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Sustain Innovation with Systematic Inventive Problem Solving


In its latest Technology Forecast – Decoding Innovation’s DNA – PwC interviewed Invention Machine EVP and CTO, Jim Todhunter, on the topic of systematic problem solving.

Systematic Problem SolvingIn the interview, Todhunter explores how systematic methods for problem solving integrated with precise knowledge research capabilities can help companies drive predictable, sustainable innovation.

As a leading provider of innovation software, Invention Machine is afforded the opportunity to regularly hear from business leaders across the globe about the challenges their organizations face when it comes to innovation.  What we don’t hear are people complaining about the ability to generate ideas. What we mostly hear is “We’ve got tons of ideas. Everybody’s giving us ideas. What we lack is the ability to filter the ideas, to really be able to understand how to focus on the ideas that are going to have the greatest potential. And then, given that we’ve identified those ideas, how do we move from an idea to an actual deliverable concept to delivered products?”

With the right innovation tools and methods, organizations can better identify and focus on those problems they need to solve and focus on which ideas represent the optimal, viable solution.  Does the idea and the eventual product it represents align with the competencies and strategy of the corporation? Does it fit relative to revenue and profitability generation? Does it  contribute to margin? And what is its feasibility and practicality to manufacture?

To confidently answer these questions and deliver high-value innovation, organizations need the right tools and infrastructure to synthesize information in the  context of a specific business problem or challenge. Fundamentally, that means that organizations must arm innovation workers with reliable innovation methods  such as Root Cause Analysis, FMEA, Value Engineering and TRIZ.  These proven methods server to focus problem solving, help identify root cause issues, and engineers and scientists better define and understand problems. They also bring uniformity, predictability and discipline to the ‘art’ of inventive problem solving.

But innovation tools and methods alone are not enough. Organizations must also equip their innovation workers with knowledge— internal and external technical know-how that is not readily accessed today. When you do that, you start to create an innovation intelligence ecosystem that allows each individual innovation worker, based on their role, to have optimal access to information on a just-in-time basis to help them accelerate the contribution to innovation.

Click here to access the interview in full.