Very interesting results. The reduction in time and cost is definitely worth checking out in more detail.

IMO, this is an important consideration. Sure, the target level is SKU-store, but at what level are the purchase orders being made? The M5 Competition didn't say anything about this, but probably the SKU level is as important as the SKU-store, if not more.

For retail data in general, I think we need to see how well a method perfoms at different levels of the hierarchy. I've seen commercial and finance teams prefer a forecast that is more accurate at the top than another that is slightly more accurate at the bottom.

The answer depends on several factors: how many time series you need to forecast? what resources you have available? how much time do you have? what are the business requirements?

I would add to your list any of Nixtla's libraries: statsforecast, neuralforecast, or mlforecast. If your data has a hierarchical structure, you can also try their hierarchical forecast. I've used the first two and they work well, are easy to implement, and relatively fast. They also provide a lot of user support.

Above all, just don't use Meta's Prophet, unless all you care about are nice looking plots.