Decision-making based on network analysis should not be a one-off exercise. Kenedict strongly believes that network analysis should be made part of day-to-day decision-making in R&D. We support this through focusing on network analytics training and implementation of renewed steering mechanisms.
Kenedict provides its clients with a set of powerful performance metrics based on the inherent structure of its internal knowledge networks.
Traditionally, R&D organisations have focused on various ways to measure their innovative performance, including input indicators (e.g. R&D expenses as a percentage of sales, R&D personnel headcounts per technology area) and output indicators (e.g. the share of revenues originating from new products, the number of patents applied for/granted, patent impacts through citation rates). The actual collaborative processes in which employees engage is, however, often overlooked when examining R&D performance.
Although companies invest heavily in increasing internal collaboration and knowledge sharing, most are not able to tangibly show the effects of these investments using objective, data-driven metrics. Kenedict provides just this – our clients benefit from a powerful set of newly developed leading and lagging indicators and are able to better steer their R&D organisations based on this.