Demand forecasting and replenishment
See demand coming, stock for it, explain every call
A forecasting pipeline that combines your sales history, promotions, and external signals into demand forecasts per item and location, then drafts the replenishment plan. Planners review the exceptions and the reasoning instead of rebuilding spreadsheets each week.
The problem
Demand planning runs on spreadsheets and gut feel. Forecasts are stale by the time they ship, stockouts and overstock both eat margin, and nobody can explain why a number moved. The signals that actually drive demand, like promotions, seasonality, and weather, live in places the forecast never sees.
The outcome
stockouts and less overstock
How the work actually flows
No black box. This is the real data and agent flow, from the moment work arrives to the moment it is done, with a human in the loop wherever the stakes are high.
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Ingest
Pull sales history, inventory, promotions, and external signals like seasonality and weather into one store.
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Model
Train per-item, per-location demand models and ensemble them against simple, reliable baselines.
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Forecast
Generate forecasts with confidence ranges, and explain the drivers behind each prediction.
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Plan
Translate forecasts into replenishment and safety-stock recommendations against your service-level targets.
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Review
Surface the exceptions and large swings to a planner with the reasoning attached for a quick decision.
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Monitor
Track forecast accuracy and stockout rate, and retrain as new patterns appear.
What we build
- A unified store for sales, inventory, promotions, and external signals
- Per-item, per-location demand models ensembled against baselines
- Explainable forecasts with confidence ranges and driver breakdowns
- Replenishment and safety-stock recommendations tied to service levels
- An accuracy dashboard with retraining as patterns shift
Representative stack
We choose tools to fit the job and your constraints. We are not tied to any one vendor.
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Let's adapt this blueprint to your systems
Take the assessment. We start from this reference and tune it to your data, your tools, and your bar for quality.