The top rated “The Open Innovation Marketplace”, authored by InnoCentive’s Founder Alpheus Bingham and President and CEO Dwayne Spradlin has been receiving positive reviews from innovation practitioners, CEOs and executives, industry analysts and the media. Though the book presents a comprehensive overview as well as a deep dive into the practice of open innovation, the authors still had more to share. Below is a chapter that didn’t make it into the book, which we’d like to share with you now. Enjoy!
Closed Innovation Suboptimizes Solutions – The World Can Do Better.
by Alpheus Bingham, Founder, InnoCentive
One of the expectations of my early career in the pharmaceutical industry was to design new synthetic routes (ways to make medicines). This was for a whole variety of molecules, not just the ones for which I had some special training and experience. At various times it included heterocycles, beta-lactams, silanes, inorganic salts, and many others. When asked to undertake such a challenge, I usually did so based on my own grasp of chemistry and the aid of a technician or two to carry out the exploratory experiments. That is not to say I never sought help. In fact a small, informal group of seven or eight PhD chemists would meet weekly and share what they were working on in hopes to gain some insight and ideas from the others. I think my experience was typical in a commercial research environment.
Contrast the approach just described, closed innovation within an industrial organization, to a purely academic exercise from graduate school that was much more successful in exploring a wider range of potential solutions. In a synthetic organic chemistry course, taught at Stanford University and overseen by Professor William S. Johnson, 20 other “generally-accepted-as-swift” chemists and I were assigned one molecule each week. Our job was to design an appropriate synthesis for that substance, that is, ways to make the molecules much like the ones I would later be making in my work assignments. We were not asked to actually conduct the synthesis in the laboratory but to support each of our recommended steps with precedents from the scientific literature. This was, essentially, no different from the first steps I would later take in the synthesis challenges I faced as an employee. These weekly homework assignments were not simple problems. Each assignment required 20 to 80 hours of effort, and students generally dropped all other coursework while this one class was taken. Papers were turned in on Monday, and that Wednesday a special evening class was held, which often extended into the wee hours of the morning.
When the students arrived, the professor began by listing five student names on the chalkboard. Each was to come forward, sketch out their proposed solutions, and then later return to the front of the class to defend their proposals. In advance, nobody knew whether being selected meant that the faculty felt there was extraordinary merit in their proposal, or if they were to serve as a warning example for having made errors that the instructor wanted to avoid ever having reoccur in the future.
One great take-home lesson from this semester of effort was how remarkably different the 20 or so answers were each and every Thursday evening. Even though each student had already met reasonably stringent criteria for their ability to compete in such a course, they interpreted each problem differently and approached it in a unique manner. One thing was clear: It would have been impossible on any given week to have chosen only one student to give the assignment to, and then, to have expected the outcome to be one of the more outstanding results. The uniqueness of each individual’s experiences and personal study could not have been read in the transcripts of their prior University coursework or even in their current research and publication record. How was it that, in industry, the assignments of such challenging problems could be made almost capriciously, and optimal solutions be expected from that process?
Well, in truth, optimal solutions can NOT be expected from the limitations of commercial research processes. But you must also acknowledge that optimal solutions are not usually a commercial objective to begin with. Solutions that are “good enough” are good enough. And “solution space,” the matrix of all possible solutions, is usually too vast for a complete search. But, none of that suggests that innovation can’t be much better than it is — that it can’t be greatly improved with a new approach. It’s that potential for improvement — evident in the contrast between what was produced with 21 parallel, diverse efforts in Johnson’s course and what gets produced under classical, serial, commercial assignments — that suggests the need for a new innovation framework and mechanism for rationally engaging in open innovation. A greater diversity of approaches to problems in commercial, philanthropic, or government endeavors would likely yield superior solutions, greater economic viability, and shorter innovation cycles — all of which would ultimately benefit both shareholders and customers. These were the issues that led to the founding of InnoCentive and that presently drive the adoption of open innovation by the most successful innovators.