Technology forecasting

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Technology forecasting attempts to predict the future characteristics of useful technological machines, procedures or techniques. Researchers create technology forecasts based on past experience and current technological developments. Like other forecasts, technology forecasting can be helpful for both public and private organizations to make smart decisions. By analyzing future opportunities and threats, the forecaster can improve decisions in order to achieve maximum benefits. [1] Today, most countries are experiencing huge social and economic changes, which heavily rely on technology development. By analyzing these changes, government and economic institutions could make plans for future developments. However, not all of historical data can be used for technology forecasting, forecasters also need to adopt advanced technology and quantitative modeling from experts’ researches and conclusions. [2]

Contents

History

Technology forecasting has existed more than a century, but it developed to an established subject until World War II, because American government started to detect the technology development trend related to military area after the war. In 1945, the U.S. Army Air Forces created a report called Toward New Horizons, which surveyed the technology development and discussed the importance for future studies. The report is an indication for the beginning of modern technology forecasting. [3] In the 1950s and 1960s, RAND Corporation developed the Delphi Technique and were widely accepted and used to make smart evaluation for the future. [4] The applications of Delphi Technique are a turning point in the history of technology forecasting, because it became an efficient tool for knowledge building and decision-making, especially for social policy and public health issues. [5] In the 1970s, private sector and government agencies out of military area widely adopted technology forecasting and helped to diversify the users and applications. As the developments of computing technology, advanced computer hardware and software facilitates the process of data sorting and data analysis. The development of Internet and networking is also beneficial for the data access and data transfer. [6] Technology opportunities analysis started since 1990. Improved software can help analysts search and retrieve data information from large complicated database and then graphically represents interrelations. [7] From 2000, more and more new requirements and challenges lead to the modern development of technology forecasting, such as prediction markets, alternate reality games, online forecasting communities and obsolescence forecasting. [3]

Important aspects

"I think we have a cultural affinity for technology that reflects optimism, but we all make poor forecasts." —Jim Moore, director of the Transportation Engineering Program at the University of Southern California [8]

Primarily, a technological forecast deals with the characteristics of technology, such as levels of technical performance, like speed of a military aircraft, the power in watts of a particular future engine, the accuracy or precision of a measuring instrument, the number of transistors in a chip in the year 2015, etc. The forecast does not have to state how these characteristics will be achieved.

Secondly, technological forecasting usually deals with only useful machines, procedures or techniques. This is to exclude from the domain of technological forecasting those commodities, services or techniques intended for luxury or amusement.

Thirdly, feasibility is a key element in technology forecasting. Forecasters should consider the cost and the level of difficulty of materialization of desires. For example, a computer-based approach “Pattern” is an expensive forecasting method which is not recommended to be used in cases of restricted funds. [2]

Methods

Commonly adopted methods and tools of technology forecasting include the Moore's law, [9] Write's law and Goddard law, [10] which generate quantitative assessments for technology progress, the Delphi method, forecast by analogy, growth curves, extrapolation and horizon scanning. [11] [12] [13] Normative methods of technology forecasting—like the relevance trees, morphological models, and mission flow diagrams—are also commonly used. Delphi method is widely used in technology forecasts because of its flexibility and convenience. However, the requirement on reaching consensus is a possible disadvantage of Delphi method. Extrapolation can work well with enough effective historical data. By analyzing the past data, forecaster extend the past development tendency in order to extrapolate meaningful outcomes in the future. [14]

Several technology forecasting methods [15] [16] [17] [18] base their prediction on the interaction between markets and technologies.  While technology progress enables firms to launch improved or new products, potential market provides the incentives for R&D investments and market success provides the funding for further R&D.

Combining forecasts

Studies of past forecasts have shown that one of the most frequent reasons why a forecast goes wrong is that the forecaster ignores related fields. [19] A given technical approach may fail to achieve the level of capability forecast for it, because it is superseded by another technical approach which the forecaster ignored. Another problem is that of inconsistency between forecasts. The inconsistency between forecasts reflects on the different locations and time used on controlled experiment. It usually produces inaccurate and unreliable data which leads to incorrect insight and faulty predictions. [20] Because of these problems, it is often necessary to combine forecasts of different technologies. In addition, the use of more than one forecasting method often gives the forecaster more insight into the processes at work which are responsible for the growth of the technology being forecast. Combining forecasts can reduce errors compare with a singular forecast. In the case when researches face troubles to pick a typical forecast method, combining forecasts are always the best solution. [21]

AI in technology forecasting

AI is starting to gain widespread adoption across industries, including technology forecasting. For example, an AI-powered method developed by Focus (company, based in Rotterdam, The Netherlands) uses patent data to estimate how fast emerging technologies are going to improve.

