The model that reads your supply chain before it breaks.
Three-signal architecture. Planner-readable outputs. No black box.
Three inputs. Each one your ERP ignores.
POS Velocity
We ingest hourly sell-through from your existing POS system. The model learns SKU-level demand curves from your actual transaction history — not industry averages, not category benchmarks. Your data.
Weather Patterns
Local 7-14 day weather forecasts mapped to your store locations at ZIP code level. The model learns which SKUs in your catalog are weather-elastic — so a cold front in Seattle triggers the right reorder, not a generic seasonal bump.
Lead Time Variance
Live lead-time variance from your supplier confirmations. When a supplier slips from a 14-day lead time to 21 days, reorder triggers for dependent SKUs adjust automatically — no manual spreadsheet update required.
How the model votes on each SKU
Automcore uses an ensemble approach that blends three model families: a gradient-boosted regression layer trained on your POS history, an ARIMA-based seasonality component calibrated to your SKU categories, and a rule-based override layer that applies hard constraints from your lead-time feed.
The three models vote on each SKU’s forecast. When models agree, confidence intervals narrow. When models diverge — typically on high-velocity SKUs during unusual weather events — the confidence interval widens, flagging that prediction for human planner review.
Recency weighting means the last 30 days of POS data carry more influence than the same period from a year ago. This makes the model responsive to emerging trend shifts without ignoring long-run seasonality.
The model retrains incrementally every 24 hours as new POS data arrives. Major retrains (full model weight refresh) run weekly. You always see which model version produced each forecast.
Outputs: what your planner sees
| SKU / Product | Location | 6-wk Forecast | Confidence | Signal |
|---|---|---|---|---|
| Rain Jacket M | Seattle NW | +34% demand | High — 88% | Order Now |
| Fleece Pullover L | Phoenix SW | −12% demand | Med — 71% | Overstock |
| Base Layer S | All | Stable ±3% | High — 92% | On Track |
| Puffer Vest XL | Chicago MW | +18% demand | Low — 54% | Review |
Forecast accuracy benchmarks
Average MAPE reduction from baseline forecast on new customers, measured over first 90 days
6-week horizon accuracy on 80% of tracked SKUs — within planning tolerance for most reorder decisions
Supplier delay capture rate — delays flagged 3+ weeks ahead on 94% of confirmed late shipments
Benchmarks measured over first 90 days across retail and manufacturing accounts. Results vary by SKU count, POS data quality, and seasonal complexity. We report actuals — not cherry-picked best-case outcomes.
See it run on your SKU data.
Request access and we’ll run a live demo on a sample of your real inventory data within 48 hours.