{"id":3110,"date":"2026-02-02T17:17:46","date_gmt":"2026-02-02T16:17:46","guid":{"rendered":"https:\/\/pharos390.com\/?p=3110"},"modified":"2026-02-05T11:25:17","modified_gmt":"2026-02-05T10:25:17","slug":"ai-logistics-real-cases-trends-alice-network","status":"publish","type":"post","link":"https:\/\/pharos390.com\/en\/ai-logistics-real-cases-trends-alice-network\/","title":{"rendered":"AI in logistics: real cases and trends from the ALICE network"},"content":{"rendered":"\n

ArtificiaI Intelligence (AI) is redefining European logistics, improving efficiency, costs, and sustainability. The <\/em><\/strong>White Paper \u2018AI in Logistics<\/em><\/strong><\/a> shows, through real cases from the <\/em><\/strong>ALICE network,<\/em><\/strong><\/a> how applications based on perception, reasoning, and action are already transforming ports and transport, confirming AI as a strategic pillar of the sector.<\/em><\/p>\n\n\n\n

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In recent years, Artificial Intelligence (AI)<\/strong> has gone from being a futuristic concept to becoming a key topic at conferences, in publications and in specialised forums. Recent advances in generative AI are transforming the global economy<\/strong>, with examples such as ChatGPT<\/strong><\/a>, which is already part of our daily lives, and Midjourney, which is revolutionising image creation. In Europe, regulation is also advancing: the EU AI Act <\/strong>(European Parliament, 2023) seeks to ensure the ethical<\/strong> and safe<\/strong> use of this technology<\/strong> by establishing principles <\/strong>of transparency<\/strong> and accountability<\/strong>.<\/p>\n\n\n\n

In the logistics sector<\/strong>, AI <\/strong>is emerging as a strategic tool<\/strong> for optimising processes<\/strong>, reducing environmental impact<\/strong> and improving decision-making<\/strong>. According to Accenture<\/strong><\/a> (Lagunas & Riedl, 2022), 86% of operations managers consider AI<\/strong> essential to achieving their growth objectives, and 40% of supply chain<\/strong> managers rank it as their second technological priority<\/strong>, behind only the cloud. Logistics is fertile ground for AI<\/strong>: it requires data and generates enormous volumes of information, as well as operating in complex and dynamic environments. Transport, which accounts for 5% of European GDP and 25% of greenhouse gas emissions, is an ideal field for applying AI<\/strong> and achieving substantial improvements.<\/p>\n\n\n\n

The White Paper \u2018AI in Logistics\u2019<\/strong><\/a> (Murrenhoff et al. 2024) explores how this technology is revolutionising the sector, from its fundamentals to real-world cases. Machine learning, one of the pillars of AI<\/strong>, enables systems to learn from data and improve their predictions without explicit programming. There are three main approaches: supervised learning, which trains models with known examples; unsupervised learning, which discovers patterns without labels; and reinforcement learning, which learns through trial and error, receiving rewards or penalties based on its actions.<\/p>\n\n\n\n

Applications in logistics<\/strong> are grouped into three main categories: perception, reasoning and action<\/strong>. Perception refers to solutions that collect information from the environment using sensors<\/strong> and data analysis<\/strong>; reasoning encompasses systems that process and analyse data to make intelligent decisions<\/strong>; and action<\/strong> comprises technologies<\/strong> that perform physical tasks using robotics<\/strong> and automation<\/strong>. The examples illustrating these categories are not theoretical, but real projects carried out by members of the ALICE network<\/strong><\/a>, which leads innovation<\/a><\/strong> in transport<\/strong> and logistics<\/strong> at European level.<\/strong><\/p>\n\n\n\n

In terms of perception, projects such as Captain AI<\/strong><\/a> use artificial intelligence <\/strong>and radar<\/strong> to improve navigation<\/strong> and avoid collisions in port environments<\/strong>, while 5G-LOGINNOV<\/strong><\/a> applies computer vision to detect the status of seals on containers<\/strong> and transmit video<\/strong> in real time<\/strong> thanks to 5G networks<\/strong>. In reasoning, the E2RM<\/strong><\/a> project by Fundaci\u00f3n Valenciaport<\/strong><\/a> predicts the availability of emptycontainers<\/strong> in depots, reducing unnecessary movements and costs, and TOOL<\/strong> forecasts traffic conditions to optimise routes<\/strong> and just-in-time<\/em><\/strong> deliveries. In action, initiatives such as SWARMPORT<\/strong><\/a> simulate nautical processes<\/strong> in ports<\/strong> with multi-agent models to reduce<\/strong> layover times<\/strong>, while EMONS<\/strong><\/a> uses reinforcement<\/strong> learning<\/strong> and neural networks to minimise empty kilometres through intelligent planning<\/strong> in land transport<\/strong>.<\/p>\n\n\n\n

Los beneficios son evidentes, pero implementar IA<\/strong> no es sencillo. El White Paper<\/em><\/strong><\/a> destaca cuatro claves para empezar: abordar proyectos peque\u00f1os y escalarlos progresivamente; combinar conocimiento t\u00e9cnico<\/strong> y de negocio<\/strong>; garantizar datos de calidad<\/strong> y cantidad suficiente; y definir recursos<\/strong> de software<\/em> y hardware<\/em>,<\/strong> valorando opciones en la nube<\/strong> para reducir costes iniciales. Adem\u00e1s, el IA Design Canvas <\/em><\/strong>desarrollado por Piest et al. (Piest, Iacob & Wouterse, 2022) propone una metodolog\u00eda<\/strong> para conceptualizar<\/strong> proyectos IA<\/strong>, estructurada en tres capas: conceptual<\/strong>, que define la idea y objetivos; fundacional<\/strong>, que establece funciones cognitivas y m\u00e9tricas; y de soporte<\/strong>, que aborda aspectos \u00e9ticos, legales y t\u00e9cnicos.<\/p>\n\n\n\n

The conclusion is clear: AI<\/strong> is not an option, but a strategic necessity<\/strong> for the logistics of the future<\/strong>. Its potential to improve efficiency<\/strong>, sustainability<\/strong> and competitiveness<\/strong> is enormous, provided that the challenges<\/strong> are addressed with planning<\/strong> and collaboration<\/strong>.<\/p>\n\n\n\n

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References<\/h3>\n\n\n\n