ERP/SCM pattern: SKU forecasting + stock optimisation for food manufacturing

A proven pattern for ERP and supply-chain platforms: use Forecasting.ai to generate SKU-level demand forecasts (with exogenous drivers) and drive stock holding, purchasing, and production decisions — reducing waste and improving availability.

At a glance

End-customer: European food producer (anonymous)
Best for SaaS vendors in: ERP, Supply Chain Planning, Inventory & Replenishment
Engines/pattern: Forecasting.ai + stock optimisation workflow
Forecast cadence: Weekly refresh of 3-week and 12-week forecasts
Data inputs: orders, promotions, weather, holidays (+ other contextual drivers)
Results: 50% reduction in year-end inventory; 62% decrease in waste; 62.5 hours/week saved

Partner note: the same engine-led workflow can be embedded by ERP / supply chain vendors.

The product challenge

Food manufacturing adds complexity: perishable inventory, promotions, weather impacts, and production constraints. The business challenge was to forecast demand reliably enough to reduce overproduction and waste while maintaining service levels.

The engine-powered solution

Forecasting.ai produced weekly SKU-level forecasts using historical orders and exogenous drivers (promotions, weather, holidays).
Those forecasts were used to inform stock holding, production scheduling, and raw material ordering, enabling earlier and better purchasing decisions.
Outputs were delivered through the customer’s existing BI environment — demonstrating a common partner approach: engine outputs → partner data model → dashboards/actions.

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Implementation & productisation notes

Start with SKU-level weekly forecasting (fastest path to measurable value).
Support multiple horizons: near-term (3-week) for operational planning and mid-term (12-week) for purchasing/production planning.
Integrate into existing BI first, then progress to in-product actions (alerts, reorder suggestions, approval workflows).

Outcomes

50% reduction in year-end inventory levels
62% decrease in stock waste
62.5 staff hours/week saved via automation
0.02% lower cost of sale through earlier, optimised ordering
0.16% increase in total sales from improved availability

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Partner playbook (what to copy)

Start with forecasts + visibility, then evolve into recommendations + approvals.
Make exogenous driver coverage visible (promotions/weather/holiday data gaps matter).
Sell CFO-friendly outcomes: inventory reduction, waste reduction, hours saved.

Partner context

ERP and supply chain products live or die by how well they handle demand variability, lead times, and stock risk. Partners frequently want to add AI-powered forecasting without rebuilding their data platform.

Quote

“When demand is volatile, manual forecasting can’t reliably account for promotions and seasonality. With SKU-level AI forecasts, purchasing and production aligned more closely to real demand — and inventory and waste reduced as a result.”

— Supply chain leadership (anonymous)

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TL;DR

Forecasting.ai powered a SKU-level forecasting and stock optimisation pattern that cut inventory by 50% and waste by 62% — a repeatable embed opportunity for ERP/SCM vendors.

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