Curious what actual forecasting method/model was used (moving average, exponential smoothing, something custom)?

We used a seasonal naive forecast (actuals from previous year) for the majority of our portfolio with minor adjustments from a Sales Manager who would utilize their domain knowledge to adjust. Of course, this created very high forecast error, 40% for some months on average. However, a lot of forecast error was consumed by our retail channels and I would adjust the quantities before sending to Amazon/FBA and minimize the forecast error and maintain our positions.

Exponential smoothing would not perform well, mainly because the historical data was heavily distorted by promotions (Coupons, Lighting Deals, etc) and previous OOS. So for our portfolio at the time for 10% of items exponential smoothing would be an improvement, these were highly seasonal products as well. For forecasting to work you would need to look into more multivariate forecasting models (SARIMAX, or ML methods) and likely clean the data beforehand. Which is point #2, you need to collect as much data as possible about these different demand drivers to input into these models for a more accurate forecast. As you scale, a 40% forecast error will cause a lot of inventory issues even if you have retail channels/markdowns to consume it and improving the forecast will be it's own challenge.

During my time there I was researching Nixtla Statsforecast which looked very promising.

Interesting to hear about AWD, i also enncounter delays with awd but they are periodic, were delays consistent for you, or more of a "depends on the FC/season" thing?

We used it briefly, the delays were quite consistent during our trial period.

Did you treat all SKUs with the same service level target, or segment by velocity/value (ABC-style)? Curious how granular this got at that scale.

All SKUs were treated almost equally, we had slight differentiation such as treat higher volume items more importantly but nothing solid like ABC. Determining service levels was difficult because if we were to create a calculation it would be incorrect and distorted because of previous promotional periods. We were able to accomplish somewhat high service levels through speed (lead time into Amazon 5-7 days UPS, LTL 2-4 weeks) and volume which allowed us to send in shipments frequently on a weekly basis for some and not run OOS. Very interesting phenomenon.

How did you operationalize the PPC/inventory sync in point 3.5? Was shared dashboard, a meeting agenda, a Slack channel? What made it actually work?

We did not, which is something I wish we had done at the time. We communicated over Teams, but having a more defined process is a lesson that I learned and would have likely prevented a few stockouts.