We work with lots of leaders in scale ups, many of whom have challenges with measuring the performance of their R&D teams.
Traditional performance measurement activities (a KPI or OKR approach) apply clearly defined measures to evaluate performance outcomes. They explain acceptable performance, and the data provides timely information on performance. They work mostly in a binary / black & white way.
An R&D environment, though, poses specific and different challenges meaning that traditional approaches of KPI’s and OKR’s are not always the best ways of measuring the performance of R&D teams or individuals.
Firstly, in an R&D environment, performance is difficult to measure and the outcome of R&D activities often cannot be quantified in advance. Some benefits may be clear in terms of finance or income, while others likely are not.
Secondly, the timeliness of the data is a concern because of the long-time span between starting an R&D effort and realisation of those benefits.
Thirdly and probably most obviously, in R&D there are many unknowns, which cannot be measured, as well as generally higher risks of failure.
So how could we do it in an R&D environment?
It’s important first to define what we are measuring. There are lots of measures that we could apply at an organisational level – Net Present Value, ROI, Budget Variance, Risk etc etc. And whilst there are lots of discussions as to the validity of these measures especially in agile start-ups and scale ups this is not our focus.
Our focus is aimed more at the individuals themselves or the R&D team as a whole. So how can we measure their performance. The first place to start would be to identify what is important to the organisation or the leaders themselves. What do you want to keep an eye on? What do you want to measure that’s specific to the R&D team? Here, we would probably look at three different areas:
- Outcomes e.g. customer assessments, condition of the technical base, maintenance back log, percent that accomplish the objectives of the organisation (how efficient are we?)
- Impact e.g. progress towards goals, new capabilities enabled, organisational needs addressed, system improvements, capability gaps covered
- Peer reviews e.g. scientific quality, scientific opportunities in areas of user importance, balance between revolutionary and evolutionary research
Its important not to under value the contribution of peer reviews to performance management. Peer reviews are an unbiased review by knowledgeable experts. The metrics can reveal the most about the quality and appropriateness of R&D individuals. Peer reviews result in improvements to research methods and procedures.