Predictive supply chain analytics platform for a logistics company, reducing inventory costs by 30% and improving delivery performance by 25%.
A regional logistics company was facing significant challenges with inventory management, demand forecasting, and delivery optimization. High carrying costs and frequent stockouts were impacting profitability and customer satisfaction.
They needed a comprehensive analytics solution to optimize inventory levels, predict demand patterns, and improve delivery route efficiency to reduce costs and enhance service quality.
Implemented advanced machine learning models using historical data, seasonality, and external factors to achieve 92% demand forecasting accuracy.
Developed dynamic inventory management system with automated reorder points and safety stock calculations based on demand variability.
Built AI-powered route optimization engine considering traffic patterns, delivery windows, and vehicle capacity to minimize transportation costs.
Created comprehensive dashboards tracking KPIs including fill rates, inventory turnover, delivery performance, and cost optimization metrics.
Decreased inventory carrying costs by 30% while maintaining 98% service levels through optimized stock management.
Enhanced on-time delivery performance by 25% through intelligent route optimization and capacity planning.
Improved demand forecasting accuracy from 60% to 92% using advanced analytics and machine learning algorithms.
Achieved $850K annual savings through inventory optimization, route efficiency, and reduced operational waste.