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2022 | OriginalPaper | Buchkapitel

4. Quantifying Technological Progress

verfasst von : Olivier L. de Weck

Erschienen in: Technology Roadmapping and Development

Verlag: Springer International Publishing

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Abstract

Technology is not static. It evolves over time. A key question is how to “properly” measure and therefore quantify technological progress. Being able to do so is important in order to set realistic targets for improvements of known technologies and to establish some estimates as to what new technologies would have to be able to achieve, in order to displace incumbent technologies and the products and services that use them. This chapter first discusses how to define figures of merit (FOMs) for quantifying technological progress over time and then applies these FOMs to analyze technology trajectories. We present three complementary models, including the S-curve, exponential improvement (also known as Moore’s Law), as well as Pareto front shifts as a way to track technological progress in multiple dimensions and relative to fundamental asymptotic limits.

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Vorheriges Kapitel Nature and Technology

Nächstes Kapitel Patents and Intellectual Property

Technology FOMs are distinct from the so-called Key Performance Indicators (KPIs) that are primarily used in project management and business to assess organizational performance.

This highlights the fact that quantifying the cost or labor efficiency of historical or ancient technologies is not trivial, since the technology will usually predate the existence of any particular currency. Instead, one may attempt to normalize the cost by other quantities such as one hour of human labor, or the price of wheat in the Roman Empire (Kessler et al., <CitationRef CitationID="CR3" >2008</Citation Ref>).

The scaling factor is 10⁹ in this case to account for the millions of instructions per second, 10⁶, multiplied by 10³ for thousands of dollars.

A singularity is a sudden disruption or shift in a mathematical function or phenomenon. A technological singularity (Kurzweil <CitationRef CitationID="CR5" >2005</Citation Ref>) is a point-of-no-return whereby technologies, and computers, in particular, become so intelligent that they can improve themselves at an ever-faster rate and eventually exceed human capabilities, potentially rendering us obsolete.

It is important to note that the horizontal lines in Fig. <InternalRef RefID="Fig1" >4.1</Internal Ref> do not represent asymptotic limits, that is, threshold values of technology, that can never be exceeded. The existence of such asymptotic values will be discussed later in this chapter. For purposes of policy-making, the value of a human life is often estimated, for example, to establish an upper threshold for the cost and benefit of medical interventions to save a human life. The World Health Organization (WHO) recommends using three times the GDP/capita/year as such a threshold.

Exponential progress occurs when a technology improves at roughly a constant percentage year-over-year, leading to a compounding effect, similar to financial investments that achieve a positive annual rate of return.

We discuss below the reasons why some technologies progress faster than others.

A processor is a machine or device that facilitates the matter transformation.

In general in matter transforming processes there is conservation of mass, that is, the mass of the inputs needs to equal the mass of all outputs. There are exceptions, as in nuclear reactions, where mass equivalence of energy, E=mc², needs to be taken into account and the mass of inputs and total mass of outputs may not be equal.

Much has been said and written recently about the “information revolution” in society (see Chap.2), giving the impression that “hardware”-centric technologies such as those used for mining, making chemicals, metals, food, and other materials are no longer important. Nothing could be further from the truth.

The technology progression discussed here is specifically for Electric Arc Furnaces (EAF), see here for details about electric arc furnaces: https://en.wikipedia.org/wiki/Electric_arc_furnace

Note that here r is negative, since the FOM decreases over time. In general, it is preferable to define technological FOMs that increase as the technology improves. The annual rate of improvement, r, was estimated using a least squares optimization to minimize the error between the actual technology improvement data (shown in Fig. <InternalRef RefID="Fig4" >4.4</Internal Ref>) and the calculated improvement obtained by determining r in Eq. (<InternalRef RefID="Equ2" >4.2</Internal Ref>).

Chapter 12 is dedicated to the topic of technology infusion analysis.

