You can hear this as an MP3.
[It’s important to understand just how much the theory has evolved in the last 10 years. Much more perhaps than in its first eight.]
Doug Kaye: Hello, and welcome to IT Conversations, a series of interviews recording and transcripts on the hot topics of information technology. I am your host, Doug Kaye, and in today’s program, I am pleased to bring you this special presentation from the Open Source Business Conference held in San Francisco on March 16 and 17, 2004.
Mike Dutton: My name is Mike Dutton, and it is my pleasure to introduce to you today Clayton Christensen. Professor Christensen hardly needs an introduction. His first bestseller, “The Innovator’s Dilemma,” has sold over half a million copies and has added the terms “disruptive innovation” to our corporate lexicon. His sequel — and you have to have a sequel to be a management guru — is entitled “The Innovator’s Solution” and is currently Business Week’s bestseller’s list. Professor Christensen began his career at the Boston Consulting Group and served as a White House fellow in the Reagan administration. In 1984, he cofounded and served as chairman of Ceramics Process Systems Cooperation. Then, as he was approaching his 40th birthday, he took the logical step of quitting his job and going back to school, where he earned a doctorate in Business Administration from Harvard Business School. So, today he is a professor of Business Administration at Harvard Business School where teaches and researches technology commercialization innovation. Professor Christensen is also a practicing entrepreneur. In 2000 he founded Innosight, a consulting firm focused on helping firms set their innovative strategies. And according to a recent article in Newsweek, “Innosight’s phones ring off the hook, and the firm cannot handle all the demand,” very similar to all the startups in open source here today. So, please join me in welcoming Clayton Christensen.
Clayton Christensen: Thank you, Mike! I’m 6 feet 8, so if it’s okay, I’ll just…the mic picks up okay. I’m sure delighted to be with you, especially because there is blizzard in Boston today; my kids have to shovel the snow!
As Mike mentioned, I came in to academia late in life, and the first chunk of research that I was engaged in was trying to understand what it is that could kill a successful, well — run company. And those of you who are familiar with it, probably know that the odd conclusion that I got of that was that it was actually good management that kills these companies. And subsequent then to the publishing of the book that summarized that work, “The Innovator’s Dilemma,” I’ve been trying to understand the flip side of that, which is if I want to start a new business that has the potential to kill a successful, well — run competitor, how would I do it? And that’s what we tried summarize in the book, “The Innovator’s solution.” It’s really quite a different book than the “Dilemma” was, because the “Dilemma” built a theory of what is it that caused these companies to fail. And then in the writing of this solution, I’ll just give you analogy for where we came out on how to successfully start new growth businesses.
I remember when I first got out of business school and had my first job. I was taught the methods of total quality management as they existed in the 1970’s, and we had this tool that was called a “statistical process control chart.” (Do they still teach that around here?) Basically you made a piece, you measured the critical performance parameter and you plotted it on this chart, and there was a target parameter that you were always trying to make the piece to hit, but you had this pesky scatter around that target. And I remember being taught at the time that the reason for the scatter is that there is just intrinsic variability and unpredictability in manufacturing processes.
So, the methods that were taught about manufacturing quality control in the ‘70’s were all oriented to helping you figure out how to deal with that randomness. And then the quality movement came of age, and what they taught us is, “No, there’s not randomness in manufacturing processes.” Every time you got a result that was bad, it actually had a cause, but it just appeared to be random because you didn’t know what caused it. And so the quality movement then gave us tools to understand what are all the different variables that can affect the consistency of output in a manufacturing operation. And once we could understand what those variables were and then develop methods to control them, manufacturing became not a random process, but something that was highly predictable and controllable.