AI Transformation of Logistics

This chapter comprehensively analyzes AI’s transformative impact on Logistics 5.0 and examines AI applications in key logistics functions, from planning to execution. In demand forecasting and inventory management, AI-powered solutions enable more accurate predictions and optimal stock levels, with organizations such as Otto achieving significant inventory reductions through AI implementation [Carnes et al., Resource orchestration for innovation: Structuring and bundling resources in growth-and maturity-stage firms. Long range planning, 50(4), 472–486. https://doi.org/10.1016/j.lrp.2016.07.003 (2017); Chen et al., Annals of Operations Research, 344, 1–6. https://doi.org/10.1007/s10479-024-06450-2 (2025)]. Solutions revolutionize procurement through automated partner assessments and smart contracts while improving warehouse operations through intelligent location selection and picking systems. Transportation management sees innovation through AI-powered route optimization, autonomous guided vehicles (AGVs), and delivery drones, although the latter face some implementation challenges. The chapter shows how AI supports sustainability initiatives by optimizing reverse logistics and tracking emissions. Another critical application is predictive maintenance, where AI analyzes sensor data to prevent equipment failures and optimize maintenance schedules (Jay et al., Sustainability-oriented Innovation: A Bridge to Breakthroughs, MIT Sloan Management Review (2015)). The chapter highlights the role of AI in enabling “smart logistics’,” characterized by seamless collaboration between humans and machines. This transformation extends to customer service through AI chatbots and risk management through advanced fraud detection systems. The chapter highlights how AI solutions increase efficiency, reduce costs, and improve decision-making across the logistics value chain while addressing implementation challenges such as data quality and security concerns.