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Applying tools from quality, operations, and analytics to guide engineers in making informed decisions and achieving impactful results.

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In today's world, we are encouraged to specialize, specialize, specialize - high school students are encouraged to pick a career path in their teens, children train from a young age in sports with an eye toward being a professional player, and software engineers are expected to have a horde of related personal projects to demonstrate at job interviews.


The research, though, is clear: generalists are more successful innovators.


For a rigorous analysis of such a wild claim, I recommend David Epstein's book Range: Why Generalists Triumph in a Specialized World. To (very) briefly summarize, innovation requires creativity with a heavy dose of analogical thinking, or translating knowledge from one area to another. Being able to make these connections requires a diversity of knowledge and experience that can only be gained from in-depth (and unrelated!) experience. Epstein shares a lot of really counter-intuitive results about relative successes between generalists and specialists in everything from sports, to the fine arts, to STEM.


At the least, I'd like to share a bit about a related study most familiar to those of us in STEM, one performed by psychologist Kevin Dunbar titled "How scientists think: on-line creativity and conceptual change in science." For this study, Dunbar spectated the weekly group meetings of four microbiology labs at a university and studied how these groups solved challenging problems. Overwhelmingly, all participants (students, postdocs, and professors alike) solved problems by applying analogies - relating the problem to experiments they had done prior, work they had heard of, or simple thought experiments using everyday items. "The more unusual the challenge, the more distant the analogies, moving away from surface similarities and toward deep structural similarities," Epstein notes about Dunbar's study.


The labs with scientists of more academically diverse backgrounds (chemistry, physics, biology, medicine) offered more frequent and more useful analogies, and these labs solved problems faster and more efficiently. As a spectator, Dunbar even got to see two labs combat the exact same experimental issue during his study, with the more diverse lab solving the issue much more rapidly.


When we solve problems, especially in innovative areas, we necessarily need to draw from unrelated experience because the thing we want to make doesn't exist yet. The more "unrelated" knowledge we have to draw from, the more agile we become as engineers, and the more creative we become as problem-solvers.


In general, I think there are two takeaways here:

  • Surround yourself with people of different backgrounds, no matter how unrelated their experience might be to the problem you're trying to solve.

  • Don't hesitate to not just dip your toes in other areas of study, but to jump right in!



 
 
 
  • Writer: Sam
    Sam
  • Jan 7
  • 2 min read

A SWOT analysis is a way to assess internal/external factors when making strategic decisions about business directions. In brief, this is a way to explicitly state the strengths, weaknesses, opportunities, and threats for a company to determine the best way to move forward. More here.


The takeaway: this mindset can be applied to literally everything.


Here's a recent example: I recently held our company's first (ever!) project kick-off meeting.

I put oodles of thought into the project scope, timeline, stage-gating, background, and responsibilities... and the meeting was a disaster. Everyone had ideas that, to me, didn't fit into my idea of how the project should run. I couldn't just copy the "agile project management" structure I had learned and paste it onto our company. A SWOT analysis of our team reveals why:


  • Our team's strength is our enthusiasm for solving problems and having expertise in a variety of fields.

  • Our team's weakness is a lack of industry experience - 75% of us have never held a full-time industry position before joining.

  • Our team has an opportunity to completely redesign the way that we approach projects.

  • Our main threat is projects with a high ideas-to-progress ratio - spending too long in the planning phase instead of making meaningful progress.


Now it was clear that the goal for this first (formalized) project in our company wasn't the same as the project goal - it was to develop a project management structure that amplified our strengths (that everyone has ideas!) while supplementing our weakness. Now we're exploring a radically different kind of project management structure that integrates structured brainstorming and dynamic timelines to focus our ideas and push the project forward.


Even if you're not an executive or business strategist, the SWOT mindset can be mapped onto an infinite number of questions, like

  • How do we determine relative priority between two projects?

  • Should we develop a process ourselves, or outsource it?

  • Is this design change going to benefit our company in the long term?











 
 
 
  • Writer: Sam
    Sam
  • Dec 30, 2024
  • 1 min read

Okay, so I'm a few days late for the joke.


The MECE tree is a favorite of consultants, and is also a great tool for making decisions as an engineer. A MECE tree is simply a decision tree that is Mutually Exclusive and Completely Exhaustive.


Let's look at an example:

The first step to solving the issue is to list broad categories of solutions - there should be no possible solution that isn't covered by these categories (mutually exclusive), or that fits into multiple categories (completely exhaustive).


Then, for each broad category of solutions, narrow it down further. These should be concrete action items. At the end, analyze each action item for its feasibility.


This is one of those tools that seems obvious when looking at an electric bill that's too high, but for complex problems this can require a lot of brainpower. I've found this to be a fun team activity - throwing out random solutions to problems too often results in missing one (or two, or ten). This is a tested way to structure problem-solving, enjoy!



 
 
 

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