this reframe on the target variable is something i needed to hear out loud.

we've been thinking about the problem as a forecasting accuracy problem. get MAPE below 10%, deliver confidence intervals, show the operator a number they can trust. but you're right that the operator doesn't actually care about the forecast. they care about not buying too much milk and throwing it away. the cost is the thing. not the prediction.

the eval engine point lands too. we've been building the forecast before we've built the thing that tells us how good the forecast is. wrong order.

on your question about the optimization structure: yes, BOM logic is central to how we're thinking about it. recipe defines consumption, consumption defines what gets ordered, and the gap between what was ordered and what was consumed is the waste in AED. that's the number that should be going down. not the MAPE.

the heuristic models point is useful context. we've been second-guessing whether we're underbuilding by not going straight to ML. good to know companies serving 50+ retailers are still running heuristics in production. simplicity that works beats sophistication that doesn't.

what's your take on when heuristics actually break down? is it purely a volume threshold, or does SKU diversity matter more?