By: Ali Alwattari
Open innovation expands opportunities for success by encouraging innovators to look outside their organizations for potential technologies and solutions that could be adapted to the challenge at hand. However, its utility and value to R&D teams depends on the ability of scientists to make sense out of open innovation. Dr. Ali Alwattari explains how.
Picture this: a scientist is leaving his manager’s office, enthusiastic but a little confused. The manager informed the scientist that from now on R&D should use “open innovation”. The scientist thinks, “That sounds good, but what does it mean in the lab?” The driving force for continuously finding ways to improve how we innovate is the inherent new-ness and uncertainty associated with innovation, research, ideas, and technologies.
Open innovation is an emerging concept theorized to broaden technical and economic options for success by encouraging workers to look outside their organizations for potential technologies and solutions that could be adapted to the challenge at hand. However, its utility and value to real R&D organizations depends on the ability of scientists to use their existing skills and practices to make sense out of open innovation. In other words, open innovation is not automatically a slam dunk for most R&D organizations.
Data from projects and professional experience both suggest that one of the barriers to open innovation is that nobody has yet explained it in a language or methodology that is familiar enough for scientists to learn and apply it. So, let’s start by translating open innovation in a technical way:
More productive innovation results are accomplished when projects define specifically “what” constitutes a breakthrough in performance before jumping into the “how” of a technology. This means scientists have to become good at “going shopping” for whatever technologies can accomplish the breakthrough goal, whether the source for it is internal or external.
To practice this scientific approach to open innovation involves finding and screening through multiple new technologies and measuring them against a specific set of new product performance goals. The probability of success of this strategy is higher than pre-selecting certain technologies and trying to make them fit. Why? Because instead of taking a technology that the innovator is familiar with and trying to “push” it to fit the solution needed, the focus is shifted to problem solving – becoming a solution finder. This is illustrated by the example below.
Case Study “develop longer wearing product”: Traditional Approach vs. Open Innovation
In order to get a longer wearing product, materials from the firm’s current industry were tried, but the performance goal could not be met. The project team then changed its strategy to focus on open innovation, by concluding that what was really needed was a better coating. This led the team to go “outside the box” of the technology of its industry to identifying alternate technologies and industries. This open strategy led to a breakthrough.
| Innovation Scorecard | Technology push | Open innovation |
| Industries identified | 1-2 | 5-10 |
| Technology leads found | 4-6 | 40-50 |
| Performance in product | 1 lead minimally helped | 8 leads significantly helped |
| Intellectual property | 0 patents filed | 3 patents filed |
| Technologies transferred | 0 made it out of lab | 2 made it into new products |
Conclusion
In conclusion, rather than coming up with new technologies and hoping for innovative outcomes, innovators can increase their probability of success in moving innovations from lab to consumer by learning how to apply open innovation in the lab. This requires a substantial shift in intellectual emphasis from the tactical approach of “technology push” to the strategic approach of open innovation and being good “technology finders.”
© 2006 Dr. Ali Alwattari, Innovation Author and Practitioner