Data driven TRL Transition Predictions for Early Technology Development in Defence

Ma, Jungmok

doi:10.14429/dsj.71.16771

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…When regression analysis is used, it is straightforward to obtain estimates of the cost overruns and the time to complete R&D projects along with confidence intervals for the estimates. Ma 33 estimated cost and schedule by identifying causal variables using the Technology Cost and Schedule Estimating (TCASE) tool as another form of linear regression. Using regression analysis of historical data to provide a basis for project forecasting provides a means to counteract biases obtained when using direct assessments by subject matter experts of times to completion and uncertainty ranges for project tasks.…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling to relate technology readiness to schedule

Kenley

Subramanian

Adams

2023

Systems Engineering

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Projects from U.S. National Aeronautics and Space Administration and Department of Energy technology development activities provide historical data on project completion timelines versus technology readiness levels that were assessed during the life cycle of a program. A statistical analysis was performed to develop a method to forecast future project completion timelines based on technical maturity assessments. The goodness‐of‐fit of the model used for forecasting also was evaluated, and the null hypothesis that the data follows the probability distribution used for the forecasting model could not be rejected at a Type I error level of 0.05. Several potential extensions to the model using product forecasting methods, semi‐Markov processes, and Bayes nets are presented. The limitations of the statistical modeling and of the potential extensions are also discussed.

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Section: Introductionmentioning

confidence: 99%

Statistical modeling to relate technology readiness to schedule

Kenley

Subramanian

Adams

2023

Systems Engineering

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Help, I need somebody! Business and technology advisors in emerging science-based ventures at American universities

Denoo,

Van Boxstael,

Belz

2024

J Technol Transf

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Early-stage science-based ventures (SBVs) require a wide range of intellectual resources and practical know-how to successfully commercialize their technologies. Often SBV founders actively gain this knowledge through advisory relationships providing business and technology guidance. We explore the effects of both business and technology advisors in combination with the founder’s entrepreneurial and technology experience. We measure early-stage success in an SBV using application readiness, a novel concept that encompasses progress in both technology discovery and validation as well as market identification and application. Using hand-collected longitudinal data from 112 emerging science-based ventures associated with American universities, we find that business advisors have a positive impact on application readiness, while technology advisors delay it; and these effects are moderated by the founder’s experience. Remarkably, a small number of advisors can have the same impact as decades of experience. Our article unpacks underexplored mechanisms through which advisors—an often-used policy tool supporting entrepreneurship—are implemented in emerging science-based ventures and makes academic contributions to the literatures on technology commercialization, advisors and human capital.

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