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This chapter examines implementing AIs in organizational logistics and operations management. The analysis emphasizes the importance of a structured approach to AI integration, distinguishing between standard AIs for general applications and vertical AIs tailored to specific industry needs. The implementation framework includes several critical components, including AI-powered digital twins, Industrial Internet of Things (IIoT) integration, natural language interfaces, and big data analytics (Srai et al., Supply chain digital twins: Opportunities and challenges beyond the hype. In 23rd Cambridge International Manufacturing Symposium, 26–27, 2019). The discussion highlights the importance of ethical governance and change management in AI implementation and advocates for dedicated ethics committees and systematic oversight processes. Cybersecurity is an important topic, particularly about AI agents in logistics, focusing on dealing with data breaches, hostile attacks, and logistics vulnerabilities through encryption, access controls, and regular audits. The technical implementation framework proposes a hybrid approach that combines proprietary and public data sources to optimize AI performance. The system architecture integrates Large Language Models (LLMs), AI kernels, and knowledge-based systems (KBS) with digital twins and IoT infrastructure (Nicoletti & Appolloni, Framework of IoT, Blockchain, Digital twins, and Artificial Intelligence solutions in support of the digital business transformation of Logistics 5.0. In L. Ferreira, M. Cruz, E. Cruz, H. Quintela, & M. Cunha (Eds.), Supporting technologies and the impact of blockchain on organizations and society (pp. 195–219). IGI Global. https://doi.org/10.4018/978-1-6684-5747-4.ch012 , 2023b). This integration enables advanced monitoring, control, and optimization of logistics processes while maintaining user-friendly interfaces for operational accessibility. The chapter highlights the importance of comprehensive planning, ethical considerations, and ongoing maintenance for successful AI implementation in logistics operations.

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Project for AI-Driven Logistics Implementation and Utilization

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  • 发表日期：

    2025年

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  • Bernardo Nicoletti

摘要

This chapter examines implementing AIs in organizational logistics and operations management. The analysis emphasizes the importance of a structured approach to AI integration, distinguishing between standard AIs for general applications and vertical AIs tailored to specific industry needs. The implementation framework includes several critical components, including AI-powered digital twins, Industrial Internet of Things (IIoT) integration, natural language interfaces, and big data analytics (Srai et al., Supply chain digital twins: Opportunities and challenges beyond the hype. In 23rd Cambridge International Manufacturing Symposium, 26–27, 2019). The discussion highlights the importance of ethical governance and change management in AI implementation and advocates for dedicated ethics committees and systematic oversight processes. Cybersecurity is an important topic, particularly about AI agents in logistics, focusing on dealing with data breaches, hostile attacks, and logistics vulnerabilities through encryption, access controls, and regular audits. The technical implementation framework proposes a hybrid approach that combines proprietary and public data sources to optimize AI performance. The system architecture integrates Large Language Models (LLMs), AI kernels, and knowledge-based systems (KBS) with digital twins and IoT infrastructure (Nicoletti & Appolloni, Framework of IoT, Blockchain, Digital twins, and Artificial Intelligence solutions in support of the digital business transformation of Logistics 5.0. In L. Ferreira, M. Cruz, E. Cruz, H. Quintela, & M. Cunha (Eds.), Supporting technologies and the impact of blockchain on organizations and society (pp. 195–219). IGI Global. https://doi.org/10.4018/978-1-6684-5747-4.ch012 , 2023b). This integration enables advanced monitoring, control, and optimization of logistics processes while maintaining user-friendly interfaces for operational accessibility. The chapter highlights the importance of comprehensive planning, ethical considerations, and ongoing maintenance for successful AI implementation in logistics operations.