APPLIED AI SOLUTIONS · DEMAND FORECASTING
Demand forecasting for inventory and planning
We build AI demand forecasting to support replenishment and planning decisions across products, locations and time horizons.
- The metric agreed before we build, at the level planning decisions are made
- Hierarchical reconciliation, so SKU, store, and region tell one demand story
- Automation on the routine forecast, planners on the volatile, high-value items
The failure is rarely the top-line number.
A total-company forecast is easy to get roughly right. The pain lives in the details a single average hides, and those details are where markdowns, expedited freight, and lost baskets are born.
Overstocked on the items that will not sell, out of stock on the ones that will. The same plan is wrong in two directions at once, and working capital sits in the wrong warehouse.
Cold starts
New products with no history, and seasonal items with too little of it.
Promotions and price
Price changes and promotions that break any model trained on normal.
Intermittent demand
The slow-moving long tail, where most of your SKUs actually live.
Incoherent numbers
Forecasts that look fine at region level but do not add up to what a store can order against.
There is no single best forecasting algorithm. The point is fit, not fashion.
We match method to the demand pattern, and often ensemble across them.
The right choice depends on data volume, seasonality, and how much explanation the business needs. A well-tuned gradient-boosted model usually beats a deep network starved of data, and we will tell you which one your data actually supports.
Forecast accuracy is not one number.
A claim of 97% accurate tells you nothing without knowing where it was measured. We agree the metric before we build, at the level where planning decisions are made.
And forecasts must be coherent. A model can be accurate per SKU and still produce numbers that do not sum to the regional plan. We apply hierarchical reconciliation across the SKU, store, and region grid, so the same demand story holds whether finance reads it top-down or a store reads it bottom-up.
No credible forecast is fully hands-off.
High-confidence, stable items flow straight into the plan. Volatile, high-value, or newly launched items, where the model's own uncertainty is wide, are flagged for a planner.
The planner adjusts with the model's reasoning and confidence interval in front of them. Every override becomes a labelled signal that sharpens the next cycle. Automation carries the routine forecast; people keep control where the stakes and the uncertainty are highest.
From a provable baseline to a forecast that lives in production.
Baseline diagnostic
Current forecast error measured in MAPE and WAPE against your data, so improvement is provable.
Feature and hierarchy design
Price, promotion, calendar, weather, and lifecycle signals, structured across your planning hierarchy.
Model build and reconciliation
Matched to your demand patterns, reconciled across SKU, store, and region.
Planner workflow
Forecasts, confidence intervals, and override capture landing inside your planning system, not a separate tool.
Monitoring and retraining
Accuracy tracked in production with drift-triggered retraining, run on our MLOps foundation.
Chosen for the demand pattern.
Built on the [Scaled GenAI and AI Platforms](/scaled-genai-and-ai-platforms) foundation and kept accurate in production through [MLOps](/mlops).
Part of our applied AI solutions portfolio.
Churn Prediction
A fuller view of customer demand, from what will sell to who will stay.
Scaled GenAI & AI Platforms
The platform this forecast runs on, from feature pipelines to serving.
Applied AI Solutions
The broader portfolio of generative, predictive and vision systems, each measured for its workflow.
What planners ask us first.
Cold-start items borrow from similar products through shared model structure and attributes, then correct as early sales arrive. Early forecasts carry wider uncertainty, which is exactly why those items route to a planner.
Only if they are ignored. We model promotion and price as explicit features, so the forecast anticipates the lift instead of being surprised by it.
Yes. Output lands in the tool your planners already use, with overrides captured there, so adoption does not depend on anyone learning a new screen.
Let us measure your current forecast error and show what a fit-for-purpose model would change.
Then a baseline worth adjusting instead of rebuilding, landing where your planners already work.