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2. Session Technology 1 Objectives and overview of course 2 When do New Technologies Become Economically Feasible? 3 Two types of improvements: 1) Creating materials that better exploit physical phenomena; 2) Geometrical scaling 4 Semiconductors, ICs, electronic systems 5 MEMS and Bio-electronic ICs 6 Nanotechnology, DNA sequencing 7 Lighting, laser diodes, and Displays 8 Human-computer interfaces (also roll-to roll printing) 10 Superconductivity and Solar Cells 11 Feedback on Group Slides 12-13 Group Presentations This is Second Session of MT5009 3. Outline Science, technology, innovation, and economic feasibility Supply and demand curves and economic feasibility Myths and realities about technology (i.e., supply curve) change #1: Performance vs. time curves resemble an S-curve #2: Slowing rate of improvement in old technology drives development of new technology #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle #4: Costs fall as cumulative production rises in learning curve #5: All technologies have the potential for rapid rates of improvements An analysis of breakthrough technologies predicted by MITs Technology Review 4. Science, Technology, and Innovation: Different People, Different Terms Research (Basic, Applied) and Development Science, Technology, Commercialization Invention (proof of concept), Innovation (commercialization of discontinuity/concept), Diffusion of discontinuity/concept Technological Discontinuity: based on new concept that comes from advances in science (sometimes called radical or disruptive innovation) In any case, technologies proceed through stages of scientific, technical and economic feasibility advances in science often continue throughout these stages and contributes towards improvements 5. Technological Discontinuities This term is widely used in courses on technology management One reason they are discussed is because Incumbents often fail to effectively commercialize them and thus lose substantial market share in the new technology Thus discontinuities represent large opportunities for new entrants It is also important to understand the concept that forms the basis for the technology Because this helps us understand the potential for improvements 6. Steam-powered fire engine Technological Discontinuities: What was change in concepts? Old Technology New Technology, i.e., Discontinuity Early Benz (1894) Wright Brothers (1904) Gliders (19th Century) 7. Returning to the Different Terms We can think of them as different stages over time Research (Basic, Applied) and Development Science, Technology, Commercialization Invention (proof of concept), Innovation (commercialization of discontinuity/concept), Diffusion of discontinuity/concept Scientific, technical, and economic feasibility Diffusion is often the last step and depends on economic (in addition to scientific and technical) feasibility 8. The Last Stage is Diffusion and Within Diffusion there are many Stages 9. Diffusion and Economic Feasibility How can we better understand economic feasibility and timing of diffusion? When might a technology become economically feasible? When might it begin diffusing? Or when might it diffuse to a larger number of customers? We can distinguish between economic feasibility and the organizational challenges of implementing new technologies Answers depends on a variety of factors But first we must think about Supply & demand curves and five myths of technology change 10. Outline Science, technology, innovation, and economic feasibility Supply and demand curves and economic feasibility Myths and realities about technology (i.e., supply curve) change #1: Performance vs. time curves resemble an S-curve #2: Slowing rate of improvement in old technology drives development of new technology #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle #4: Costs fall as cumulative production rises in learning curve #5: All technologies have the potential for rapid rates of improvements An analysis of breakthrough technologies predicted by MITs Technology Review 11. Quantity (Q) Price (P) q p What do Demand and Supply Curves Mean and what do they have to do with Diffusion? Demand Supply 12. What are some problems with last Slide? 