The method leverages machine learning to scan existing technologies in specified areas, filters out irrelevant ones based on user context, and finally estimates improvement speeds for each technology based on indicators hidden in patent data. The methodology behind it is based on scientific research and was developed in a collaborative effort with MIT [22]

Relative researches and Applications

Forecasting institutes

Scientific Journals

Uses in manufacturing

Technology forecasting heavily relies on data and data makes contributions to manufacturing and Industry 4.0. IoT System provides a strong platform to make predictive analysis in the post-Industry 4.0. The advanced technologies will increase forecasting accuracy as well as reliability. As the rapid development of IoT technology, more and more industries will be equipped with sensors and monitors. The emergence of modern manufacturing changes the appearances of factories. IoT system helps managers to monitor and control the production process by collecting, tracking and transferring data. Data is powerful. Managers also can do business analysis based on marketing data. Information such as customer buying preference and market demanding could be collected and used for production estimation. [23]

Trend analysis based on current growth assumption could be used in manufacturing. The analysis strongly helps the cycle time reduction of manufacturing process and energy consumption. In this case, modern technology increases production efficiency as well as economic efficiency. [24]

Technology forecasting with technology radar

Companies often use technology forecasting to prioritize R&D activities, plan new product development and make strategic decisions on technology licensing, and formation of joint ventures. [25] One of the instruments enabling technology forecasting in a company is a technology radar. Technology radar serves to identify technologies, trends and shocks early on and to raise attention to the threats and opportunities of technological development as well as to stimulate innovation. [26]

Technology radars have successfully been implemented for the purpose of identifying, selecting, assessing and disseminating a company-wide technology intelligence. [27] [26] These Technology Radars follow a certain radar process which itself brings significant value for a company: [27]

See also

Related Research Articles

The Delphi method or Delphi technique is a structured communication technique or method, originally developed as a systematic, interactive forecasting method which relies on a panel of experts. The technique can also be adapted for use in face-to-face meetings, and is then called mini-Delphi. Delphi has been widely used for business forecasting and has certain advantages over another structured forecasting approach, prediction markets.

Scenario planning, scenario thinking, scenario analysis, scenario prediction and the scenario method all describe a strategic planning method that some organizations use to make flexible long-term plans. It is in large part an adaptation and generalization of classic methods used by military intelligence.

Morphological analysis or general morphological analysis is a method for exploring possible solutions to a multi-dimensional, non-quantified complex problem. It was developed by Swiss astronomer Fritz Zwicky.

<span class="mw-page-title-main">Futures studies</span> Study of postulating possible, probable, and preferable futures

Futures studies, futures research, futurism, or futurology is the systematic, interdisciplinary and holistic study of social/technological advancement, and other environmental trends; often for the purpose of exploring how people will live and work in the future. Predictive techniques, such as forecasting, can be applied, but contemporary futures studies scholars emphasize the importance of systematically exploring alternatives. In general, it can be considered as a branch of the social sciences and an extension to the field of history. Futures studies seeks to understand what is likely to continue and what could plausibly change. Part of the discipline thus seeks a systematic and pattern-based understanding of past and present, and to explore the possibility of future events and trends.

A technology roadmap is a flexible planning schedule to support strategic and long-range planning, by matching short-term and long-term goals with specific technology solutions. It is a plan that applies to a new product or process and may include using technology forecasting or technology scouting to identify suitable emerging technologies. It is a known technique to help manage the fuzzy front-end of innovation. It is also expected that roadmapping techniques may help companies to survive in turbulent environments and help them to plan in a more holistic way to include non-financial goals and drive towards a more sustainable development. Here roadmaps can be combined with other corporate foresight methods to facilitate systemic change.

Strategic foresight is a planning-oriented discipline related to futures studies. In a business context, a more action-oriented approach has become well known as corporate foresight.

Technological Forecasting and Social Change is a peer-reviewed academic journal published by Elsevier covering futures studies, technology assessment, and technology forecasting. Articles focus on methodology and actual practice, and have been published since 1969.

<span class="mw-page-title-main">Foresight (futures studies)</span> Term referring to various activities in futurology

In futurology, especially in Europe, the term foresight has become widely used to describe activities such as:

Technology Intelligence (TI) is an activity that enables companies to identify the technological opportunities and threats that could affect the future growth and survival of their business. It aims to capture and disseminate the technological information needed for strategic planning and decision making. As technology life cycles shorten and business become more globalized having effective TI capabilities is becoming increasingly important.

The following outline is provided as an overview of and topical guide to futures studies:

Technology scouting is an element of technology management in which

Corporate foresight has been conceptualised by strategic foresight practitioners and academics working and/or studying corporations as a set of practices, a set of capabilities and an ability of a firm. It enables firms to detect discontinuous change early, interpret its consequences for the firm, and inform future courses of action to ensure the long-term survival and success of the company.

