Annual Savings
Forecast Accuracy
Faster Delivery
A leading retail giant was facing significant losses due to inventory mismanagement. Overstocking led to waste, while understocking caused missed sales opportunities. Their traditional forecasting methods were unable to keep up with rapidly changing consumer trends.
We developed a custom Machine Learning model capable of analyzing historical sales data, social media trends, and even weather patterns to predict demand. This engine was integrated directly into their supply chain management dashboard.
The system achieved a 95% accuracy rate in demand forecasting, reducing inventory waste by over 30%. The client realized annual savings of $2.5 Million and significantly improved customer satisfaction scores.