13. What are some problems with last slide? Previous slide assumes performance is unimportant In reality, performance is important Market evaluates products and services in terms of price and a variety of performance dimensions Difficult to represent multiple dimensions on a two- dimension graph, so most graphs only show price and quantity Lets consider performance, which assumes price is held constant 14. Quantity (Q) Performance (P) q p In terms of performance, What do Demand and Supply Curves Mean and what do they have to do with Diffusion? Supply Demand 15. Price, Performance, and Demand Price and performance determine the amount of demand and supply Rising performance often leads to growing demand Falling price often leads to growing demand But what about before there is a market (i.e., no commercial production)? When performance is too low? Or when price is too high? How can we represent these dynamics with supply and demand curves? 16. 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 17. 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 18. 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 Typical movement of demand curve over time Typical movement of supply curve over time 19. 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 demand curve over time Typical movement of supply curve over time 20. Whether we Focus on Performance or Price Demand and supply curves help us think about important issues Impact of falling price or increasing performance on demand Levels of performance and price that are needed before a technology becomes economically feasible Other factors impact on diffusion such as standards, regulations, and organizational issues Demand and supply curves can also help us to think about the first Products to diffuse First value propositions First designs Markets to accept this diffusion First customer segments First customers within segments First sales channels 21. But What Drives Changes in Demand and Supply Curves? Demand Curves Increases in income Changes in competing and complementary technologies Changes in consumer preference Supply Curves Predominant viewpoint is demand drives improvements on factory floor and thus changes in supply curves But this ignores the reality of R&D universities and other labs improve technologies long before the technologies are commercially produced Lets understand the myths and realities of technology change 22. Outline Science, technology, innovation, and economic feasibility Supply and demand curves and economic feasibility Myths and realities about technology (i.e., supply curve) change #1: Performance vs. time curves resemble an S-curve #2: Slowing rate of improvement in old technology drives development of new technology #3: Product design changes drives performance increases and process design changes drives cost reductions, with product preceding process design changes in life cycle #4: Costs fall as cumulative production rises in learning curve #5: All technologies have the potential for rapid rates of improvements An analysis of breakthrough technologies predicted by MITs Technology Review 23. Time Performance Myth vs. Reality of Performance vs. Time Curves on Logarithmic Scale Slowdown and Limits Acceleration Time Performance (logarithmic scale) a. The Myth b. The Reality Note: limits exist but they are often further away than ordinarily thought 24. The Theory for Purported S-Curves Improvements accelerate as research funds moved from old to new technology in response to increases in demand for new technology or to slowdown in rate of improvement in old technology (Foster, 1986; Garcia and Clantone, 2002; Utterback, 1994) Acceleration may also occur as technology is better understood by scientists and firms, constraints are overcome, and complementary technologies developed and implemented (Butler, 1988) For later part of purported S-curve, rates of improvement slow as cost of marginal improvements increases and natural limits emerge research funds then move to still newer technology and thus acceleration in newer technologys rate of improvement (Foster, 1986; Butler, 1988; Utterback, 1994) 25. S-Curves make it easy to fall for hype 26. Lets look at some real data Mostly straight lines on a logarithmic plot but with some deviations 27. 0.01 0.1 1 10 100 1965 1975 1985 1995 2005 b. Lumens per Dollar (RedLEDs) vs. Time 0.001 0.01 0.1 1 10 1940 1960 1980 2000 2020 c.KwHours per Dollarvs. Timefor CrystallineSilicon Solar Cells 10 100 1000 2002 2006 2010 2014 e. Current (Amps) x Length (meters) vs.Time for YBaCuO Superconductor 100 1000 1985 1990 1995 2000 d. Energy (Joules)Per Volume (cc)vs. Time for Li-ionBatteries 0.001 0.1 10 1000 1960 1980 2000 2020 White Red a.Lumens per Packagefor LEDs vs.Time 0.1 1 10 100 1970 1980 1990 2000 2010 c. EfficiencyofAmorphous Siliconand OrganicSolar Cells vs.Time Amorphous Silicon Organic Limit? Slowdown? 