Enhancing Sales Forecasting Accuracy for Improved Product Allocation in Retail

The client experiences difficulties in accurately predicting sales of new products during the initial weeks after launch. This hampers effective product allocation, leading to overstocking or stockouts, which increase costs and diminish customer satisfaction.

A large-scale global retailer with extensive store presence seeking to optimize inventory and product distribution through advanced sales forecasting.

• Develop a robust sales forecasting system that predicts product demand within the first few weeks of launch with high precision.
• Achieve at least a 50% improvement in forecast accuracy compared to existing models.
• Reduce the product deficit rate to below 5%, minimizing stockouts and excess inventory.
• Enhance inventory allocation strategies by incorporating trend analysis and historical sales data to optimize stock levels across different product variants, colors, and sizes.

• A data pipeline that extracts, transforms, and loads sales and product data from various sources into an analytical environment.
• A machine learning model that compares new items to similar past products based on attributes like color, size, and category, incorporating trend data for increased accuracy.
• Experiment tracking and versioning capability to refine forecasting models, using tools that log experiments, results, and configurations.
• An internal analytics dashboard for visualization of forecast results, model performance metrics, and inventory recommendations.
• Inclusion of sales trend data within product subgroups to improve supply predictions for specific variants.
• Automated alerts and reporting functionalities for inventory managers to act on demand forecasts.

• Existing sales databases and POS systems for real-time data ingestion
• External trend and fashion data sources to incorporate current market trends into forecasting models
• Internal inventory management and supply chain systems for seamless data flow and decision support

• System scalability to handle increasing data volumes and user load, supporting thousands of store data points
• High model performance with forecast accuracy improvement of over 50%
• Real-time or near-real-time data processing capabilities to inform prompt decision-making
• Secure data handling to protect sensitive sales and inventory data
• Reproducibility of experiment results to enable continuous model improvement

Implementation of the enhanced forecasting system is expected to significantly optimize inventory management, reducing stockouts and overstocks, and leading to cost savings. Targeted improvements include over 50% increase in forecast accuracy and lowering product deficit rates to below 5%, resulting in improved customer satisfaction and revenue growth.