Improving Air Freight Forwarding Business Through Air Freight Rate Prediction

Abstract

"The air freight forwarding industry plays a vital role in global trade by transporting high-value and time-sensitive shipments worldwide. However, predicting air freight rates has become increasingly complex due to market volatility, fuel price fluctuations, changing demand, and geopolitical factors. This project aims to tackle these challenges by creating predictive models to forecast air freight rates, aiming to reduce uncertainty and enhance decision-making in the freight forwarding process. One of the key issues in the air freight sector is the unpredictability of rates, leading to decision delays, supply chain disruptions, and increased operational expenses. Fluctuating air freight rates make it challenging for companies to plan budgets, set prices, and ensure reliable service. Rate uncertainties can also impede contract negotiations, strategic planning, and business opportunities. This project utilizes machine learning techniques like Decision Trees, Linear Regression, and Random Forest to predict air freight rates more accurately. By analyzing historical shipment data, chargeable volumes, sales information, and other relevant factors, these models strive to offer dependable rate forecasts for informed decision-making. Enhanced air freight rate prediction enables freight forwarding firms to streamline operations, mitigate risks, and enhance customer satisfaction by providing competitive rates and dependable services. Ultimately, this project aims to contribute to a more stable and efficient air freight industry, reducing decision delays caused by rate uncertainties and benefiting businesses and customers alike."