Machine learning-powered analytics platform that improved demand forecasting accuracy by 85% and reduced inventory costs by 35%.
The Challenge
A supply chain company struggled with demand forecasting, leading to overstocking, stockouts, and high inventory costs. Traditional forecasting methods couldn't account for seasonality, trends, and external factors.
Our Solution
We developed a machine learning-powered predictive analytics platform that:
- Analyzed historical sales data and external factors
- Built predictive models using machine learning algorithms
- Provided demand forecasts with confidence intervals
- Integrated with inventory management systems
- Offered scenario planning and what-if analysis
- Delivered real-time predictions via API
Results
The predictive analytics platform delivered significant improvements:
- 85% improvement in forecasting accuracy compared to traditional methods
- 35% reduction in inventory costs through better demand planning
- 10x faster predictions through automated ML models
- Reduced stockouts and overstock situations
- Better resource allocation and planning
Key Technologies
Built with Python for data processing, Machine Learning models with TensorFlow, data manipulation using Pandas, and deployed on Azure ML for scalable predictions.
