Consistent accuracy gains
High precision
Retail and SKU-level impact
Reduced forecast errors
The challenge
Enhancing forecast accuracy in a saturated market
A Fortune 100 CPG company faced month-on-month fluctuations in tea shipment forecast accuracy--23% to 46% at the retailer level, 45% to 71% at the SKU level.
Machine learning was needed to capture market dynamics for better forecasting. The company aimed to address missing data—e.g. point-of-sale and promotional inputs, and inconsistent shipment patterns at the retailer level for certain SKUs.
Key challenges
Required accurate forecasts for better promotions and supply chain
Large month-on-month variations at retailer and SKU level
Need for clear shipment patterns at the retailer-SKU level
Need for critical inputs like sales and promotional data
The solution
Optimized forecasting with AI
Feature engineering
Identified 21 predictors
Analyzed promo impact
Derived 199 features
Feature optimization
Refined features with ‘greedy’ method
Key factors: EPOS, date, week
Tuned for SKU accuracy
Implementation approach
1
Model development
Combined with a neural network
Optimized accuracy and bias
Built retailer and SKU models
2
Ensembling and tuning
Integrated models for precision
Used dual-objective function
Adapted to trends
3
Deployment and integration
Embedded into forecasts
Enabled improvements
Delivered insights
The impact
Driving forecast accuracy with AI-powered insights
Accuracy boost
Exceeded demand forecasts
>60% accuracy
Consistent gains
Precision gains
6% retailer-level accuracy
Reduced bias and errors
+5% SKU-level accuracy
Business impact
Improved demand planning
Data-driven decisions
Minimized errors
Looking ahead
Scaling AI adoption
Expand AI-driven forecasting to other product categories
Enhancing model precision
Refine algorithms for greater accuracy
Grow supply chain efficiency
Use insights to optimize inventory and reduce waste
