1) Inventory growth. Inventory turns have decreased every year (look at their public statements!), to the point where they are worse than brick and mortar retail. Some inventory growth is good...you need to buy more stuff to sell more stuff. But a healthy company would see increasing inventory turns over time, leveling off once they figure out what items are worth stocking and what are not.

2) Warehouse growth. The underlying assumption is that if you're closer to the customer, you save more money on transportation. That's true to some extent, but they fuck it up in other ways: fragmenting inventory resulting in split shipments (a way bigger deal than most people realize, I wouldn't be surprised if this alone overwhelmed the savings from shorter linehauls), increasing safety stocks, truck endpoint combinatorial explosions, truck scheduling issues, inventory balancing problems, reduced truckload utilization factors from reduced shipping volumes per warehouse and destiation. Larger warehouses have their problems, but compared to the issues that more warehouses cause, they are very solvable in comparison.

3) Random stow processes and path-based picking are hard wired into everything about their operations. From their software to their warehouse design to their metrics. It works phenomenally for books, but not so well for things that aren't physically shaped like books or have sales distributions like books. Inbound costs with random stow are orders of magnitude higher than case-level stow for case-packed items, experience exponential growth in costs when inventory shelves are at high levels of utilization, and random stow combined with path-based picking means your pick rates decrease almost linearly with inventory turns (more inventory per unit sold means walking farther between each pick which means more labor cost per pick, and prioritized placement to fix the pick cost problem just shifts costs towards a much more complex and costly stow process).

4) Refusal to reevaluate decisions made in the past that no longer apply. Two big examples are random stow processes, and inventory selection.

- Random stow vs other stow methods were evaluated back when Amazon was a bookstore, and they were an astounding win. Now that Amazon sells everything, there are plenty of methods that are vastly superior, and even the possibility of different methods for different products, but they've built out several hundred warehouses under the assumption of random stow and would require monumental efforts to retool them and write the software to support it.

- Inventory selection is seen as a universal good and is never going to stop growing. That's fine for a business growing into new sectors and categories, but when you have 3000 different brands and models of staplers, you've passed the point of marginal benefits exceeding marginal costs. The effect on inventory turns, warehouse growth, and warehouse operations alone is staggering.

5) Their Kiva robots work beautifully in small warehouses with low shipping volumes, but have severe scaling issues in larger warehouses due to inherent design problems. They have centralized path planning which is an NP-complete problem that can't keep up with the real time requirement at large warehouses. They require whole-warehouse shutdowns every time a single piece of inventory falls off a shelf. They experience physical deadlocks due to inventory being tied up in multiple conflicting processes at the same time. Sure, they eliminate picking labor, but they replace it with decreases in stow and packing productivity, and unseen to it all is that the fact that dependence on Kiva means small warehouses which means you further explode the logistics problem by having more warehouses and more fragmented inventory.

6) They introduced and scaled FBA without item-level inventory accounting. Now, due to co-mingling, fraud and piracy are a systemic problem. And it's not entirely solvable without item level accounting...which would require physical and technical changes to every step of the process all the way from the shipper to the warehouse(s) to the customer.

If I had to summarize Amazon's core problem, it is this[0]: they optimize the things that are easy to measure, because they don't know how to think about the things that are hard to measure. Warehouse labor is easy to measure...the effect of growing their warehouse network on logistics and inventory management is not. So they grow their warehouse network, see their costs increase, shrug their shoulders, and then double down on labor costs because that's the only thing they can understand.

[0] https://news.ycombinator.com/item?id=14920458