Scalable and flexible
Rapid deployment
Data-driven insights
Impactful optimization
The challenge
Adaptive modeling and system alignment
A leading global CPG company partnered with Fractal to develop an AI-powered demand forecasting engine for a single country. After its success, the client aimed to scale the platform across multiple countries, ensuring consistent, data-driven demand planning on a global scale.
Key challenges
Adapting the model for diverse markets and demand patterns
Handling multiple sources, formats, and inconsistencies
Tailoring for seasonality and consumer trends
Aligning with supply chain systems
The solution
Seamless data integration
Data processing and storage
Stored and processed data in Azure Blob and processed in SQL DB
Applied EDA, DI, DQ, and feature engineering
Saved forecasting outputs in SQL DB
Model execution and deployment
Built an R-based forecasting engine on Azure ML
Scaled across countries and categories
Extracted outputs as CSV for clients
Implementation approach
1
Data integration
Connected datasets to Azure
Streamlined data flow and formatting
Standardized inputs for consistency
2
Prototype development
Built and tested an R-based forecasting engine
Validated model accuracy
Optimized for scalability
3
Scaling and deployment
Automated data processing
Enabled seamless output transmission
Expanded across countries and categories
The impact
Driving scalable and intelligent forecasting
Scalability and efficiency
Rapid multi-country deployment
Automated, AI-driven forecasting
Reduced manual effort
Accuracy & insights
Enhanced forecasting precision
Real-time, data-driven planning
Improved market visibility
Business impact
Optimized inventory and resources
Minimized supply disruptions
Better strategic decisions
Looking ahead
Expansion
Extend forecasting to more markets and categories
Innovation
Enhance AI models for greater accuracy
Integration
Strengthen alignment with supply chain systems
