper understanding of learning in a technological context than do current models ◦ they provide new insights into technological diffusion (Griliches, 1957; Mansfield, 1968) and productivity growth (Solow, 1956)  The technology diffusion and productivity growth literatures pay little attention to improvement rates ◦ but it seems apparent that rapid improvement rates lead to earlier economic feasibility and faster rates of diffusion and productivity growth
  37. 37. Relationship with Learning (2) More attention to improvement rates is required in research on technological change  The two mechanisms provide an initial operational explanation for why some technologies experience rapid rates of improvement over long periods of time  ◦ that is superior to any explanation that might come from current theories such as the learning curve (Wright 1936; Arrow 1962; Argote and Epple 1990; Ayres 1992)
  38. 38. Relationship with Learning (3) Incremental modification of equipment that is emphasized by learning curve is one part of both mechanisms but it is not the most important part of the mechanisms  It is in process side of both creating materials and geometric scaling 
  39. 39. Relationship with Learning (4)  Nevertheless, incremental modifications of equipment cannot explain many orders of magnitude improvements ◦ In fact, learning from production cannot explain even small improvements in a per mass or volume basis since such improvements clearly involve something more basic about the artifact than just small changes in processes ◦ Our work identifies the creation of new materials and large reductions in scale as the changes responsible for rapid improvements and such learning requires R&D activities and not necessarily cumulative production
  40. 40.  Thank You Appendix
  41. 41. Item 4 (GaAs Lasers) Heat sink: heat must be removed in order to prevent overheating of laser Mirror: contaminants in mirror cause light to be focused on a spot and thus burn up the mirror Processes 1) Fewer defects can have large impact on maximum power because small reduction in defects can lead to much higher power 2) Faster processes leads to lower costs Source: Martinson R 2007. Industrial markets beckon for high-power diode lasers, Optics, October: 26-27. Personal Communication with Dr. Aaron Danner
  42. 42. Item 5, Photosensor Note the names of the process and material changes Source: T. Suzuki, “Challenges of Image-Sensor Development”, ISSCC, 2010
  43. 43. Item 6, Solar Cells Note the different materials for each set of data points More details on each set of data points can be found in various sources. For crystalline silicon, see Green M, 2009. The Path to 25% Silicon Solar Cell Efficiency: History of Silicon Cell Evolution, Progress in Photovoltaics 17: 183-189
  44. 44. Item10, battery Note the names of different materials Source: Koh and Magee, 2008; Tarascon, 2009). For more details see Tarascon, J , 2010. Key Challenges in future Libattery research. Philosophical Transactions of the Royal Society 368: 3227-3241
  45. 45. Item11, Capacitors. Note that energy density is a function of capacitance times voltage squared and the names of different materials Sources: Koh and Magee, 2008; Naoi and Simon, 2008)
  46. 46. Item12, Flywheels. Note that energy density is a function of mass times velocity squared and stronger materials (carbon fiber) enable higher speeds Sources: Koh and Magee, 2008; Renewable and Sustainable Energy Reviews 11(2007): 235-258
  47. 47. Item 28b, Crop Yields for Corn Note the different material classes and the improvements for each of them Source: Troyer, 2006
  48. 48. Magnetic Materials (items 30 and 31)
  49. 49. Item 15, MEMS for Inkjet Printers Note the reductions in scale that accompany increases in the number of nozzles Source: Stasiak et al 2009 Quote for MEMS from (Stasiak et al, 2009): “The development of compact firing chamber architectures enabled smaller ejected drop volumes and higher nozzle packing densities. The smaller drops required less firing energy per drop for increased frequency and higher throughput. Furthermore, the smaller drops provided more colors per dot, lighter tones, and photo-quality printing on a wide variety of media.”
  50. 50. Magnetic (Items 21 and 22) and Optical (Item 23) Storage Density Note that increases in density can only be achieved by making storage areas smaller For more details, see (Daniel et al, 1999; Esener et al, 1999)
  51. 51. Item 13. Energy Transmission Higher voltages lead to lower losses per mile because losses are a function of surface area (function of radius) and transmission is a function of volume (function of radius squared) (AEP, 2008)
  52. 52. Item 17, LCDs. Sources: Television Making: Cracking Up, Economist, Jan 21, 2012, p. 66. (Keshner and Arya 2004; Display Search 2010)
  53. 53. Item 29, Ratio of GDP to transport sector, Aircraft Passenger Times Speed Aircraft and aircraft engines benefit from increases in scale  Other transportation equipment (freight vessels, oil tankers, trucks) benefit from increases in scale  Better computers also have an impact 
  54. 54. From 1807 tons in 1878 To 500,000 tons in 2009 Oil Tankers
  55. 55. From 10 HP (horse power) in 1817 To 1,300,000 HP today (1000 MW) Steam engine Their modern day equivalent: steam turbine
  56. 56. Electricity Generating Plants Edison’s Pearl Street Station in NY City (1880) More Recent Plant From Kilowatts (125 HP engine) to Giga-Watts
  57. 57. From DC-1 in 1931 (12 passengers, 180 mph) To A-380 in 2005 (900* passengers, 560 mph) *economy only mode *Economy only mode
  58. 58. Relative Price Per Output Falls as Scale Increases 10000 Oil Tanker: 1000s of tons Smallest was 1807 tons Steam Engine (in HP) Maximum scale: 1.3 M HP Relative Price per Output 1000 Marine Engine Largest is 90,000 HP Commercial aircraft Smallest one had 12 passengers 100 Aluminum (1000s of amps) Electric Power Plants (in MW); much smaller ones built 10 LCD Mfg Equip: Largest panel size is 16 square meters 1 0.1 1 Chemical Plant: 1000s of tons of ethylene per year; much smaller plants built 10 100 1000 Output (Scale) 10000
  59. 59. Improvements in Computations Per Second (Koomey et al, 2011) Why do computers experience improvements in processing speed? Are these large (or small) improvements in processing speed? How many other products experience such large improvements?
  60. 60. Item 24, Wireline Transport Figure 2.9 Reductions in Optical Loss of Optical Fiber Based on personal communication with Dr. Aaron Danner 1000 Optical Loss (db/km) 100 10 1 0.1 0.01 1960 1965 1970 1975 1980 1985 Source: NAS/NRC, 1989. Source: Koh and Magee, 2006 of lasers and fiber Source: Fiber-Optic Communication Systems, Govind P. Agrawal, Institute of Optics, University of Rochester
  61. 61. Item 9, Electric Motors: Better materials were needed for stronger magnets Source: Koh and Magee, 2008
  62. 62. Item 32, Machine Tools Improvements in material strengths led to faster cutting speeds. Note the materials listed in the right hand figure. Source: American Machinist, 1977