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When to Apply Design Thinking and Lean Innovation (and when not to…)

By Jan van den Ende posted 01-27-2022 16:03

  

Product Design Develop Tools

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3 minutes

Design Thinking and Lean Innovation are extremely popular today. Broadly, in the business world, people talk about MVPs (Minimum Viable Products) and Pivots when discussing activities related to innovation. Design Thinking and Lean Innovation seem to be the solution to all kind of problems: failing projects, lack of innovation, delays, demotivated innovators, etc. But can these concepts be applied as successfully as claimed?

First, what are they? As I explain in my book Innovation Management, both Design Thinking and Lean Innovation are iterative innovation processes, to be executed at the beginning of an innovation project. Design Thinking focuses on empathizing with customers. It means that by observing the customer, or by asking repeated questions, the innovator is able to obtain an understanding of the hidden needs of customers for which solutions are desirable. Based on these insights, the innovator develops solutions, tests them with customers, and redesigns them. Lean innovation starts with proposing a simple solution, a Minimum Viable Product (MVP), to customers, and adapts (‘pivots’) until a solution is reached.

Both Design Thinking and Lean Innovation have a laudable purpose: decreasing uncertainty around an innovation as early as possible. However, today they are seen as panaceas for any problem experienced in innovation activities. Are they really as extensively adaptable as some think?

First, these approaches of course only work in a context of relatively simple solutions, which are easily adaptable. Nobody could have imagined that innovators would develop an aeroplane or harbour crane in a lean innovation process. But there is another, less obvious, limitation. Both Design Thinking and Lean Innovation start with a rather arbitrary point – a group of customers, a first solution – and move on from there. Because of that, in my book I label these approaches as ‘evolutionary’. Is such an evolutionary approach the most appropriate in every context? While the claim is that these approaches are especially designed for turbulent environments, I would counter that they are suboptimal in exactly these contexts!

And why is that? The figures below show two environments. The bottom plane of the graphs represents the choice of innovation solution. The vertical direction shows the expected performance of the innovation. If you start somewhere in the left figure and move (‘pivot’) to a better solution, you automatically end up in the middle, in the optimal solution. In the turbulent environment on the right-hand side, this is not the case. When you start somewhere and you move to a better point, you may end up in a rather low ‘hill’. In other words, if you had started somewhere else, the end result could have been better.

Figure. A stable (left) and turbulent (right) environment

What does this mean in practice? When you are in a turbulent environment, you can more effectively test completely different alternatives, and then make a choice between them. I call this a ‘set-based’ approach, rather than the evolutionary approach of Design Thinking or Lean Innovation. Applying only one of these two approaches can lead you to a suboptimal end result in such an environment. While many innovators assume that these two approaches prepare them for a turbulent environment, in reality, these approaches underperform in those settings!

What does this mean in practice? When you are in a turbulent environment, you can more effectively test completely different alternatives, and then make a choice between them. I call this a ‘set-based’ approach, rather than the evolutionary approach of Design Thinking or Lean Innovation. Applying only one of these two approaches can lead you to a suboptimal end result in such an environment. While many innovators assume that these two approaches prepare them for a turbulent environment, in reality, these approaches underperform in those settings!


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