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This was my thoughts exactly. More often than not, these systems are still implemented but a manual "exception" process is later tacked onto it. It sounds lik

This was my thoughts exactly. More often than not, these systems are still implemented but a manual "exception" process is later tacked onto it. It sounds like they haven't hit the normal Stage 2 phase: Implement a manual override process with the goal of allowing employees to temporarily bump levels for holidays and special sales and the like. The key here is temporary. The computer resets the levels to whatever it thinks they should be after a normally (short) period of time. Stage 3 is to act surprised (as a corporate culture) when it is discovered that salaried managers are spending a 10 hour day every week/month adjusting all the levels. Effectively, store-level employees have returned to the original process but in a very inefficient and undesirable way. Stage 4 varies. Sometimes you loop back to Stage 1: Zealous enforcement and the removal of any way for store-level employees to provide input into the ordering system. Sometimes, you scrap the whole thing and go back to manual ordering. This usually keeps the algorithmic system but instead as a sort of assistant/recommendation engine. Usually it picks the top X overstocked/spoiled items in the store and gently nudges management to reduce inventory levels. This usually turns into a consistent win because management feels empowered and eventually comes to trust the recommendations, often rubber stamping most things and only correcting egregious errors: "I.E. Christmas in 2 weeks, only order 10 hams." Unfortunately, the source of this info is directly working with a retailer trying to integrate one of these systems. After we hit Stage 3, I really did try to build a consensus around repurposing it into a weekly recommendation engine of 10-20 items to adjust in the store. Instead we just removed the override capabilities and went right back into trouble. Apparently, there was a view I had overstepped my boundaries trying to tell the business how to treat the stores. So I left before I could get placed at the bottom of the Annual Stack and Rack. :-/

pbhjpbhj 9y ago
Surely they have enough data at a long lived supermarket to predict stock demand using weather factors and seasonality automatically. I'd expect them to be factoring in customer onboarding rates, including anticipated accelerations, accounting for ongoing ad campaigns and such too.
- drtillberg 9y ago
  Whole Foods whittled down the list of items that are "never" out-of-stock. Now that tier of redundancy is reserved for a select group of core items explicitly called "never-outs." In doing so, HQ implicitly reclassified the remainder of the inventory as "sometimes-outs." Having a lot of "sometimes-out" items predictably will lead to "sometimes-all-out-at-the-same-time" categories and "some-stores-are-always-out" items. The outages are clumping, in terms of categories, timing, and location. Which is a bad and apparently unanticipated mode of failure for the procurement system.

pbhjpbhj 9y ago

Surely they have enough data at a long lived supermarket to predict stock demand using weather factors and seasonality automatically. I'd expect them to be factoring in customer onboarding rates, including anticipated accelerations, accounting for ongoing ad campaigns and such too.

drtillberg 9y ago
Whole Foods whittled down the list of items that are "never" out-of-stock. Now that tier of redundancy is reserved for a select group of core items explicitly called "never-outs." In doing so, HQ implicitly reclassified the remainder of the inventory as "sometimes-outs." Having a lot of "sometimes-out" items predictably will lead to "sometimes-all-out-at-the-same-time" categories and "some-stores-are-always-out" items. The outages are clumping, in terms of categories, timing, and location. Which is a bad and apparently unanticipated mode of failure for the procurement system.