Foundation Models-Driven Support to Logistics

Bernardo Nicoletti
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Bernardo Nicoletti: Temple University

Chapter Chapter 5 in Artificial Intelligence for Logistics 5.0, 2025, pp 133-162 from Springer

Abstract: Abstract This chapter comprehensively examines FMs and their transformative impact on logistics management and operations. It examines how FMs, particularly Large Language Models (LLMs) and VFMs, are revolutionizing various aspects of logistics, from procurement to warehouse operations to transportation. The chapter details how FMs improve demand forecasting and inventory management through advanced predictive analytics, enabling organizations to optimize inventory levels and reduce waste. In warehouse operations, FMs facilitate layout optimization, improve picking efficiency, and strengthen safety protocols through real-time monitoring systems. The chapter highlights the role of FMs in developing sustainable practices, including energy optimization and waste reduction in warehouse environments. Transportation logistics benefits from FMs through improved route optimization and last-mile delivery solutions. The chapter explores how FMs improve quality inspection processes through autonomous drones and advanced defect detection systems [Lichtenwalter et al., Detect industrial defects at low latency with computer vision at the edge with Amazon SageMaker Edge. Retrieved December 31, 2024, from https://aws.amazon.com/it/blogs/machine-learning/detect-industrial-defects-at-low-latency-with-computer-vision-at-the-edge-with-amazon-sagemaker-edge/ (2021)]. It highlights FMs’ predictive maintenance capabilities and how they help prevent equipment failure and reduce operational downtime. While the chapter acknowledges that implementing FMs comes with challenges, such as data management and system integration, it highlights the significant long-term benefits of implementing FMs in logistics. It concludes that organizations with these technologies can gain a competitive advantage in the global marketplace while promoting sustainable practices.

Keywords: Foundation Models (FMs); Logistics management; Artificial intelligence; Predictive maintenance; Warehouse optimization; Route optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-94046-0_5

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DOI: 10.1007/978-3-031-94046-0_5

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