Innovating To Win

Innovation Tips from Guy Kawasaki

In the Art of Innovation, Guy Kawasaki discusses 9 tips for innovation.  The post gives some good pointers to approaching innovation.  To summarize Guy’s points, innovation requires vision, tenacity, flexibility, customer orientation, and a splash of audacity.  Take a look at the post here.

Anatomy of an Innovation

I avoid writing about my job too often.  But, I have gotten a lot of questions recently on how the concept for Invention Machine Goldfire came about and its path to market innovation.  It’s an interesting case because not only is it about a technology and product innovation; it also takes us into the realm of innovating innovation.

Birth of a concept
In the winter of 2002, Trident Capital invested in a company called Invention Machine.  Trident tapped Mark Atkins and me to be the turnaround team.  (Mark and I had just been part of a successful exit for Trident in where he was CEO and I was CTO.)  We had helped with the investment due diligence for Trident, and we felt the company had some interesting technologies.  However, the existing products and positioning were not optimal but could be realigned to produce great value.

Sitting at the Au Bon Pain across the street from the office, Mark and I discussed the strategy for the company.  On a napkin, we mapped out a concept to create a new software platform for innovation that would bring together some of the company’s technology and knowhow assets.  This new product would combine innovation methodology with state of the art semantic technology to make is easier for knowledge workers to innovate.  Mark asked me if would be possible to do; I told him it would.  He asked how long it would take; I told him we would have a deliverable product in 10 months.  Of course, identifying a product concept is a far cry from delivering a product.  There were some important things that needed to be understood along the way.

Assessing resources; Understanding the job
Few things are entirely new.  All innovations build on the past.  New products evolve from the changing needs of customers as they interact with incumbent solutions to do today’s jobs.  For this reason, the first issue I tackled was to understand the resources we had to work with and the jobs that customers wanted to get done.

From the resource perspective, it begins with the people.  I interviewed every person on the product team to understand the scope of skills and the depth of the team.  Fortunately, we had very strong people on the team including a world class research group.  Another aspect of resource was the existing technology.  The company had a grab bag of componentry from the existing products.  Some of it would be reusable, but much was not going to contribute to the new product.  The third internal asset of the organization was its institutional learning about innovation best practices.  The company had been engaged in innovation consulting for over a decade with some of the leading innovation companies in the world and had helped these companies to successfully build repeatable innovation practices.

The other key asset was the customer base.  Most of my first 45 days at Invention Machine was spent on the phone interviewing clients around the globe, learning from them what they did.  Each interview recorded not just what the client did with our software, but also the nature of their work, what they did outside of our software, and what they wished they could do differently.  This interaction was the key to further developing the original concept into a strategic path to delivering value.

Moment of synthesis
There was no aha moment.  Innovation doesn’t really happen like that.  Rather, it was like a photographic image gradually appearing as you watch the paper sitting in the developer bath.  The pieces were fitting together.  Innovation is a full life cycle activity.  At each point in the lifecycle, knowledge workers have two main challenges: indentifying the right concept, and finding the optimal solution to the problems of realization of the concept.  These challenges occur across and at every level of the entire value creation chain.  Software tools provide problem analysis frameworks for thinking about and modeling innovation challenges.  The resulting models can be thought of as networks of requirements and problem statements.  These statements can be mapped into conceptual representations of solution templates and correlated with information captured in knowledge databases to identify precise solution concepts for the problems.  In short, we could imagine a system where as an engineer, designer, or scientist went about their work, the software would automatically source and deliver high relevance information and speed the time to solution.

Validating value
A concept not delivered in merely a concept.  If it does not deliver value and is not accepted by the customer, there is no innovation.  In the fall of 2003, we delivered the first version of Goldfire to customers.  A module called the solution manager provided a new capability where the software figured out what question the knowledge worker needed to ask based on the problems implied in their models and sourced high relevance concepts without the user needing to submit queries.  User feedback was strong; user liked the integration of best practices and knowledge access. The new platform delivered value for the innovation workers.

Endless innovation
Of course, innovation is a continuous process.  As new solutions are introduced, our vantage point and understanding evolve as do the goals and aspirations of our customers.  Through studying what innovation workers need to do, we have continued to evolve the product with a number of changes.  Some of these have been incremental enhancements, but others have been big leaps forward.  That is a familiar pattern for most products.  There is a broad spectrum of change that occurs in the innovation process.  It is important to swing for the fences if you are going to find the big innovation that sets you in a class by yourself, but the incremental innovations are important too.  Small innovations build value and more importantly, when pursued with the same innovation methods hone your innovation capabilities so that you are always ready to innovate on demand.

I hope you found this recounting of one path to innovation interesting.

