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Innovation 101, Lesson 3: Open Innovation



It sounds good, but how can real scientists apply it in their labs and in the tough world of technology buying and selling, asks Ali Alwattari of Procter & Gamble?



By Dr. Ali Alwattari, Innovation Practitioner and Author



Published July 8, 2010
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Picture this—a scientist leaves 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 “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. However, its utility and value to real R&D organizations depends on the ability of existing skills and practices to make sense out of open innovation; i.e., it is not automatically a slam dunk.

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 regular 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 new performance before jumping into the “how” of a technology. This means scientists must become good at “going shopping” for whatever technologies can accomplish the breakthrough goal, be the source internal or external.

To practice this scientific approach to open innovation involves finding and screening multiple new technologies and finding out how they rank versus a specific set of new product performance goals. The probability of success of this strategy is higher than pre-selecting certain technologies because the innovator’s focus is shifted to problem solving instead of innovation by “technology push.”

This is illustrated by the example below – the case study of “developing a longer wearing product”: Traditional Approach vs. Open Innovation:


“Innovation Scorecard”
Traditional
“technology push”
Open innovation
“technology pull”
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 product

In order to get a longer lasting product, materials from the product industry were tried, but the performance goal was not met. The project team then changed strategy to more open innovation by discovering that a better quality and more water resistant coating was what really matters. This led to going outside the box of industry technology and identifying alternate technologies and industries based on “function” rather than extension of “existing technology,” the more obvious but underperforming solution.

In conclusion, rather than coming up with new technologies and hoping for innovative outcomes, innovators can increase the probability of success in getting 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.”

©Ali Alwattari 2010


About the Author
Dr. Ali Alwattari is with Procter & Gamble as an R&D and New Product Development scientist introducing new technologies. He is the inventor on 10 patents and applications including elastic polymers for Cover Girl No Smudge Mascara, controlled stress wrinkle reducers for Olay technology pipeline and controlled release substrate for Pampers Sunnies sunscreen wipes.

Prior to joining P&G, Dr. Alwattari was with Gillette serving as New Product Development manager and scientist leading innovation for Shave Preps and Post Shave Skin Care. New technologies validated and transitioned into product development included color visual signal shave gels, post shave cooling gel-wipes, high lubricity shave gels and self-heating shave creams.

Prior to that, he was an R&D scientist with Nexis Biotechnologies.

Dr. Alwattari received his BS in chemical engineering from MIT and a PhD in chemical engineering from the University of Texas Austin.

He can be reached at: alwattari@alum.mit.edu or 513-375-9190



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