Residential electricity rates in the United States vary from state to state in the range from 10 to 23 [¢/kWh]. The electricity cost for EAF is typically on the order of 100 [$/MWh] as of 2020.

It is not advised to use percentages or indices as a technological FOM, unless it is very clear what was used as a reference for normalization purposes. Comparing the progression of different technologies that use different reference baselines is not valid.

The word “exhibits” in OPL is reserved for attribute links.

The company ArcelorMittal is the largest steelmaker in the world today with a total annual production volume approaching 100 million tons. The company began in the 1980s by converting older inefficient BOF furnaces to EOF (energy-optimized furnaces) by introducing a preheating system for scrap steel, using heat from off-gassing for the scrap preheater.

Normally, the efficiency of a machine cannot exceed 1.0 (or 100%) since there can usually not be more work generated than energy that enters the system boundary. An exception to this rule may be fusion reactors (energy conversion) where the goal is to achieve a fusion energy gain factor of at least Q=1, better Q=10, which is the ratio of energy released by the plasma over the external energy input needed to heat and maintain the plasma. The mega-project ITER which is being built in Southern France is aiming at Q=10.

For details, refer to Chap. 17.

An interesting challenge is how to quantify the productivity or cost of ancient technologies, before the introduction of modern currencies, such as the Euro €, or time accounting systems.

Note that the selection of specific FOMs to compare different technologies may create a differential advantage of one technology over the other.

However, SI units are preferred to facilitate international comparisons of technologies.

Some FOMs for a technology might improve, such as the maximum power that can be generated [W], while other FOMs for that same technology get worse, for example, [kg CO₂ /W].

As we will see later, there can also be a significant correlation between the performance of a technology in terms of its FOMs and the market share of the associated product(s), see Chap. 12.

There are no prescribed rules for how to arrange the elements on an Object Process Diagram. However, it is good to be consistent, such as placing inputs on the left and outputs on the right.

We will revisit this point in Chap. 7 when we discuss the “Innovator’s Dilemma,” which is related to the fact that new niche markets can emerge over time that value other FOMs more heavily, than those that are weighted most heavily in the main market where the competition between the primary market actors takes place. An example of this is the emergence of compactness (small volume) for portable applications in the computer disk drive market. An important trend in technology development is the emergence of sustainability-related FOMs that capture the amount of waste produced by a particular product, system, or technology. The goal is to reduce waste, thus increasing sustainability and compatibility of such technologies and products with nature (see Chap. 3).

The unit here in Eq. (<InternalRef RefID="Equ3" >4.3</Internal Ref>) is [y^-1]=[1/y], indicating the average progress made per calendar year.

An example of a fundamental limit is c, the speed of light. See Chap. 22 for a discussion on limits.

Another issue with the linear model in Fig. <InternalRef RefID="Fig7" >4.7</Internal Ref> is the negative intercept of the y-axis which may be nonphysical.

The important difference between the linear and the exponential model is that in the linear model the annual improvement is fixed on an absolute scale, whereas in the exponential model the annual rate of improvement is a fixed (average) improvement relative to the prior year. This leads to a compounding effect, similar to the balance in a savings account which increases at a fixed annual rate, assuming no withdrawals. The result is exponential growth as in Fig. <InternalRef RefID="Fig8" >4.8</Internal Ref>.

This was first applied to the diffusion of hybrid corn seed by Griliches based on data from the 1930s and 1940s and first published in 1957.

Or any of the other categories of FOM listed in section 4.1.

An interesting question is whether there is a relationship between the shape of the S-curve and the number of competitors involved in a particular technology. We discuss this point in Chap. 7 and especially in Chap. 10 (competition as a driver for technology).

This may be both a conservative and realistic prediction as in 2020 the world record for multijunction solar cell efficiency stood at 47.1% for a six-junction solar cell (6-J) at NREL with 143x solar concentration. The parameters for the blue prediction curve in Fig. <InternalRef RefID="Fig14" >4.14</Internal Ref> are a=3.75, b=2.5, c=11.75, m=-10, n=2, and 𝜏=13.

