AI-Driven Capacity Forecasting for Last-Mile Logistics

Abstract

The article examines the features of throughput forecasting based on artificial intelligence in the field of last-mile logistics. The relevance of the topic is driven by the fact that, under conditions of rapid growth in e-commerce and tightening consumer expectations, last-mile logistics faces unprecedented pressure, requiring hyper-accurate forecasting of operational capacity to enable a just-in-time delivery model and meet delivery deadlines without excessive costs. Traditional planning methods demonstrate their inadequacy under conditions of high demand volatility. This work describes an artificial intelligence (AI)-based framework used for forecasting daily resource requirements (personnel, transport) in large-scale logistics networks. The framework is based on a hybrid machine learning architecture that combines regression models for baseline load forecasting and the XGBoost algorithm for detecting and quantifying abnormal demand spikes. A key element of the system is a dynamic capacity buffer algorithm that, in real time, calculates the required reserve of resources to mitigate risks associated with forecast errors. The article analyzes the architecture, implementation methodology, and empirical results, and also discusses the role of such systems as a strategic tool for demand shaping.