Optimizing inventory and reducing waste with AI-powered demand forecasting
National Retail Chain
Retail & E-commerce
Time Series, ML, Big Data
Our client, a national retail chain with over 500 locations, was struggling with inventory management challenges that were impacting both profitability and customer satisfaction:
These issues were resulting in significant revenue loss and reduced customer loyalty.
We developed a machine learning model that analyzes historical sales data, seasonality, promotions, and external factors (weather, local events, economic indicators) to predict demand with high accuracy. The model continuously learns and adapts to changing patterns.
The system generates optimized inventory orders and allocation plans for each store, considering shelf life, storage constraints, and supplier lead times. It also provides dynamic pricing recommendations for items approaching their shelf life.
We implemented a supplier portal that shares forecasted demand, enabling suppliers to better plan production and deliveries. The system also provides real-time visibility into inventory levels and movement across the supply chain.
Reduction in inventory waste
Increase in sales from better stock availability
Reduction in working capital tied to inventory
Forecast accuracy for key product categories
“The demand forecasting solution has been a game-changer for our business. We've significantly reduced waste, improved product availability, and made our supply chain more responsive. The AI's ability to account for external factors like weather and local events has been particularly impressive, helping us stay ahead of demand fluctuations.”
Discover how our demand forecasting solutions can reduce waste and increase sales in your business.