Sustainable Innovation at Johnson & Johnson

Here's a great podcast with Ray Jakubowicz, R&D Program Director, Worldwide Systems R&D for Johnson & Johnson’s Ortho-Clinical Diagnostics.  In this podcast, Ray talks about the role of sustainable innovation and how it’s viewed as a key competency for the future at J&J.

Ray also discusses the challenges of spreading innovation disciplines across independent operating companies in a larger organization, and the need to develop new long horizon metrics to measure corporate return on innovation.

It’s a good interview with a change agent in the trenches of innovation every day.  Watch the podcast below and hear Ray’s take on the impact of sustainable innovation practices.

Dipping Into Your Innovation Gene Pool

Nature teaches us that genetic diversity is a good thing.  It creates the environment for change and drives evolution.

For innovation, diversity of a different sort is just as vital.  Diversity of perspective and ideas creates the same type of change enabling dynamic.  There are many ways companies try to tap into different perspectives.  Here I want to point out a simple but often overlooked path to innovation genetic diversity.

This morning, I conducted a working design session with some of my product engineers.  However instead of just having the usual cadre of architects in the room, today we had team members who weren’t engineers involved in the discussion.  I had invited them specifically to gain the benefit of their non-engineering centric point of view.  It was a fabulously productive session and helped to fill several voids in the thinking to date on a new product concept.

These non-engineering participants had to go far out of their way to attend the meeting.  (They both have 2+ hour commutes to get to the office.)  How did they feel about being asked to come in just for this meeting?  When I began the meeting by thanking them for coming in, one of them responded by saying, “Are you kidding?  When I told someone on the train going home last night that I had been asked to come in for the meeting because the company wanted my input on a new product we are designing, several people said “Boy, I wish my company valued my opinion.””

Bottom line is that this simple exercise was a win at multiple levels.  From the product design perspective, the input of the non-engineers was invaluable.  From the company perspective, the resulting product will undoubtedly deliver much high value to our customers as a result of this session.  From an HR perspective, these workers feel more enfranchised in their job roles.  How could one hope for a better outcome!

Innovation leaders must recognize that they are not the only source of relevant concepts to drive innovation.  What’s more, you can’t always predict where the valued input will come from, nor can you expect people will always recognize when they have a valuable contribution to make.  Yet, there people in your organizational eco-system that have big contributions to make.

In order to tap into your organization’s innovation gene pool, make it a practice to reach out to people who are not always included in the discussion and draw them in.  Both you and they will be surprised at the value this brings.  Also, use technology to help connect your knowledge workers.  Since you can’t predict where the great idea may come from, implement a passive collaboration environment that connects people around their actionable knowledge.

Take these two steps and you will see a great lift in your organization’s innovation capability.

Innovation Via Biomimicry – What You Said

The results are in, and once again they tell an interesting story.  In our most recent poll, we asked if you are employing biomimicry as a source of innovation inspiration.  The results can be seen to the right.  I find two things very interesting about these results.

Of most interest is that over 40% of respondents are unfamiliar with what biomimicry is.  With the tremendous speed with which science and technology evolve, it is hard to stay abreast of everything under the sun.  However, innovation practitioners have a unique mandate to do just that.  Our ability to drive innovation sustainably is directly related to our ability to harness available global knowledge and experience across many disparate domains. 

Biomimicry is the practice of studying the solutions of nature and looking for ways of repurposing the best solution which have withstood the crucible of natural selection to our innovation challenges.  Biomimicry has emerged in recent years as a very fertile and rich method for tapping into the wisdom of nature.  For more information on this valuable technique, check out the Biomimicry Institute.  You might also find this two part interview with John Bradford of Interface an interesting example of how one company has put biomimicry to work.

Biomimicry and Sustainable Innovation: Part 1

Biomimicry and Sustainable Innovation: Part 2

It is also interesting that among those who are aware of biomimicry, the responses were evenly split as to whether you use the method or not.  I am not sure how to interpret this result.  What do you think?

Once again, your participation in the innovation poll has provided an interesting picture of the state of innovation.  Please remember to vote in the latest poll on which types of innovation initiatives will drive your company’s strategy in 2009.

Interesting Video On Drawing Innovation Inspiration From Nature

Thanks to Bill Fowlkes for pointing out the very nice segment from the TED folks.  In this talk, Robert Full of U.C. Berkeley provides some very interesting insights on how biomimicry can be an inspiration for innovation citing examples of animal locomotion.

There is certainly a lot of fruitful ground in this area.  I am seeing many companies gaining tremendous value from using innovation best practices to target areas of investigation and then finding high value conceptual paths from the examples of natural systems.

The video runs about 20 minutes.

Want to learn more about how companies are applying these concepts?  Here is a video podcast featuring John Bradford of Interface talking about how his company is doing it.

This video podcast runs about 12 minutes.

