Improve Your Engineering Team With Better Methods
Are all of your engineers using AI? Should they?
I have been using Chat GPT and Bard since January 2023 and find it fantastic for some things, good for others, and terrible for some. For example I am writing this page myself because chatbots have a tone different than mine and are intended for the general public rather than people like you. I have also been using Github copilot for short programs and find it is occasionally much better than me, and sometimes surprisingly bad. Overall it has made me 2-3x better at short code I can do faster than I can hire an expert and explain what I want.
When I look at specific issues manufacturing startups struggle with it often seems more about working as a cross functional team than lack of specific tools like AI.
A classic team solving approach is Six Sigma. This peaked years ago and GE has essentially proven it's obsolete. At the time mentors told me it was repackaged TQM (Total Quality Management). Lean manufacturing concepts started around the same time and has done better since. All of these and more overlap each other and new tools like AI may help or be a distraction.
Does your team have the shared language and format that comes from everyone approaching problems with proven methods? Does every report you see look different, confusing, and even after more explanation its not clear your team is making the best decision? Are problems taking far longer to solve than expected? Is a dominate person dictating paths that may not be data driven? Are some people from a specific company or industry that have a shared way of doing things that miss out on your agility and new possibilities?
You have a world of options to improve this! I don't recommend any one of these (or others I could mention) because I don't know your situation and don't think any one approach is right for everyone.
Does this look like your organization?
Your product is unique, your market is challenging, and your greatest competition may be the status quo. In order to make a complex product you either need to buy or make everything it needs. Even the parts you buy need people to figure out the best way to assemble them and interface with everything.
The small team that proved the concept needs to grow to a large cross functional team to bring to market
4 Engineering Team Challenges
a Tech Startup Must Overcome to Scale
1. The team that invented your technology are similar and worked closely for a long time. To scale you need many other skill sets and people from other disciplines, industries, and companies. Those have different cultures, practices, terminology, and assumptions that add friction even when things are going smoothly.
2. Many best practices, such as lean, have been made almost ceremonial in the largest legacy companies. They do them in a time consuming, burocratic way that don't get the benefits intended. Many startups feel it is best to skip them and miss the benefits.
3. Startups lack much of the infrastructure that evolved within large companies. Growing this from nothing is time consuming, but a copy and paste from your legacy companies miss the opportunity to do better and make the most of being modern and nimble.
4. Keeping leadership informed and other documentation is a challenge. You don't want everyone to write a white paper about every problem, and no one has time to read them anyway. But email chains, and random quick updates are hard convey the scale of issues and are hard to refer back to later when needed.
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Provided by Jay Lewis
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Over 20 years of experience bringing complex technology products from concept to full production. Have worked closely with software engineers, operations managers, systems engineers, hardware engineers, manufacturing engineers, data scientists, program managing, and startup investing. Industries include space, aviation, vehicles, medical devices and alternative energy.
I can handle up to 3 clients at a time and have 1 right now (3Q 2023).