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Patterns That Predict Innovation Success

Published date: June 4, 2012 ะฒ 3:00 am

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The New York Times published a list of โ€œ32 Innovations That Will Change Your Tomorrow,โ€  an eclectic mix of concepts that range from the wild and wacky like SpeechJammer (#14) to more practical ideas like a blood test for depression (#25).

I analyzed each of the 32 concepts to see which ones could be explained by the five patterns of Systematic Inventive Thinking.  These patterns are the โ€œDNAโ€ of products that can be extracted and applied to any product or service to create new-to-the-world innovations.  Dr. Jacob Goldenberg found in his research that the majority of successful innovations conform to one or more of these patterns.  Conversely, the majority of unsuccessful innovations (those that failed in the marketplace) do not conform to a pattern.

Based on my analysis, here is the breakdown of which pattern explains each innovation on the list:

  • Task Unification: 9
  • Attribute Dependency: 7
  • Division: 3
  • Subtraction: 3
  • Multiplication: 3
  • None of the above: 8

In other words, 24 of the 32 innovations in the New York Times list could be explained by the SIT patterns.  The eight concepts that were not pattern based were either process or performance enhancements. For example, the carbon fiber bicycle frame (#9) is one of the eight.  That does not mean these eight will not be successful.  But based on Dr. Goldenbergโ€™s research, the odds are they are less likely to succeed than if they had one of the patterns embedded inside. The patterns, in essence, are predictive of success.

The Innovation Measurement Trap

Published date: April 9, 2012 ะฒ 3:00 am

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Category: Uncategorized

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Measuring innovation can lead to unintended consequences.  Here are eight ways to avoid the traps.

1. Measure innovation alternatives, not just the current program.  When assessing the impact of an initiative, always ask, โ€œcompared to what?โ€  Donโ€™t fall into the trap of measuring only what the company is doing today.  Rather, measure it against the next best alternative.  For example, if you are using a ideation methodology like S.I.T., be sure to measure the effectiveness of using S.I.T. versus another ideation method.  Understand why you are using one method over another by forecasting results from the alternative.  This re-frames the question from โ€œdoes this method work?โ€ to โ€œdoes this method work better than this alternative?โ€

2. Measure inputs, not just outputs.  Companies are quick to judge innovation initiatives based on the yield of ideas.  A better approach is to be mindful of what the company puts into innovation.  Measure activity such as number of training sessions conducted, number of employees skilled at a methodology, and man hours used in innovation workshops.  Benchmark these against competitors and other relevant companies to gauge whether you are investing enough.

3. Measure quality, not just quantity:  People focus too much on quantitative measures because theyโ€™re easier to collect than qualitative ones.  Quantitative data seems more objective.  Simple measurements like โ€œnumber of ideas generatedโ€ may seem valuable on the surface, but these can lead to the trap of โ€œidea churningโ€ just to hit big numbers.  One way to avoid this trap is to assign a panel of independent reviewers to do a qualitative valuation of all ideas generated.  Develop a standard rubric or use existing methods to evaluate the creativity of ideas on a qualitative basis.

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