This was 10 years ago now, so sort of like Amazon Prime before it became ubiquitous, but for materials and tools. However McMaster was and remains much better organized and much better spec-ed.
I would like to know if a McMaster-Carr, Grainger, et al had fallen into the same traps Amazon has when it comes to supply chain and if eventually Amazon will be shaped into a similar company & business model.
Amazon knows that retail margins are tiny, a few percent at best, and that is not what they are interested in. It takes a lot of labor to provide high quality vetting and constant vigilance over suppliers. What they are interested in is high margins, which comes from being a platform.
I don't think McMaster Carr or Grainger ever had any intention of becoming platforms for resellers so they could take a top line cut of sales and outsource quality control.
If anything, I think Amazon is probably trying to reduce their shipped and sold by Amazon.com retail operations and focus on the high margin web services. Why compete with Walmart/Target/Best Buy/Home Depot/Lowes for <5% profit margin with huge liabilities when you can make 20%+ easy on super scalable web services?
What's interesting is that considering the existing logistics, none of these guys though of expanding into other ecommerce earlier. One of them could have been Amazon...
For example, let's say I want a piece of hollow metal cylinder (any metal, to be determined later based on cost and availability), with an ID of around 12 mm and a wall thickness of at least 5 mm, at least 150 mm long. It ends up being a needlessly frustrating experience even for such a simple item.
Getting things custom made is expensive, so customers are pressured to use what's widely available. Stocking lots of things not widely purchased is also expensive. These forces have been working for a long time to give us a pretty wide selection that covers most uses.
Examples: Oil filters are often differently sized but still interchangeable between models. You can use an Aisin-built airflow meter from a 90s Mazda to replace one in a Toyota, even though they're different housings, and it would work (if not perfectly) because it's essentially the same part with the same electronics. Brakes and suspension parts often interchange across many models, with the possibility of 'upgrading' to heavier duty parts from more expensive models.
Anyway, it'd be great if you could combine a lot of data and NLP to search off-the-shelf parts based on parameters of varying specificity. Anything from "made of metal and roughly x/y/z dimensions" to "shares the same bolt pattern as part # on a joining surface" could be made searchable in theory.
When I tried to re-order through my personal company (not in any way cannabis-related) I was told I'm not a big enough company for them to deal with (and they cancelled the order).
From a CAD/dimensional standpoint, their website is a goldmine. I just wish they would take my money.