What metrics can be used to measure the success of a research commercialization program?

Successful Commercialization = Product on-market (impact), royalties and perhaps sponsored research back to the institution that generates new IP and commercialization opportunities.

It is important to consider the First Time Right/ First Pass Yield metric in designing and planning the program. The number of cycles (or tries) required to achieve the desired outcome is a useful measurement when comprehensively and realistically assessed. Of course, it is a continuum- the number of cycles will be higher at the exploratory stage and should decrease as process knowledge is gained. Unplanned events must be investigated, corrected, and applied to other potential areas. That is costly in resources, time, materials, and capital. A high FPY establishes confidence and credibility.

It is complicated but simply put there are short term and long term outcomes of research commercialization for example, prototypes, sponsored research, IP , publications, licenses and royalties could be outcomes in a short term. Products-in market, spin-offs and jobs created are rather long term outcomes. Also, the economic impact of workforce trained during product development and the value of lives saved or “improved“ by the products in market should be valid outcomes.

In my opinion, evaluating research commercialization success necessitates assessing multiple metrics. Key metrics are: --Revenue Generation --Patent Activity --Spin-offs --Market Adoption --Partnerships --IP Portfolio Growth --Job Creation --Economic Impact --Research Impact, and --Customer Satisfaction These encompass financial impact, innovation, practical application, industry engagement, intellectual property, economic influence, academic reach, and stakeholder feedback, collectively determining program effectiveness. Hope it helped.

When measuring the intellectual property generated, it is most important to adopt a broad definition of IP and not limit it to registered forms of IP. There is data to suggest that technology protected by a combination of patents and trade secrets is easier to commercialise when compared to that same technology only protected by patents.

I worked as a research scientist for most of my career. I was often frustrated by the need to generate measurable metrics. To me, this was an effort that was top down, to justify spending. I get it. The problem for me was the methods used to quantify the "invention goes here" problem. I never found a way to quantify the time for "nothing to something" invention. The rest of the development process was well characterized and lent itself to accurate project plans - the invention problem remained difficult. I have a bunch of patents. When asked how much money I made off the patents. I always answer: "I got to keep my job." Invention is tough.

These metrics are crucial for demonstrating the value and relevance of the program. However, they can be difficult to measure and attribute due to external factors. Cambridge University, for example, has had a significant impact on the local economy through its research commercialization program, creating thousands of jobs and contributing to economic growth. In addition to revenue and jobs, the measurement of social and environmental impact, as well as political influence, could be considered.

Concept to market timeline. It can be broken down to development optimization, global market analysis and commercial planning metrics.

These metrics are useful for improving program quality and effectiveness. However, they are subjective and can vary based on individual perceptions. Stanford University, for example, uses satisfaction surveys to collect feedback from participants in its research commercialization program. In addition to satisfaction surveys, one might consider collecting testimonials and reviews, as well as measuring student retention and success.

In my experience, initial opportunities for licensing are ignored in favor of the possibility for a spin-off. In some circumstances, potential commercial partnerships are deprioritized as a result. Commercial partnerships can become productive relationships in research. So balance in terms of the types of commercialization avenues is important. Metrics would help guide commercial outreach modalities and relationships.

These metrics are important for optimizing program performance and impact. However, they require constant and flexible adaptation of the program to the changing internal and external environment. The Massachusetts Institute of Technology (MIT), for example, has successfully achieved balance and integration in its research commercialization program, aligning the program with its broader vision and mission. In addition to the distribution of resources and activities, consideration could be given to measuring diversity and inclusion, as well as alignment with the Sustainable Development Goals.

Ka-ching! Many metrics I've seen are SMART, but what are we really improving? Just the bottom line? We must consider future generations, including intangibles like ethics, B-Corps, ESGs, equity, housing rights, and more. How do we measure IP or concepts like open space? While financial growth is important, we must rethink our focus when only a few benefit. R&D projects often fail due to a lack of vision and funding for commercialization. Some countries support research and SMEs but leave innovators struggling to bring products to market, leading to technology leaving the country. It's crucial to invest throughout to ensure our ideas don't go to waste.

In this section, it is important to consider other factors that can influence the success of a research commercialization program. For example, the culture of the organization, the support of senior management, and the availability of resources can have a significant impact on the success of the program. One such example is the Hebrew University of Jerusalem, whose inventions generate more than $2 billion in annual sales, demonstrating the potential for success of a research commercialization program.