Most forecasting projects optimise a symmetric error metric, then are judged on outcomes that are wildly asymmetric. A stockout on a hero line and an overstock on a slow one are not the same size of mistake, and the model should know that.
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A forecast trained to minimise mean absolute error treats a unit of over-forecast and a unit of under-forecast as equally bad. In a real retail business they almost never are. Understocking a high-margin, high-demand line costs the margin and sometimes the customer. Overstocking a perishable or seasonal line costs the markdown and the working capital. The ratio between those differs by category, by season and by product lifecycle stage.
Building that asymmetry into the objective — forecasting a quantile rather than a mean, with the quantile chosen per category from the actual cost ratio — in our experience typically produces a larger commercial improvement than any change of model architecture. It is also the step most often skipped, because it requires a conversation with finance and merchandising rather than with data science.
The second most valuable step is equally unglamorous: modelling promotions and price properly. A forecast that treats a promotional week as an outlier to be smoothed is discarding the signal that matters most for planning.
Not forecast accuracy in isolation. Accuracy is an input; the outputs that matter are service level, markdown, stockout rate and working capital tied up in inventory. A forecast that is marginally less accurate but biased in the commercially cheaper direction is a better forecast for the business, and reporting only error metrics conceals that.
We also measure the override rate — how often planners overrule the system, and whether they were right when they did. Persistent, correct overrides in one category are the clearest possible signal of a missing feature, and they usually point at something the planners know and the data does not record.
sales · price · promotion
attributes from unstructured data
chosen from the cost ratio
inventory and replenishment
override recorded with reason
the missing-feature signal
They are a real and improving research area and worth benchmarking, particularly for cold-start. As a wholesale replacement for a well-tuned classical forecaster on a business with history, we have not yet seen the case made on real retail data — so we would benchmark rather than assume, in either direction.
Enough to see the seasonality you care about, which usually means at least two cycles. Less than that and you are relying on cross-product signal, which is exactly where the semantic similarity work earns its place.
It can detect that the regime has changed and flag it. It cannot forecast an unprecedented event, and a system presented as able to is misleading. What good design gives you is fast detection and a clear handover to human judgement, not prescience.
The quantile you forecast is a commercial decision. It is usually made by accident.
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Apps, platforms and campaigns for these teams, long before the AI work. Every one is written up on the work page — what we did, and where our part ended.
