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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]

History

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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 alternate reality games, online forecasting communities and obsolescence forecasting.[3]

Important aspects

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"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

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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

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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

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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

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Forecasting institutes

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Scientific Journals

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Uses in manufacturing

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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

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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]

  • Identification: employees acting as technology scouts from all over the world submit novel technologies to the platform.
  • Selection: based on the technology, its potential impact and novelty, a radar team revises submitted technologies and selects the most valid ones.
  • Assessment: selected technologies are then assessed on the basis of market opportunity and implementation risk.
  • Dissemination: radar displays assessed technologies according to maturity, position in the value chain, and relevance.[28]

See also

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References

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  1. ^ Quinn, James Brian (1967-03-01). "Technological Forecasting". Harvard Business Review. No. March 1967. ISSN 0017-8012. Retrieved 2019-12-07.
  2. ^ a b Abdou, E.; Mahmoud, S. (1977-01-01). "The role of technological forecasting in planning future developments". IFAC Proceedings Volumes. IFAC Conference on Systems Approach for Development, Cairo, Egypt, 26–29 November. 10 (14): 65–68. doi:10.1016/S1474-6670(17)66433-4. ISSN 1474-6670.
  3. ^ a b 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.
  7. ^ Zhu, Donghua; Porter, Alan L. (2002-06-01). "Automated extraction and visualization of information for technological intelligence and forecasting". Technological Forecasting and Social Change. TF Highlights from ISF 2001. 69 (5): 495–506. doi:10.1016/S0040-1625(01)00157-3. ISSN 0040-1625. S2CID 16720975.
  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. ^ a b 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. ^ a b 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.
  28. ^ "Technology Radar | An opinionated guide to today's technology landscape". Thoughtworks. Retrieved 2024-06-05.
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