Plan, measure, and control R&D productivity directly. Eliminate R&D waste and jump to top performance.
R&D productivity is one of the strongest control levers in the hand of top management to reach profitable growth. However, it is one of the productivities most difficult to measure, because it varies strongly by changes of project portfolio and project targets. Bad experiences in the past let top management believe that R&D productivity targets are risky. However, if direct measurement is reached, productivity potentials of 20% to 60%, in some cases above, can be released and used for additional profitable growth.
Often R&D productivity is measured piecemeal. In spite of improvements of key performance indicators (KPI) like engineering costs as % of turnover, gut feeling suggests, feeded by various impressions, that R&D did not really become more productive.
R&D productivity potentials are so manifaceted as R&D is complex. Hence they are invisible or immeasurable for controlling. They may be deeply buried within targets, technology, processes, project management, and within ambiguity and uncertainty at the beginning of R&D projects.
Long term planning as well as engineering budgets are often built up unsystematic with far reaching consequences. High double-digit productivity potentials are intransparent. Project results are not dependable again and again with the effect of high margin losses. Rerouting of engineering capacity is made difficult.
In the past R&D often has implemented multiple actions to improve productivity, e.g. tools and best practises. In the point of view of controlling no effect is visible, because cause and effect have not been measured directly; R&D „has got another toy“.
If management succeds in directly measuring R&D productivity, planning and controlling will be enabled by dependable KPI. Requirements will be standardised, where useful. Number of engineering cycles will be reduced. Product engineering process (PEP) will loose waste by selective application of predevelopment, system engineering, simulation, lean or agile techniques. The grown test concept will be basically evaluated. Capacity planning and control will be implemented where useful. Engineering tasks will be selectively reallocated by make-or-buy decisions, low-cost approaches or co-engineering with partners. Complex matrix organisations will be simplified. R&D will be aligned to businesses.
When R&D productivity increases, top management is able to plan parts of R&D capacity for increased profitable growth. Key to this is Statistical Engineering. It is proven in identifying, measuring directly and dependably quantifying R&D productivity potentials. It provides the potentials for planning and controlling.
So top performance of R&D productivity is reached. Does R&D deserve applause for it?