A large portion of companies’ R&D policy is geared towards increasing collaboration between employees within the R&D organisation. Objective evaluation of the effects of such policy decisions has, however, proved difficult using traditional performance metrics. Kenedict enables organisations to objectively evaluate the effects of R&D policy and craft future policy measures based on this.
Many organisations have been consolidating their R&D activities in centralised R&D centres. The aim of such policy decisions is often to better leverage internally available knowledge by decreasing the physical distance between knowledge clusters. Mapping subsequent iterations of knowledge networks over time provides the valuable opportunity to evaluate the effects of such R&D policy initiatives on actual changes in collaborative behavior.
The effects of R&D centralisation programs, employee incentive programs aimed at increasing collaboration, or investments in setting up expert groups and Communities of Practice can be objectively evaluated in this way. A wide variety of metrics and visualisations support these evaluations, thus providing a solid basis for assessments of the actual effectiveness of policy measures.
Next to evaluating past policy decisions, network analytics also provides the opportunity to construct predictive models of the effects of future policy measures. Scenario analysis of various policy measures can be significantly improved in this way, paving the way for R&D policy and strategy measures with a clear impact on improving overall collaboration and innovative output.
Visualizing network evolution to assess policy effects