Real-time Delphi (RTD) is an advanced form of the Delphi method. The advanced method "is a consultative process that uses computer technology" to increase efficiency of the Delphi process.

Futures & Foresight Science is an academic journal published by Wiley. The journal publishes articles dedicated to advancing methods that aid anticipating the future. The journal was established in 2019 by Professor George Wright, University of Strathclyde, Professor George Cairns, Queensland University of Technology and Professor Heiko von der Gracht, Steinbeis University Berlin.

<span class="mw-page-title-main">Paola Pisano</span> Italian academic and politician

Paola Pisano is an Italian academic and politician for the Five Star Movement. In September 2019, she was appointed Minister for Technological Innovation in the Conte II Cabinet.

The Trend Receiver Concept is a method for developing Customer Foresight and has been overall identified as an approach to develop foresight. At the core of the Trend Receiver Concept is the identification of suitable conversation partners, so called Trend Receivers, when developing foresight on the future demands and habits of consumers.

Customer foresight is a new field of applied research. It aims to understand future consumer preferences and wishes with regard to tomorrow's products and services. It does so by combining customer research and foresight research elements. Customer foresight can be conceived as an interaction with projected future markets through selected customers by understanding their wishes and attitudes, ideas and visions as well as their perception of signals and drivers of change. Even though the concept cannot predict the future, it enables companies to prepare for different future scenarios and thus improves strategy and decision-making processes.

Horizon scanning (HS) or horizon scan is a method from futures studies, sometimes regarded as a part of foresight. It is the early detection and assessment of emerging technologies or threats for mainly policy makers in a domain of choice. Such domains include agriculture, environmental studies, health care, biosecurity, and food safety.

Future-oriented technology analysis (FTA) is a collective term from futures studies for analyzing future technology and its consequences. It includes technology intelligence, technology forecasting, technology roadmapping, technology assessment, and technology foresight. Technology Futures Analysis or Technology Future Analysis (TFA) is a synonym.

<span class="mw-page-title-main">William Halal</span> American academic and engineer

William E. Halal is an American aerospace engineer, air force officer, academic, author, consultant, and speaker. He is Professor Emeritus of Management, Technology & Innovation at George Washington University as well as the Founder and President of TechCast, a web-based system that uses knowledge to forecast breakthroughs on emerging technologies and social trends.

