A VC-Backed CNC Machine Shop?
Back in March came news that Hadrian, a startup focused on disrupting the aerospace & defense supply chain, raised an incredible 90 million dollars in VC funding. Their pitch is that they can produce parts 10X faster and more efficiently due to their massive investments in automation. They have their sights set on the “thousands of mom-and-pop machine shops” that currently service this space. An estimated 3,000 of them generate $40 billion in revenue.
I have been doing mid-tier ERP consulting for the past 25 years via my companies, GingerHelp and SaberLogic, and that has given me the great fortune to work with many of these kinds of machine shops. And that experience gives me some skepticism that Hadrian is going to be able to rapidly move into this space. With per part requalifications, first articles, certifications, and processes approvals, it will be a grind for them to take that business. But at the same time, one has to acknowledge that VC money is a bit of a wildcard. If you read the rationale behind their investment from Andreessen Horowitz, they put Hadrian into the same class of transformational collaborations between hardware and software as Tesla and SpaceX. If you contemplate a well-funded startup among those peers, perhaps those “insurmountable barriers” are just a matter of thinking too small.
How Do I Compete With That?
As an ERP systems consultant, much of my work centered around how to make shop floor workflows more efficient. My co-founder comes from a private equity background, running a fund that invests heavily in aerospace and defense. Hadrian is not wrong regarding the significant problems that must be addressed sooner rather than later. The aging workforce of machinists, labor supply shortages, and the need to improve the security of our supply chain. These are all very real issues I am sure you are facing even today. If you do nothing, these challenges will just become more difficult.
Performing in the space requires a mix of both manufacturing execution along with many value-add services. Here lies where I believe existing machine shops have an incredible edge. You already have the people and processes that do this work. If your challenge is to improve your efficiency by multiples in order to survive, that is a lot easier and more cost-effective than starting from scratch. We lose efficiency in so many ways in manufacturing and often the largest sinkholes have nothing to do with how fast a spindle is spinning. I have had customers that have analyzed non-productive time and found that 25% went to labor time tracking into the ERP system. Others have discovered dramatic inefficiencies in the communications between operators and engineering.
When you analyze it, there is almost always some sort of low-hanging fruit that can be optimized and pay for itself quickly. Perhaps your investments are best made in optimizing administrative efforts. Perhaps the better investment is a pallet changer. The key here is that you have the edge because you have data right now to tell you where your inefficiencies are and that allows for cost-effective targeting of investments and efforts. Even with the latest equipment, a newcomer is going to have to work out many kinks in their processes before they will be where you are today. If you employ targeted optimizations now you will broaden your lead and, if you keep at it, the company that disrupts this space may be yours.
Think Like A Startup
Most successful startups share a key attribute – they collect as much data as possible and use it to optimize performance. An example would be the early shopping cart at Amazon, as described in this blog. There was a debate internally about showing recommendations at checkout, with a senior vice president deeply opposed to the idea. He believed it would deter shoppers from completing the checkout process. But instead of tossing out the idea, they ran an A/B test with audiences randomly served versions of the site with that feature. The results were clear, it didn’t deter checkouts and it increased revenue hence why we see that feature today.
You are almost certainly running hundreds of A/B tests today whether you recognize them like that or not. The combination of parts, revisions, shifts, machines, tools, and employees – each are small tests that can help you to identify the most efficient paths. But very few companies mine this data for value in the same way Amazon did with the shopping cart experiment. I believe all companies should strive for this sort of decision-making if they want to remain competitive. And it should not be a matter of randomly drilling down into jobs with poor margins from time to time. It needs to be a deliberate process of theorizing an experiment, implementing the means to collect the data, allowing the experiment to run, and making decisions based on statistical results.
Iterative improvements only work if you have the resolve the continuously reinvest your gains into the next improvement. What happens when Hadrian lands their next three customers? They get another massive VC cash infusion and continue to grow at a breakneck speed. This should be your motivation – they are on your heels. You need to continuously be improving to stay one step ahead.
Thank you for reading my post. In some of our next posts, we are going to describe specific analyses and how you can collect them. If you are looking for a more automated way to collect and analyze these key data points, check out Harmoni.
Until next time,