Problem
Cargomatic faced significant challenges in managing additional accessorial fees incurred during shipping operations. These unexpected fees often led to revenue leakage, impacting profitability and making operational forecasting difficult. Without a way to anticipate these costs, shipment planning was frequently disrupted, and relationships with carriers were strained due to unpredictable pricing adjustments.
Revenue losses from unanticipated fees were a persistent problem, and the lack of actionable insights prevented Cargomatic from effectively mitigating these expenses. The company needed a solution that could identify and predict potential accessorial fees before they occurred, enabling better financial and operational planning.
Solution
Cargomatic partnered with Zams to develop an advanced predictive classification model designed to anticipate additional accessorial fees. The model analyzed a variety of key input variables, such as shipment weight, dimensions, freight class, route specifics, and carrier performance metrics, to deliver actionable predictions.
To achieve high accuracy, advanced classification algorithms like Gradient Boosting and Random Forest were employed. The predictive model was seamlessly integrated into Cargomatic’s logistics management platform, providing real-time fee predictions during the shipment planning phase, allowing for more informed decision-making.
Outcome
By implementing this predictive model, Cargomatic significantly improved its ability to anticipate accessorial fees, achieving a 70% accuracy rate—outperforming the industry standard of 50%-60%. This improvement led to a 4% reduction in revenue leakage, saving the company substantial annual costs.
Additionally, the scalability of the model ensured it could handle increasing data inputs as operations expanded. This not only provided immediate financial benefits but also positioned Cargomatic for long-term success in optimizing its shipping cost management.
