AI Demand Forecasting for Food & Beverage
Food manufacturer in Campania - 110 employees, 800 SKUs, national distribution
-68% (from 35% to 11%)
Average forecast error
-220,000 EUR/year
Expired product waste
-82%
Stock-outs on best-sellers
-75%
Emergency batch costs
+3.2 percentage points
Operating margin
The problem to solve
Demand forecasting was based on historical averages and sales team intuition. The average gap between forecast and actual sales was 35%, causing: 280,000 EUR/year in waste from expired products, recurring stock-outs on best-selling items, and unoptimized production costs due to emergency batches.
What we implemented
AI demand forecasting model that integrates: sales history by SKU/channel/area, seasonality, planned promotions, market trends, weather, and events. It generates weekly forecasts per SKU with confidence intervals, suggests optimized production plans, and anticipates raw material purchasing needs.
Measured impact
Average forecast error
-68% (from 35% to 11%)
Expired product waste
-220,000 EUR/year
Stock-outs on best-sellers
-82%
Emergency batch costs
-75%
Operating margin
+3.2 percentage points
Timeline
14 weeks (6 data engineering + 4 model + 2 integration + 2 tuning)
Investment
55,000 - 80,000 EUR (SME Digitalization grant up to 50%)
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