Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-26T16:10:21.175Z Has data issue: false hasContentIssue false
  • English
  • Français

IDENTIFYING GAPS IN AUTOMATING THE ASSESSMENT OF TECHNOLOGY READINESS LEVELS

Part of: Design Organisation and Management

Published online by Cambridge University Press:  11 June 2020

S. Faidi  and
A. Olechowski
S. Faidi*
Affiliation:
University of Toronto, Canada
A. Olechowski
Affiliation:
University of Toronto, Canada
*
*safa.faidi@mail.utoronto.ca

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Crucial in the design process, Technology Readiness Levels are a common form of technology maturity assessment. Studies suggest that the TRL scale can be subjective and biased. Automating the assessment can reduce human bias. This paper highlights important challenges of automation by presenting data collected on 15 technologies from the nanotechnology sector. Our findings show that, contrary to claims from the literature, patent data exists for low maturity technologies and may be useful for automation. We also found that there exists unexpected trends in data publications at TRL 2, 3 and 4.

Keywords

technology developmentbig data analysisproduct development
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 551 - 558
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Britt, B.L. et al. (2008), “Document Classification Techniques for Automated Technology Readiness Level Analysis”, Journal of the American Society for Information Science and Technology, Vol. 59 No. 4, pp. 675–680. https://doi.org/10.1002/asiCrossRefGoogle Scholar
Engel, D.W. et al. (2012), “Development of Technology Readiness Level (TRL) Metrics and Risk Measures”.10.2172/1067968CrossRefGoogle Scholar
Fast-Berglunda, Å. et al. (2014), “Using the TRL-methodology to design supporting ICT-tools for production operators”, Elsevier, pp. 726–731. https://doi.org/10.1016/j.procir.2014.02.039Google Scholar
Lokuhitige, S. and Brown, S. (2017), “Forecasting Maturity of IoT Technologies in Top 5 Countries Using Bibliometrics and Patent Analysis”, In Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017, pp. 338–341. https://doi.org/10.1109/CyberC.2017.35CrossRefGoogle Scholar
Lezama-Nicolás, R. et al. (2018), “A bibliometric method for assessing technological maturity: the case of additive manufacturing”, Scientometrics. Springer International Publishing, Vol. 117 No. 3, pp. 1425–1452. https://doi.org/10.1007/s11192-018-2941-1Google ScholarPubMed
NASA (2016), NASA System Engineering Handbook SP-2016-6105 Rev2. Rev2 edn. Edited by Hoffpauir, D.. Available at: http://hdl.handle.net/2060/20170001761Google Scholar
NASA Technology Roadmaps TA 10: Nanotechnology (2015), Available at: https://www.nasa.gov/sites/default/files/atoms/files/2015_nasa_technology_roadmaps_ta_10_nanotechnology_final.pdfGoogle Scholar
Olechowski, A. (2017), Essays on decision-making in complex engineering systems development. https://doi.org/10.1007/s11661-014-2485-9CrossRefGoogle Scholar
Sarfaraz, M., Sauser, B.J. and Bauer, E.W. (2012), “Using System Architecture Maturity Artifacts to Improve Technology Maturity Assessment”, Procedia Computer Science, pp. 165–170. https://doi.org/10.1016/j.procs.2012.01.034CrossRefGoogle Scholar
Watts, R.J. and Porter, A.L. (1997), “Innovation Forecasting”, Technological Forecasting and Social Change, Vol. 56 No. 1, pp. 25–47. https://doi.org/10.1016/S0040-1625(97)00050-4CrossRefGoogle Scholar