Preliminary Analysis of B2B Courier Charge Accuracy Using ML

Abstract

Accurate estimation and validation of courier charges are essential because they underpin B2B logistics operations, enabling cost control, maintaining billing transparency, and building trust between logistics providers and their business clients. The pricing of courier services in extensive supply chain systems is affected by several changing factors, including shipment weight, transportation distance, service type, scheduled delivery time, and specific pricing rules established by different carriers. The differences between estimated amounts and actual billed amounts create situations that lead to revenue loss and operational inefficiency. The research paper establishes an initial examination of B2B courier charge accuracy by employing machine learning methods to create models that assess the existing variations. By analysing past shipment data, the study identifies key factors driving charge deviations and develops a predictive regression model to determine final courier charges—a Forest at Random. To improve prediction accuracy, a regressor is used to find nonlinear relationships between shipment characteristics. To identify discrepancies, the study compares predicted charges with actual billing records and evaluates model performance using standard regression metrics. The research findings are further examined through statistical summaries and graphical representations that provide useful information for decision-making. The study shows that automated systems for charge validation and cost optimisation can be developed using machine learning techniques. This study lays the groundwork for developing scalable, data-driven systems that will reduce billing disparities in B2B logistics operations and increase pricing transparency. Index Terms: Random Forest Regressor, Machine Learning, B2B Logistics, Courier Charge Prediction, and Cost Optimisation