Lean Startup in the industrial world
We design tomorrow is a series of Deep Tech exploration programs that start early 2018. We will release more informations but feel free to reach out to us if interested.
How to apply the Lean Startup framework in the context of heavy industries with much longer lifecycles and security constraints?
Some background first:
Lean Startup in a nutshell: learn quickly with a frugal innovation approach
Lean Startup focuses on quick learning through field experimentation, so your product matches user expectations as much as possible.
Work is done in short iteration cycles made of the following phases:
- Build: “Prototype” main product features
- Test: Go out, experiment with your “prototype” and gather data
- Learn: Collect information that will help you improve your product and build new “prototypes” to be tested
One of the core concept of Lean Startup is building Minimum Viable Product (MVP): the smallest product or service you can learn from; everything else is unnecessary.
So how does this all translate in the industrial world?
At the macro level:
- Manage your innovation project at the feature scale, in order to adapt your knowledge acquisition goals (breakthrough vs Incremental innovation)
- Identify the level of criticality of each your innovative project features and decide if it’s a go/no go on a lean startup approach
- A culture of an organisation only takes 10% of employees onboard, to switch: learn early, learn cheap, learn fast, to minimise risks of failures coming with every innovation project
For each phase of the cycle:
Learn efficiently
Know if you are in an incremental innovation or breakthrough innovation context.
Here is some extra insight on how to learn quickly: make sure you know if you are in an incremental or breakthrough innovation context.
What should be tested at each stage?
We have developed a model to identify key learnings at each critical stage of your projects. Give it a go!
Don’t stop at a Minimum Viable Product. Build the Minimum Viable Ecosystem
Minimum Viable Ecosystems are the optimal ecosystems of stakeholders in which to deploy your innovative projects at each stage. Those MVEs ensure the right set of knowledge, skills and capabilities. MVEs evolve through continuous learning on stakeholders adoption, habit transformation, as knowledge, skills, capabilities are re-assessed for projects deployment.
Introduction to the WDS Value flow exercise — A proprietary methodology developed by WDS part of a training with EDF to define your Minimum Viable Ecosystem at each stage by exploring value creation, and mapping value flows
To go further…
How to maximise knowledge creation and efficiency of breakthrough and incremental innovation projects?
Did you know that for years, Tesla, hacked Lotus Vehicles into electric vehicles, learning from customer adoption?
At the time they already had a waiting list for those modified vehicles but at the same time a strong learning strategy. Based on the usage of the battery and acquired data, they could later transfer that data to their partner Panasonic. They have kept this culture of ongoing measurement, learning and improvement…
Early in Tesla’s history, the company had solely focused on developing core battery technology, then later, the time came to think about how to approach building a fully integrated electric car.
How to explore value creation and capture strategies of your innovation projects and develop your minimum viable ecosystem, the optimum ecosystem for deployment ?
Companies historically optimized their “dominant design” for efficiency and risk reduction in every aspect of such core activities. They made sure their ecosystem of stakeholders were organized in the best way to deliver core business activities associated with the dominant design(s) of their offerings. Any innovation project will destabilise part of the internal and external ecosystem and adopting a minimum viable ecosystem strategy can help you maximise learning. When designing your ecosystem, (re) define the roles and strategic weight of stakeholders in existing ecosystems; trigger ecosystem mutations leading to new stakeholders and flows; include new stakeholders and new knowledge.
It will help foresee what we call their minimum viable ecosystem, the optimum ecosystem of value depicting existing and new stakeholders new importance and roles as well as new activities.
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