Understanding which innovation projects are most likely to succeed is a long-standing challenge for governments, funders, and R&D\u2011intensive organisations. Despite the widespread use of Technology Readiness Levels (TRLs) as a benchmark for maturity, few organisations have a reliable, data\u2011driven way to predict how projects will progress through these levels or what proportion of a portfolio will ultimately reach critical milestones.<\/p>\n\n\n\n
Our recent work tries to close this gap by developing a structured, statistical methodology to estimate the likelihood of TRL progression at both the project<\/strong> and portfolio<\/strong> levels. The approach, tested using large datasets from energy innovation programmes, offers a practical and transferable approach that other organisations can readily adopt to move from descriptive reporting towards forward-looking decision making.<\/p>\n\n\n\n <\/p>\n\n\n\n The methodology combines three core elements:<\/p>\n\n\n\n By contrast, commonly assumed drivers such as funding amounts showed no statistically significant effect once maturity, expectations and timing were accounted for.<\/p>\n\n\n\n \u201cWhat is the probability that at least 20% of our portfolio will reach TRL 7+?\u201d<\/em> <\/p>\n<\/blockquote>\n\n\n\n Although developed in a public innovation funding context, the approach is highly transferable. Any organisation managing a pipeline of research, product development or complex technological innovation can benefit from moving beyond descriptive TRL reporting towards probabilistic decision-support.<\/p>\n\n\n\n The framework supports more strategic portfolio design by identifying which combinations of project characteristics \u2013 such as starting TRL, expected progress and duration \u2013 are associated with higher success probabilities. It also allows organisations to stress-test assumptions by constructing synthetic portfolios (for example, exploring outcomes if all projects enter at a higher TRL), thereby improving transparency and risk awareness in investment decisions.<\/p>\n\n\n\n The predictive power of the model is strong but can be improved further. In particular, gathering data on the complexity of the integration of the technology developed under a specific project, as well as information on team capabilities, may improve its predictive power.<\/p>\n\n\n\n Ultimately, this work demonstrates that TRL progression can be predicted, and that a thoughtful combination of evidence review, statistical modelling and probability theory can help funders and other organisations make smarter, more transparent decisions about their innovation investments.<\/p>\n\n\n\n For more information contact: Cristina Rosemberg<\/a> and Iakov Frizis.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Understanding which innovation projects are most likely to succeed is […]<\/p>\n","protected":false},"author":18,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[2],"tags":[],"solution":[],"sector":[118],"class_list":["post-65297","post","type-post","status-publish","format-standard","hentry","category-geen-onderdeel-van-een-categorie","sector-research-innovation-nl"],"acf":[],"yoast_head":"\nA robust, evidence\u2011based approach<\/strong><\/h2>\n\n\n\n
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\u201cHow many projects do we need in the pipeline to ensure a minimum number reach TRL 8 or TRL 9?\u201d<\/em><\/p>\n\n\n\nWhy this matters for other funders and organisations<\/strong><\/h2>\n\n\n\n
Looking ahead<\/strong><\/h2>\n\n\n\n