Thinking About Metrics

Paul Sloan considers innovation metrics on his BQF blog.  This question comes up quite frequently as companies need to be able to justify their investments in innovation and R&D.   As Paul notes, the Boston Consulting Group recommends in a related report that companies should select a small number of metrics appropriate to the business.

Here are some thoughts I have had on the subject of metrics.  Tell me what you think.

Process metrics – these are raw performance measures of ideation and conceptual solution process
- Number of concepts identified (measures quantity of work output)
- Quality of concepts (subjective measure of quality of work output)
- Percent of concepts from outside of domain of expertise (“out of the box” measure)
- Resources to complete project (an efficiency measure)
- Percent concepts to survive first filter (a more objective quality measure)

Project impact – these are measures of how the innovation effort impacts the effectiveness of your product development process and accelerates market entry
- Cycle time to product delivery (are you impacting time to market)
- Prototype cycle reduction (are you achieving a direct cost reduction benefit)
- Process milestone attainment (are you impacting your NPDI process efficiency)

Value building – does innovation result in revenue or market share growth
- % revenue from new products (an old but useful way of looking at innovation impact)
- Individual project ROI (a more direct way of assessing impact on a per project basis)
- Successful launch rate (a key metric to show improvement in effectiveness of go-no-go decision process)
- Patents (an important measure of efficiency, particular as compared to peer organizations on a patents per revenue dollars basis)
- Revenue & market share growth (are you driving the top line)

Developing Innovation Skills

This is installment three in my series on building a sustainable innovation culture.  In installment one, I outlined the Five Pillars of Sustainable Innovation Culture.  Installment two examined Executive Leadership, the first pillar.  Here, we will look at the next pillar-- Skill Development.

There is a widespread belief that innovation just happens—somehow some bright person gets an idea out of nowhere.  This notion could not be further from the reality of innovation.  Innovation is a process; innovation is hard work; innovation is a transferable skill.  In my recent post, “Can Innovation Be Learned,” we explored the rationale for why innovation can be viewed as a learned set of behaviors and something which can be mastered.  Organizations need to understand this and develop the training and resource development programs to build innovation process skills as a core competency in the product research and development teams.

In approaching the issue of skills development, the organization must consider the variability in roles and needs of different innovation workers.  There are several ways to look at this.  First let’s consider the hierarchy of problem solving complexity.  Consider the pyramid of problem complexity below.  Here, complexity is a measure of solving difficulty that may involve may attributes.  However for purposes of discussion, I will focus on one attribute—the accessibility of the solution. 

At the top of the pyramid, we have the most complex problems.  Denoted as star-complexity problems, these problems are beyond the capabilities of conventional solution because the knowledge required to solve the issue is beyond the scope of known science.  These problems will wait for many years before the requisite information is defined at which time they can be solved with thanks to the discovery of new phenomenon.

At the other extreme, we see non-complex problems.  These are issues that are easily addressed within the local knowledge of the solver.  These are issues that we have seen before and when presented with the same situation can readily say, “Oh yeah, we addressed that issue before, and here’s what to do.”

For those familiar with the concept of levels of inventiveness, this classification will make a lot of sense.  The key take away from this model is this.  The higher you move in the complexity pyramid, the less frequently the class of problem is encountered.  Conversely, the higher the problem occurs in the pyramid, the greater the potential value of the solution.  Lastly, no one really needs to be trained to solve non-complex problems.

So, what does this tell us about planning a strategy for building innovation skills within an organization?  Simply, not every one needs in depth innovation skills.  That’s right.  Problems at each level of the pyramid yield to different strategies of solving.  Simple strategies should be used for more simple problems; more robust methods for more complex problems.  The importance of this is to work with human nature.  If you feel you must bring out a highly complex method for what feels like a relatively simple problem, you are less likely to go through the process.

Beyond complexity, there is another dimension to the innovation skill landscape.  This is inherent in the diversity of the innovation tasks that different innovation workers perform.  This point should require no explanation.  It should be fairly obvious that the task of identifying a new application for a core technology is quite different from the task of improving production efficiency.  Both tasks may lead to innovative solutions, but each will follow a very different path of discovery.

With this in mind, I recommend a tiered innovation skills program.  At the first level of skills development, knowledge workers should be trained in the basics of innovation methodology.  They should be given the tools they need to address medium-complexity problems that are common to their job function and low-complexity problems with facility.  This will allow them to experience early success and practice their innovation thinking on a daily basis.  Additionally, the newly trained innovation worker should be shown how to identify problems beyond their current grasp and how to seek help elsewhere in the organization to tackle these issues.

As innovation workers gain skill, advanced training should teach the methods of solving problems into the high-complexity range and specific methods related to common cross-functional problem sets.  Finally, innovation workers that show the desire to master the full gamut of innovation methods can be trained in the low-frequency innovation methods as well as facilitation skills.