References

  1. ↑ Quinn, James Brian (1967-03-01). "Technological Forecasting". Harvard Business Review. No. March 1967. ISSN   0017-8012 . Retrieved 2019-12-07.
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  3. 1 2 Council, National Research (2009-09-28). Persistent Forecasting of Disruptive Technologies. National Academies Press. ISBN   978-0-309-11660-2.
  4. ↑ OBrien, Peter W. (1978). "The Delphi Technique and Educational Planning". The Irish Journal of Education / Iris Eireannach an Oideachais. 12 (2): 69–93. ISSN   0021-1257. JSTOR   30076717.
  5. ↑ Adler, Michael; Ziglio, Erio (1996). Gazing Into the Oracle: The Delphi Method and Its Application to Social Policy and Public Health. Jessica Kingsley Publishers. ISBN   978-1-85302-104-6.
  6. ↑ Martino, Joseph P. (1999-08-01). "Thirty years of change and stability". Technological Forecasting and Social Change. 62 (1): 13–18. doi:10.1016/S0040-1625(99)00011-6. ISSN   0040-1625.
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  8. ↑ Lufkin, Bryan (6 July 2016). "Five transport promises that never quite changed the world". BBC. Archived from the original on 2016-07-09.
  9. ↑ Brock, David C. (2006). Understanding Moore's Law: Four Decades of Innovation. Chemical Heritage Foundation.
  10. ↑ Goddard, C. (December 1982). "Debunking the Learning Curve". IEEE Transactions on Components, Hybrids, and Manufacturing Technology. 5 (4): 328–335. doi:10.1109/tchmt.1982.1136009. ISSN   0148-6411.
  11. ↑ Jun, Seung-Pyo; Sung, Tae-Eung; Park, Hyun-Woo (2017). "Forecasting by analogy using the web search traffic". Technological Forecasting and Social Change. 115 (C): 37–51. doi: 10.1016/j.techfore.2016.09.014 .
  12. ↑ Chen, Yu-Heng; Chen, Chia-Yon; Lee, Shun-Chung (1 June 2011). "Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies". International Journal of Hydrogen Energy. 36 (12): 6957–6969. doi:10.1016/j.ijhydene.2011.03.063. ISSN   0360-3199 . Retrieved 21 March 2021.
  13. ↑ Sylak-Glassman, Emily J.; Williams, Sharon R.; Gupta, Nayanee (2016). "Appendix C". Current and Potential Use of Technology Forecasting Tools in the Federal Government: C-1–C-4.
  14. ↑ Read "Persistent Forecasting of Disruptive Technologies" at NAP.edu. 2009. doi:10.17226/12557. ISBN   978-0-309-11660-2.
  15. ↑ Itami, Hiroyuki; Numagami, Tsuyoshi (1992). "Dynamic interaction between strategy and technology". Strategic Management Journal. 13 (S2): 119–135. doi:10.1002/smj.4250130909. ISSN   0143-2095.
  16. ↑ Kline, Stephen; Dyer-Witheford, Nick; Peuter, Greig De (2003-05-26). Digital Play. McGill-Queen's University Press. doi:10.1515/9780773571068. ISBN   978-0-7735-7106-8.
  17. ↑ Song, Michael; Droge, Cornelia; Hanvanich, Sangphet; Calantone, Roger (2005). "Marketing and technology resource complementarity: an analysis of their interaction effect in two environmental contexts". Strategic Management Journal. 26 (3): 259–276. doi:10.1002/smj.450. ISSN   0143-2095.
  18. ↑ Orbach, Yair (2022). Forecasting the Dynamics of Market and Technology. Ariel University Press. ISBN   978-965-7632-40-6.
  19. ↑ Porter, Alan L.; Cunningham, Scott W.; Banks, Jerry; Roper, A. Thomas; Mason, Thomas W.; Rossini, Frederick A. (2011-07-12). Forecasting and Management of Technology. John Wiley & Sons. ISBN   978-0-470-44090-2.
  20. ↑ Pappenberger, F.; Cloke, H. L.; Persson, A.; Demeritt, D. (2011). "HESS Opinions "On forecast (in)consistency in a hydro-meteorological chain: curse or blessing?"". Hydrology and Earth System Sciences Discussions. 8 (1): 1225–1245. Bibcode:2011HESSD...8.1225P. doi: 10.5194/hessd-8-1225-2011 . ISSN   1812-2116.
  21. ↑ Armstrong, J. (2001-06-17). "Combining forecasts". Marketing Papers (34).
  22. ↑ Triulzi, Giorgio; Alstott, Jeff; Magee, Christopher L. (2020-09-01). "Estimating technology performance improvement rates by mining patent data". Technological Forecasting and Social Change. 158: 120100. doi: 10.1016/j.techfore.2020.120100 . ISSN   0040-1625.
  23. ↑ Zhong, Ray Y.; Xu, Xun; Klotz, Eberhard; Newman, Stephen T. (2017-10-01). "Intelligent Manufacturing in the Context of Industry 4.0: A Review". Engineering. 3 (5): 616–630. doi: 10.1016/J.ENG.2017.05.015 . ISSN   2095-8099.
  24. ↑ Cascini, Gaetano; Becattini, Niccolò; Kaikov, Igor; Koziolek, Sebastian; Kucharavy, Dmitry; Nikulin, Christopher; Petrali, Pierluigi; Slupinsky, Mateusz; Rabie, Mahmoud; Balachandar; Ramadurai (2015-01-01). "FORMAT – Building an Original Methodology for Technology Forecasting through Researchers Exchanges between Industry and Academia". Procedia Engineering. TRIZ and Knowledge-Based Innovation in Science and Industry. 131: 1084–1093. doi: 10.1016/j.proeng.2015.12.426 . ISSN   1877-7058.
  25. ↑ Firat, Ayse Kaya; Woon, Wei Lee; Stuart, Madnick (2008). "Technological forecasting - a review" (PDF). Composite Information Systems Laboratory (CISL), Massachusetts Institute of Technology. S2CID   14340682. Archived from the original (PDF) on 2019-02-28. Retrieved 24 February 2020.
  26. 1 2 Rohrbeck, René; Heuer, Jörg; Arnold, Heinrich (2006). "The Technology Radar - an Instrument of Technology Intelligence and Innovation Strategy". 2006 IEEE International Conference on Management of Innovation and Technology. Vol. 2. pp. 978–983. CiteSeerX   10.1.1.527.7418 . doi:10.1109/ICMIT.2006.262368. ISBN   1-4244-0147-X. S2CID   27061994.
  27. 1 2 Boe-Lillegraven, Siri; Monterde, Stephan (2015). "Exploring the cognitive value of technology foresight: The case of the Cisco Technology Radar". Technological Forecasting and Social Change. 101: 62–82. doi: 10.1016/j.techfore.2014.07.014 .
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