Using open innovation to improve seed analytics
A really interesting case study this week from Swiss agrochemical and seed company Syngenta, who needed to find more efficient ways of identifying desirable plants and make optimal use of resources. The case study is published on the MIT Sloan website (you have to supply your details to download).
Syngenta used two different crowdsourcing platforms to address their challenges, selecting the most appropriate platform according to what kind of solvers they needed. They chose to crowdsource because their challenges were highly technical, requiring skills that are hard to find through the normal recruitment process, and in order to speed up the process of finding the right solution.
The most interesting aspect of the case study is Syngenta’s account of the steps they needed to take to make open innovation work in their organisation, for example:
“leveraging the potential of outside experts requires close cooperation from in-house employees, who need to feel that it’s good for the business and doesn’t threaten their jobs. Cooperation from staff is also essential for framing problems and evaluating options.”
They took clear and careful steps to address these issues, as follows:
“Every problem, challenge, or contest was posted internally so that staff had the first opportunity to offer solutions. Even when in-house talent lacked the particular skill set needed to address complex mathematical issues, their practical experience in plant breeding helped refine the questions we were asking. Rather than seeing the shift as threatening, employees saw that their input was being used to advance an ambitious project.”
It’s really worth reading the whole case study to see how they built open innovation into their business and learned to frame challenges properly:
“In our experience, one of the biggest challenges of open innovation is learning how to define problems you want to solve in ways that engage potential problem solvers. As a result, we have learned that rather than presenting problems broadly, it’s often better to divide them into smaller chunks.”