Technology forecasting: Difference between revisions

{{Short description|Attempting to predictPredicting the future characteristics of useful technological machines, procedures or techniquestechnology}}

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.<ref name=":1">{{Cite book|url=https://www.nap.edu/catalog/12557/persistent-forecasting-of-disruptive-technologies|title=Persistent Forecasting of Disruptive Technologies|last=Council|first=National Research|date=2009-09-28|publisher=National Academies Press |isbn=978-0-309-11660-2|language=en}}</ref> In the 1950s and 1960s, [[RAND Corporation]] developed the Delphi Technique and were widely accepted and used to make smart evaluation for the future.<ref>{{Cite journal|last=OBrien|first=Peter W.|date=1978|title=The Delphi Technique and Educational Planning|journal=The Irish Journal of Education / Iris Eireannach an Oideachais|volume=12|issue=2|pages=69–93|issn=0021-1257|jstor=30076717}}</ref> 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.<ref>{{Cite book|url=https://books.google.com/books?id=jo1Z1JZIrKIC&q=The+Delphi+Method+and+Its+Application+to+Social+Policy+and+Public+Health.&pg=PP7|title=Gazing Into the Oracle: The Delphi Method and Its Application to Social Policy and Public Health|last1=Adler|first1=Michael|last2=Ziglio|first2=Erio|date=1996|publisher=Jessica Kingsley Publishers|isbn=978-1-85302-104-6|language=en}}</ref> 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.<ref>{{Cite journal|last=Martino|first=Joseph P.|date=1999-08-01|title=Thirty years of change and stability|journal=Technological Forecasting and Social Change|volume=62|issue=1|pages=13–18|doi=10.1016/S0040-1625(99)00011-6|issn=0040-1625}}</ref> 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.<ref>{{Cite journal|last1=Zhu|first1=Donghua|last2=Porter|first2=Alan L.|date=2002-06-01|title=Automated extraction and visualization of information for technological intelligence and forecasting|journal=Technological Forecasting and Social Change|series=TF Highlights from ISF 2001|volume=69|issue=5|pages=495–506|doi=10.1016/S0040-1625(01)00157-3|s2cid=16720975 |issn=0040-1625}}</ref> 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.<ref name=":1" />

"'''I think we have a cultural affinity for technology that reflects optimism, but we all make poor forecasts.'''" &mdash; ''Jim Moore, director of the Transportation Engineering Program at the University of Southern California''<ref>{{Cite web|url=http://www.bbc.com/autos/story/20160706-5-transportation-technologies-we-wish-were-more-popular |url-status=dead |archiveurl=https://web.archive.org/web/20160709013701/http://www.bbc.com/autos/story/20160706-5-transportation-technologies-we-wish-were-more-popular |archivedate=2016-07-09 |title=Five transport promises that never quite changed the world |first=Bryan Home|datelast=Lufkin |date=6 11July June2016 2021|work=BBC}}</ref>

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 beenbe used in cases of restricted funds.<ref name=":0" />

Commonly adopted methods and tools of technology forecasting include the [[Moore's law]],<ref>{{Cite book |last=Brock |first=David C. |title=Understanding Moore's Law: Four Decades of Innovation |publisher=Chemical Heritage Foundation |year=2006}}</ref> [[Write's law]] and Goddard law,<ref>{{Cite journal |last=Goddard |first=C. |date=December 1982 |title=Debunking the Learning Curve |url=http://dx.doi.org/10.1109/tchmt.1982.1136009 |journal=IEEE Transactions on Components, Hybrids, and Manufacturing Technology |volume=5 |issue=4 |pages=328–335 |doi=10.1109/tchmt.1982.1136009 |issn=0148-6411}}</ref> which generate quantitative assessments for technology progress, the [[Delphi method]], [[forecast by analogy]], [[growth curve (statistics)|growth curve]]s, [[extrapolation]] and [[horizon scanning]].<ref>{{cite journal |last1=Jun |first1=Seung-Pyo |last2=Sung |first2=Tae-Eung |last3=Park |first3=Hyun-Woo |title=Forecasting by analogy using the web search traffic |journal=Technological Forecasting and Social Change |date=2017 |volume=115 |issue=C |pages=37–51 |doi=10.1016/j.techfore.2016.09.014 |url=https://ideas.repec.org/a/eee/tefoso/v115y2017icp37-51.html |language=en|doi-access=free }}</ref><ref>{{cite journal |last1=Chen |first1=Yu-Heng |last2=Chen |first2=Chia-Yon |last3=Lee |first3=Shun-Chung |title=Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies |journal=International Journal of Hydrogen Energy |date=1 June 2011 |volume=36 |issue=12 |pages=6957–6969 |doi=10.1016/j.ijhydene.2011.03.063 |bibcode=2011IJHE...36.6957C |url=https://www.sciencedirect.com/science/article/abs/pii/S0360319911006537 |access-date=21 March 2021 |language=en |issn=0360-3199}}</ref><ref>{{Cite journal|url=https://www.jstor.org/stable/resrep22784.10|title = Appendix C|last1 = Sylak-Glassman|first1 = Emily J.|last2 = Williams|first2 = Sharon R.|last3 = Gupta|first3 = Nayanee|journal = Current and Potential Use of Technology Forecasting Tools in the Federal Government|year = 2016|pages = C-1–C-4}}</ref> Normative methods of technology forecasting—like the relevance trees, morphological models, and [[mission flow diagram]]s—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.<ref>{{Cite book|url=https://www.nap.edu/read/12557/chapter/4|title=Read "Persistent Forecasting of Disruptive Technologies" at NAP.edu|year=2009|doi=10.17226/12557|isbn=978-0-309-11660-2|language=en}}</ref>

