"Does Lean belong in the Life Sciences industry?" That's the question we here at SLLSA are asking.
Our last post on the series discussed Validated Learning, the concept Eric Ries introduces in his new book, The Lean Startup. The topic of this post, Build-Measure-Learn, is another major concept of lean. We would love to hear your thoughts on how this may or may not apply to life science companies.
Build-Measure-Learn is the feedback loop that generates Validated Learning. Its first step, Build, involves turning ideas into a Minimally Viable Product (MVP).
The MVP is that version of the product that enables a full turn of the Build-Measure-Learn loop with a minimum amount of effort and the least amount of development time. The minimum viable product lacks many features that may prove essential later on.
-The Lean Startup
The key here is to build the smallest version of your product that allows the management team to either discover a key component of their business model or test an assumption. The goal is to avoid spending a lot of time, effort, and resources only to later learn that 1) your customer doesn't recognize the problem you're trying to solve; 2) your customer isn't willing to pay for that solution; 3) your customer isn't willing to buy that solution from you; or 4) you aren't capable of building that solution.
From the last post on Validated Learning, we know that learning has to be validated which brings us to step two: Measure. For a MVP to be valuable, it must also include an appropriate mechanism to provide feedback to management. Remember, the purpose behind the MVP is to allow the management team to learn something new about their business. Therefore, an MVP not only includes the actual prototype but also procedures for collecting and measuring information as potential customers interact with the early prototype.
The final step, Learn, is the most critical step. It’s at this point, after management has collected data from potential customers, that the management team re-evaluates their strategic decisions. Does the data gathered support management’s original vision and/or assumptions? Or does the data encourage the management team to change strategic directions?
Ries uses the term ‘pivot’ to refer to situations in which the data gathered encourages the team to change directions. His book provides many detailed examples of successful companies who were forced to pivot based on the results of early experiments, suggesting that this is routine behavior for successful startups.
To recap, Build-Measure-Learn is the process by which entrepreneurs generate Validated Learning by focusing on building iterations of Minimally Viable Products (MVPs). The question at hand now is, "Does this apply to the Life Sciences?" What do you think? Share your thoughts below. We're eager to hear what our readers have to say.