We all know that some of the greatest breakthroughs and innovations come from unexpected sources and exposure to new or different thinking.
So the idea of ‘open innovation’ – a more decentralised approach to innovation that could include collaboration with competitors, customers or even between disparate departments of a large organisation – should be a no-brainer for an enterprise seeking to stay ahead. It can reduce cost, accelerate time to market and create new revenue streams.
But letting go of hierarchy, and opening your soft, R&D underbelly to a collaborator can be risky and run counter-intuitive to your instincts. How do you protect your intellectual property? How do you decide which projects to invest the not-inconsiderable time and resources needed to explore creative collaborations?
Australian has been slow to embrace open innovation. Our rank for collaborative innovation is in the bottom half of the OECD scorecard.
For those that do try it, many find it hard to get off the starting blocks because they are reluctant to let go of their siloed and hierarchical company structures, dooming the project before it has even begun.
Innovation experts have wrestled with the task of designing a business model that can help guide organisations to identify the right approach and strategies to make open innovation a commercial success.
Forefront among these is former Silicon Valley executive, Professor Henry Chesbrough of UC Berkeley’s Haas Business School, an early proponent of open innovation who notes the flourishing of new executives with ‘open innovation’ in their job titles.
Back in 2005, UK researchers Keld Laursen and Ammon Salter devised an ‘inverted U-shape of openness’ model with an optimal degree of openness leading to maximum innovation success. They acknowledged a major hurdle was accommodating the varying, ‘optimals’ of sectors, individual companies and situations.
This challenge was taken up Dr Alexander Lang, who presented his research designing an algorithm-based business model that can generate recommendations customised to individual organisations, at the ISPIM Innovation Summit in Melbourne in December 2017.
In 2015, Lang surveyed 96 companies in Germany and Austria to develop a matrix of the internal and external groups with whom their R&D departments might collaborate. They answered questions about their level of trust and frequency of collaboration with these groups, allowing Lang to determine a level of ‘real openness’. He then calculated the ‘virtual openness’ by assessing the risks and opportunities of each collaboration to determine the ‘ideal openness’: the gap between real and virtual openness that indicates the optimisation potential.
Lang discovered it was difficult to land on one value for a whole company’s real openness as it varied between departments, projects, project phases and partners. He identified 12 ‘influenced factors’ which he categorised into graded risks (negative values) and opportunities (positive values) for each collaboration. These included: quality and quantity of knowledge in the company, competencies within the company and of the partner, speed, adaptability, and performance of product development, user experience, sales volume, costs, market position and legal implications.
Using this data, he first applied an ‘easy algorithm’ by adding up all the values of the factors within each group of collaborators to determine the virtual openness for each collaborative relationship.
He then optimised the model to address identified deficits and generate a more accurate, advanced algorithm.
Lang introduced a methodology to calculate the relative urgency of action for each step in the process of open innovation, and the results were depicted in a histogram for easy visual reference of priorities.
To compare the simple and advanced algorithms, the recommendations generated by each model were discussed with companies in a series of workshops in 2017. The recommendations of the advanced model were unanimously agreed to be the more relevant to the businesses.
Lang has developed a software tool, enabling companies to self-assess their challenges in implementing open innovation, to generate company-specific recommendations. He is continuing to optimise this tool to make it more specific to company type but, obviously, this requires an enormous amount of data collection. Lang’s long-term vision is a self-assessing learning tool that can be adopted by companies in the future.
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– Danielle Koopman, Impact Innovation guest blogger – @DanKoop1