The Technology Readiness Level (TRL) scale goes from 1 to 9 and captures the degree of maturation of a technology all the way from a mere idea (such as a sketch on a cocktail napkin) to a certified product or service available in the marketplace. More discussion on the TRL scale follows in Chaps. 8 and 16.

The “utopia point” is a mathematical concept from multiobjective optimization and multi-criteria decision-making, and it represents the best value along each separate FOM dimension that is achievable. The utopia point itself is not achievable since it ignores the existence of tradeoffs and constraints; however, it represents an aspirational goal or target for a technology to move toward over time.

We will discuss the role of constraints and so-called Lagrange multipliers (“shadow prices”) in technology development in Chap. 11 on technology sensitivity analysis.

TSFC = thrust-specific fuel consumption in units of [kg/s/N] is a normalized measure of fuel efficiency for aircraft engines that allows to compare engines across different generations

Recently, there is a debate whether Moore’s law is running out of steam, that is, slowing progress. So far, however, there is no such evidence for a slowdown.

A true doubling every 2 years would require an annual rate of about 41%. The rate of 37% per year observed in computing over the last 50 years (see Figs. <InternalRef RefID="Fig1" >4.1</Internal Ref> and <InternalRef RefID="Fig22" >4.22</Internal Ref>) comes very close to that. Our case studies in Chaps. 13 and 18 will exceed even these rates of improvement.

Several of these questions are the subject of active research in academia and in industry and may not have a definitive answer yet. Chapter 7 will discuss in some more detail the topic of technology transitions.

See Chap. 22 for a further discussion on this topic, including the potential existence of a technological singularity.

American Iron and Steel Institute, Steel Industry Technology Roadmap, December 2001, Committee led by Mark Atkinson and Robert Kolarik URL: https://steel.org/~/media/Files/AISI/Making%20Steel/manf_roadmap_2001.pdf

de Filippi, R. et al. “High Speed Rail Safety”, Technology Roadmap created at MIT in 16.887, 2019, URL: https://roadmaps.mit.edu/index.php/High-Speed_Rail_Safety

de Weck, O. (2017). Lectures on Technology Progress, SDM Core, Massachusetts Institute of Technology, EM.412

de Weck, Olivier L., Daniel Roos, and Christopher L. Magee. Engineering systems: Meeting human needs in a complex technological world. Mit Press, 2011

Kessler, D., & Temin, P. (2008). Money and prices in the early Roman Empire. The monetary systems of the Greeks and Romans. 2008 Feb 14:137–59.

Koh, Heebyung, and Christopher L. Magee. "A functional approach for studying technological progress: Application to information technology." Technological Forecasting and Social Change 73, no. 9 (2006): 1061-1083.CrossRef

Kurzweil, Ray. “The singularity is near: When humans transcend biology”. Penguin, 2005.

Magee, Christopher L., Subarna Basnet, Jeffrey L. Funk, and Christopher L. Benson. “Quantitative empirical trends in technical performance.” Technological Forecasting and Social Change 104 (2016): 237-246.

Moore, G. E. (1965). “Cramming more components onto integrated circuits” (PDF). Electronics Magazine. p. 4. Retrieved 2006-11-11.

Rogers, Everett M. “Diffusion of innovations”. Simon and Schuster, 1962

Smaling, R. and de Weck O.. "Assessing risks and opportunities of technology infusion in system design." Systems Engineering 10, no. 1 (2007): 1-25.CrossRef

Solow, R.M. (1957). Technical change and the aggregate production function. The Review of Economics and Statistics 1:312–20. 1957 Aug

Titel: Quantifying Technological Progress
verfasst von: Olivier L. de Weck
Verlag: Springer International Publishing
Buch: Technology Roadmapping and Development
Print ISBN: 978-3-030-88345-4

Electronic ISBN: 978-3-030-88346-1

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-88346-1_4

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