28. 0.001 0.01 0.1 1 10 1980 1990 2000 2010 2020 h. Millions of Pixels per Dollar vs.Time for Camera Chips 1 10 100 1985 1990 1995 2000 2005 2010 i.Light Sensitivity(mV/sq micron) vs. Time for CameraChips 0.001 0.01 0.1 1 1985 1995 2005 j.1/Pixel Size (sqmicron) vs.Time for Camera Chips 0.0000001 0.00001 0.001 0.1 10 1980 1990 2000 2010 k. Mobility (cm2/Volt-sec)of Organic Transistors vs.TIme 0.000001 0.0001 0.01 1 100 1950 1970 1990 2010 Micro- processors DynamicRandom AccessMemory (DRAM) MOS Logic f. Millions of Bits per Chipvs. Time 0.0001 0.01 1 100 1950 1970 1990 2010 DynamicRandom Access Memory Flash Memory g.Millions of Memory Bits/Dollar vs. Time Slow down? 29. 1.E+01 1.E+04 1.E+07 1.E+10 1.E+13 1.E+16 1930 1950 1970 1990 2010 l.Computations/kwhour vs. Time 1E-09 0.000001 0.001 1 1000 1940 1960 1980 2000 m. Thousands of Computations/ Second/Dollar vs.Time 0.00001 0.001 0.1 10 1000 1970 1990 2010 n. CTScanner: 1/(Scan Time x Resolution) vs.Time 0.001 1 1000 1000000 1950 1970 1990 2010 o. Millions of BIts per Sqare Inch vs. TIme for MagneticDisks 0.1 10 1000 100000 1950 1970 1990 2010 p. Millions of Bits per Volume (cc) vs. Time forMagnetic Tape 0.001 0.1 10 1000 1950 1970 1990 2010 q. Millions of BIts/Dollarvs.Time forMagnetic Tape Slowdown? Acceleration? Acceleration? Acceleration? 30. 0.001 0.1 10 2001 2003 2005 2007 2009 2011 2013 PhaseChangeRAM Ferro-electric RAM Magnetic RAM r.Storage Capacity(GB)per Memory Chip vs.TIme 0.001 0.01 0.1 1 10 1965 1970 1975 1980 1985 s.Distanceper Loss (km/decibel) vs.Time for OpticalFiber 0.1 10 1000 100000 1980 1990 2000 2010 t. LastMileBandwidth (1000s of bits/sec)vs.Time 0.01 0.1 1 10 100 1000 1980 1990 2000 2010 u. Millions of Bits per Second vs.Time for Cellular Telecom 0.0001 0.01 1 100 10000 1000000 100000000 1970 1980 1990 2000 2010 v. Sequenced BasePairs per Dollarvs. Time Acceleration Acceleration? 31. 100 1,000 10,000 100,000 1920 1930 1940 1950 1960 1970 1980 y. Aircraft PassengerMiles per Hour vs.Time 1980 1990 2000 2010 0.1 1 10 2000 2005 2010 x. Output(liters)perDollar vs.Timefor Cellulosic Ethanol 1970 1980 1990 2000 2010 32. No Evidence for an S-Curve None of the 32 time-series curves display classical S- curve Second half of S-curve, i.e., limits, only evident in one technology, best laboratory efficiency of amorphous silicon solar cells (Figure 1.c) However, output (kwHours) per dollar is probably still rising similar to output per dollar for crystalline silicon solar cells (See Figure 1.c.). Another data base (Economist, 2012) shows continued reductions in cost beyond 2003 (Nemet, 2005), which is last data point Figure 1.c 33. No Evidence for an S-Curve (2) First half of S-curve, i.e., acceleration, only evident in one technology, cellular telecommunications (Figure 1.u) This acceleration is expected since cellular phones were first used for voice communication data speeds only became important in late 1990s as displays reached levels necessary for data speeds to become important; this explanation is consistent with the theory of S- curves (Foster, 1986; Butler, 1988; Utterback, 1994) Better measure of performance for early years of cellular phones would probably be number of voice conversations possible per unit of spectrum We will show this data later this semester. Not enough data points, but straight line 34. No Evidence for an S-Curve (3) Several other curves deviate from straight line Slowdowns Mobility of organic transistors Computer tomography But this doesnt mean limits will soon be reached since other technologies have seen slowdowns followed by accelerations Magnetic recording density of tape and disks (due to introduction of giant magneto resistance) Number of sequenced DNA base pairs - due to introduction of new technology (454, Illumina) 35. Reality Most curves more closely resemble a straight line on a logarithmic plot than an S-curve What does a straight line on a logarithmic plot mean? Lets look at the statistical analysis Linear model Logarithmic model Logarithmic model with time squared 36. Technology Dimensions of measure Number of Data Point Linear Model Log. Model Log Model with Time2 Term R-Sq. P-Value R-Sq. P-Value R-Sq. P-Value Sign of T2 Light Emitting Diodes (LEDs) Lumen/package, red 15 .29 .02 .98