Abstract

AI-driven workforce forecasting is revolutionizing how global logistics and supply networks prepare for peak demand periods and navigate operational disruptions. This paper explores the strategic application of artificial intelligence, particularly machine learning algorithms, to predict workforce requirements with precision across complex supply chains. Traditional labor planning approaches often fall short during seasonal surges, sudden market shifts, or crisis events. In contrast, AI-based labor forecasting tools leverage historical data, real-time metrics, and external variables to generate accurate, adaptive forecasts that support proactive decision-making. The study highlights the integration of predictive analytics into workforce management systems to anticipate labor shortages, optimize shift scheduling, and balance resource allocation. By identifying patterns in shipment volumes, port delays, route efficiency, and customer demand cycles, AI models help supply chain leaders maintain operational continuity and meet service-level expectations even under stress. Moreover, scenario-based simulations and digital twin technologies are examined for their role in stress-testing workforce strategies against various disruption scenarios, including pandemics, geopolitical instability, and climate-induced events. These virtual replicas of logistics systems allow decision-makers to assess the impact of labor adjustments in real time, increasing resilience and agility. Case studies from multinational logistics providers demonstrate how AI-enhanced planning frameworks have improved hiring timelines, reduced overtime costs, and maintained service reliability during peak seasons such as Black Friday and Chinese New Year. The paper underscores that AI-driven forecasting not only ensures labor availability but also strengthens enterprise responsiveness by aligning human capital strategies with evolving global supply chain dynamics. Ultimately, the transition toward AI-enabled workforce forecasting marks a pivotal advancement in intelligent logistics management. By embedding predictive capabilities into labor planning, organizations gain a critical edge in mitigating disruptions, scaling operations during demand spikes, and optimizing labor investments for long-term competitiveness.