Machine Learning Models for Predictive Workforce Planning in Public Sector Logistics

Abstract

This review explores machine learning models for predictive workforce planning in public sector logistics, emphasizing their role in improving efficiency, accuracy, and resource allocation. The traditional planning approach tends to make assumptions that are unchangeable, and is not suitable for logistics systems with fluctuating demands. Machine learning methods such as regression, tree models, time series forecasting, and deep learning help predict the workforce requirements based on operational, workforce, and external data sources. Additionally, the study focuses on the feature engineering, evaluation metrics, applications, challenges, and ethics of the research. It also covers trends to watch, including real-time analytics and AI-powered automation in intelligent workforce management.