Following such a template helps the organization build pervasive basic innovation skills and also a core team of high-skill, leveragable innovation resources.  The cultivation of innovation skill in the enterprise is important for developing a sustainable innovation culture.

Can Innovation Be Learned?

Reader Tim de Jardine raised the perennial question of “Can innovation be taught?”  It’s a question that comes up from time to time because of the persistent myth of the great thinker in the back room who is singularly gifted with the ability to create great ideas.  If one believes in this myth then one believes that people are either blessed with the ability to be innovative or they are not.  If you happen to be among the unfortunates that are not blessed with the gift then you can just forget about innovation because, as Andy Rooney once put it, “You can’t teach a fence post.”

However, the myth is just that – a myth.  I promised Tim that I would prepare a summary of the case for the innovation as a trainable skill.  So, here it is.

First, consider the following diagram.  It is a highly simplified model of the innovation thought process.  With out going into the relative merits and nuances of the Wallas versus Wertheimer thought models, this diagram draws upon the Wallas model in a way so as to facilitate a simple discussion.  It is said that a picture is worth a thousand words.  While I don’t know which thousand words are embodied in this diagram, there is a story being told here—a story of the birth of a big idea.

Innovation doesn’t happen in a vacuum.  It is a response to a stimulus.  The stimulus is represented here by the Problem.  Consciously or sub-consciously, the innovator performs an analysis of the problem state.  This analysis can be roughly associated with the preparation phase of the Wallas model.  Through this analysis, the innovator gains a more intimate understanding of the problem and the unique attributes of the problem (represented by the bolt and sun icons) that define the requirements of potential solutions.

There is a second aspect to the innovative process which is termed in the Wallas model as the incubation phase.  In the diagram, this is termed "association".  (I am not entirely happy with my word choice here, and the difficulty in identifying a word for this process is indicative of why the innovation myth is so persistent.  It is difficult for people to consider a concept for which there is not a name.)  This process of association is where the innovator draws upon knowledge to identify concepts that are applicable to the problem.  I have termed this association to stress the notion that the innovator is somehow filtering a potentially vast pool of knowledge and making the connections between the problem and its attributes to the specific conceptual knowledge that speaks to the attributes in a way that may contribute to a solution.

Eventually, when a critical mass of connections have been made the innovator is able to synthesize a new concept which is a potential innovative solution to the problem.  This act of synthesis is represented in the Wallas model as the illumination phase.  After the creation of the new solution concept, the innovator moves on with the arduous task of defining and refining a concrete expression of the innovation (corresponding to the final stage of the Wallas model, the verification and elaboration phase).

The purpose of breaking the process down in this way is to shed light on the notion that each of the component processes of innovation is something that any individual is able to perform.  The argument can be made that different people may have differing innate abilities in these areas.  It is reasonable to assume this is true, but the dominance of genetic influence over environmental expression is not clear. 

You may not have the highest level of natural talent for a discipline, but that does not mean that you can not attain some degree of mastery of the basic skill.  Though I will never play in Carnegie Hall, by dint of practice, I can play the piano beautifully.  There is even strong evidence that in the domain of cognitive function, some basic abilities can be developed over time as the brain retains the ability to regenerate and develop new connections well into old age.

The development of each of the component processes of innovative thinking can be enhanced through both training and technology.  There are many well understood and documented techniques for the problem analysis step.  These techniques are designed to help accelerate and clarify understanding of the problem state by identifying the unique problem attributes in a comprehensive and repeatable way.

Through the application of these techniques, innovation workers are able to focus their thinking more effectively by zeroing in on the right problem more quickly and having a much clearer concept of the metrics by which to evaluate potential solutions.  Technology can be applied to assist innovation practitioners in the application of specific methodologies to both shorten the path to mastery and ensure the accurate application of the method.

In the association process, there are many practical psychological methods to help facilitate making the connections which are the necessary precursor to the creative act of innovation.  The techniques have been successfully in wide use for many years.  Technology also can play a huge role in this area.  As the end result of problem analysis is a statement of design intent, technology can be used to surface concepts that map directly to the stated design intent—concepts which would otherwise be lost in the great sea of global knowledge.  By doing so, technology can place the innovation worker at the very apex of the confluence of information that leads to the state of illumination.

The same is true for the synthesis step.  Through the combination of method and technology, innovation workers can be led through the process of fitting the pieces together in a meaningful way and subsequently deriving a practical expression of the solution.

The proof points for this argument are easy to find and underscore the argument with a compelling body of evidence.

TRIZ Enabled Mergers and Acquisitions

For those of us who did not make it to TRIZCON2008, a neat paper by Jim Belfiore has been published on the TRIZ Journal site.  This paper is a good read and presents a novel take on applying some TRIZ concepts to the task of analyzing companies for possible M&A interest.  Take a look and share your thoughts on this interesting piece.