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Technology Readiness Levels: Determining Maturity

Apr 12, 2016

“The science of today is the technology of tomorrow.” – Edward Teller

Technology readiness levels (TRLs) were originally implemented in the 1970’s by NASA to compare apples, oranges, and suitcases. Actually, it was to compare disparate technology developments in terms of their technological maturity. “Apples and oranges” might translate to development of two distinct experiments to fly on a space shuttle, one on the effects of microgravity on fruit fly breeding and another on free-form fabrication of parts by electron-beam deposition.

However, TRLs had to measure the maturity of a new directional thruster or even a main engine for the shuttle as well. TRLs introduced a metric for maturation independent of all other considerations, such as the type or complexity of the technology under examination, enabling the widest conceivable range of technology developments to be assessed usefully on a single scale.

Figure 1: NASA TRL Scale


Why would an organization want a comparable assessment of the technological maturity of projects in its own (or a supplier’s) R&D portfolio?

Let’s assume that managers already know all there is to know about the technology maturity of their projects. TRLs enable efficient communication of that knowledge, both for decision making purposes and to trumpet accomplishments.

One example of efficient communications relevant to decision making is the U.S. Government Accountablity Office’s frequent recommendation since 2006 that federal agencies not incorporate technology developments into systems development before they have achieved TRL 6 or 7. It is at that point the developers are expected to have proven their concept and approach under conditions appropriate to the final application.

TRLs enable analysis of the portfolio in terms that include technological maturity. Want to know what your R&D pipeline looks like? TRLs are a good measure. They can be extended, so that projects may be assessed starting, current, and anticipated ending TRLs. Similarly, they can be combined with other measures, such as the age or remaining lifetime of projects, to show how far projects have come—or need to go—over their term.

What TRLS do not provide is as important as what they do provide.

TRLs Do:

  • Measure the location of a technology under development against a sequence of maturity milestones.
  • Provide a maturity metric that may be combined with other metrics for analytical purposes.

TRLs Do Not:

  • Measure the value (e.g., technical merit or market need) of a technology development effort.
  • Indicate risk per se (though the testing through which technologies pass as they ascend in TRL should be constructed to progressively reduce the technical risk).

It may be noted that other metrics exist to measure these aspects that TRLs do not measure, and TRLs may be combined with them in analysis.

Since its introduction by NASA, the use of TRLs has spread to other federal agencies, including the departments of Defense and Energy, and to the private sector. In the process TRLs have expanded from their initial association with aerospace technology into many other fields of technology development, sometimes encountering difficulties in translation or implementation. Organizations for which integration of complex components is a major facet of large development efforts, for example, have reported difficulties with TRLs not being sufficiently focused on integration issues[i].

One response to these difficulties has been to develop extensions to the TRL concept, including integration readiness levels and system readiness levels. Other practitioners believe that “integration is an inherent sub-attribute of technology readiness and so the TRL levels themselves should reflect integration readiness.”

In the end, the choice may boil down to what works best in each implementation.

Written by: Andrea Ware

*Olechowski, A., Eppinger, S.D., and Joglekar, N. “Technology Readiness Levels at 40: A Study of State-of-the-Art Use, Challenges, and Opportunities.” MIT Sloan School Working Paper 5127-15. April 2015.

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