ShelfCast: AI demand planner on AMD MI300X

Problem: every week a store decides how many of each product to order. Replenishment systems extrapolate recent sales and cannot see a holiday, a promotion or what the manager knows, so planners patch numbers by hand. The cost is empty shelves and waste. ShelfCast is an AI demand planner that turns a forecast into the right order. It (1) computes a statistical baseline with calibrated quantiles; (2) builds an evidence brief: past event and promotion effects measured against that baseline and kept only if they clear the product's own noise, price moves, a classical uplift model and any free-text planner note; (3) asks an open LLM (Qwen3-30B-A3B, vLLM on ROCm, one AMD Instinct MI300X) for five judgments in one request under a JSON schema, each naming its drivers; (4) guards, recalibrates and orders the cost-optimal quantile (newsvendor). The LLM is never the only forecasting method. Results from the full MI300X run (2,600 backtest forecasts, each made only from the past): - Real Walmart sales (M5, 60 products, 26 weeks): inventory cost -8.0% from ordering the cost-optimal quantile; 90.1% of outcomes inside the 90% range. On these anonymised items the model's net effect is within 0.6% of the baseline (its guard blocked 92 of 389 changes). We report this openly: it is why the LLM is a judgment layer, not the forecaster. - Planner notes (synthetic store): in weeks with a free-text note, 19.4% more accurate than the baseline, 17.6% more than a no-LLM rule with the same evidence, inventory cost -21.9%. AMD: 256 forecasts a minute on one MI300X (4,722 output tokens/s, 100% valid JSON), $0.19 per 1,000 forecasts. Open weights on the retailer's own GPU: sales data never leaves. Demo: yy5652-hash.github.io/shelfcast · Code (MIT): github.com/yy5652-hash/shelfcast