AI-Powered Demand Forecasting Platform
Built an intelligent demand forecasting system using machine learning algorithms to predict product demand with 95% accuracy, reducing inventory costs by 30%.
Task
Build an intelligent demand forecasting system using machine learning to predict product demand with high accuracy and optimize inventory management across retail operations.
Technologies
Python, TensorFlow, PyTorch, LSTM Neural Networks, Lambda, S3, RDS, Kinesis, Docker, Kubernetes, Dagster, Grafana, CloudWatch.
Result
Achieved 95% prediction accuracy, reduced inventory costs by 30%, increased sales by 15%, and automated 80% of forecasting processes within 6 months.
In today’s competitive retail landscape, a major multinational chain with over 2,000 stores and a catalog of 50,000+ SKUs faced severe inventory management challenges. Traditional forecasting methods, based only on historical sales data, failed to capture key demand drivers such as seasonality, weather, economic shifts, competitor actions, and social media trends.
The result was costly inefficiencies: overstocking that led to heavy markdowns, understocking that caused frequent stockouts and lost sales, and millions in wasted capital tied up in slow-moving inventory. Customer satisfaction dropped as empty shelves became common, while warehouse space filled with excess goods.
Meanwhile, the retailer’s forecasting team spent 80% of their time on manual data processing instead of proactive planning, leaving them unable to keep pace with modern market complexity.
Data Security & Privacy
Implement enterprise-grade security protocols to protect sensitive sales data and customer information throughout the AI pipeline
Scalable Architecture
Design cloud-native infrastructure that automatically scales to process millions of data points and generate forecasts for thousands of products
Adaptive Models
Create flexible machine learning models that adapt to changing market conditions and can incorporate new data sources seamlessly
Automated Forecasting
Eliminate manual processes by automating data ingestion, model training, prediction generation, and inventory recommendations
Cost Optimization
Reduce inventory holding costs and stockout losses through precise demand predictions and optimized safety stock levels
Cross-team Integration
Enable seamless collaboration between merchandising, supply chain, and finance teams through shared AI-driven insights
Intuitive Dashboard
Provide user-friendly interfaces that make complex AI predictions accessible to non-technical business users
Rapid Adaptation
Quickly respond to market changes with real-time model updates and dynamic forecasting adjustments
Prediction Accuracy
Continuously monitor and improve model performance to maintain high accuracy and reliability in demand forecasts