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.<ref>{{Cite book|url=https://books.google.com/books?id=JOPnJQFfT-0C&q=Forecasting+and+management+of+technology+%2F+Alan+Thomas+Roper+.+.+.+%5Bet+al.%5D.+%E2%80%93+2nd+ed.+p.+cm.+Includes+index.+ISBN+978-0-470-44090-2+%28hardback%29%3B+978-0-470-95161-3+%28ebk%29%3B+978-0-470-95178-1+%28ebk%29%3B+978-1-118-04798-9+%28ebk%29%3B+978-1-118-04816-0+%28ebk%29%3B+978-1-118-04818-4+%28ebk%29%3B+978-1-118-04821-4+%28ebk%29&pg=PR4|title=Forecasting and Management of Technology|last1=Porter|first1=Alan L.|last2=Cunningham|first2=Scott W.|last3=Banks|first3=Jerry|last4=Roper|first4=A. Thomas|last5=Mason|first5=Thomas W.|last6=Rossini|first6=Frederick A.|date=2011-07-12|publisher=John Wiley & Sons|isbn=978-0-470-44090-2|language=en}}</ref> 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.<ref>{{Cite journal|last1=Pappenberger|first1=F.|last2=Cloke|first2=H. L.|last3=Persson|first3=A.|last4=Demeritt|first4=D.|title=HESS Opinions &quot;On forecast (in)consistency in a hydro-meteorological chain: curse or blessing?&quot;|url=https://www.academia.edu/2599590|journal=Hydrology and Earth System Sciences Discussions|language=en|volume=8|issue=1|pages=1225–1245|issn=1812-2116|doi=10.5194/hessd-8-1225-2011|bibcode=2011HESSD...8.1225P|year=2011|doi-access=free}}</ref> 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.<ref>{{Cite journal|last=Armstrong|first=J.|date=2001-06-17|title=Combining forecasts|url=https://repository.upenn.edu/marketing_papers/34|journal=Marketing Papers|issue=34 }}</ref>

Technology forecasting heavily relies on data and data makes contributions to manufacturing and [[Industry 4.0]]. [[Internet of things|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.<ref>{{Cite journal|last1=Zhong|first1=Ray Y.|last2=Xu|first2=Xun|last3=Klotz|first3=Eberhard|last4=Newman|first4=Stephen T.|date=2017-10-01|title=Intelligent Manufacturing in the Context of Industry 4.0: A Review|journal=Engineering|volume=3|issue=5|pages=616–630|doi=10.1016/J.ENG.2017.05.015|issn=2095-8099|doi-access=free|bibcode=2017Engin...3..616Z }}</ref>

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.<ref>{{cite journal |last1=Firat |first1=Ayse Kaya |last2=Woon |first2=Wei Lee |last3=Stuart |first3=Madnick |title=Technological forecasting - a review |journal=Composite Information Systems Laboratory (CISL), Massachusetts Institute of Technology. |date=2008 |s2cid=14340682 |url=http://pdfs.semanticscholar.org/8ea2/bd1792cf794506966ecaacb2e3315de1fc5a.pdf |archive-url=https://web.archive.org/web/20190228080258/http://pdfs.semanticscholar.org/8ea2/bd1792cf794506966ecaacb2e3315de1fc5a.pdf |url-status=dead |archive-date=2019-02-28 |access-date=24 February 2020}}</ref> 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.<ref name="he technology radar-an instrument o">{{cite journalbook |last1=Rohrbeck |first1=René |last2=Heuer |first2=Jörg |last3=Arnold |first3=Heinrich |title=he2006 technologyIEEE radar-anInternational instrumentConference ofon technologyManagement intelligenceof Innovation and innovation strategyTechnology |journalchapter=IEEEThe InternationalTechnology ConferenceRadar on- Managementan Instrument of InnovationTechnology Intelligence and TechnologyInnovation Strategy |date=2006 |volume=2 |pages=978–983 |doi=10.1109/ICMIT.2006.262368 |isbn=1-4244-0147-X |citeseerx=10.1.1.527.7418 |s2cid=27061994 }}</ref>

* Dissemination: radar displays assessed technologies according to maturity, position in the value chain, and relevance.<ref>{{Cite web |title=Technology Radar {{!}} An opinionated guide to today's technology landscape |url=https://www.thoughtworks.com/radar |access-date=2024-06-05 |website=Thoughtworks |language=en}}</ref>

*[https://www.unido.org/foresight/registration/dokums_raw/volume1_unido_tf_manual.pdf] {{Webarchive|url=https://web.archive.org/web/20130602145925/https://www.unido.org/foresight/registration/dokums_raw/volume1_unido_tf_manual.pdf |date=2013-06-02 }} Unido Technology Foresight Manual.