How is AI transforming the supply chain?

95% of data-driven decisions, soon automated

The need to develop a solid AI implementation strategy arises from a surprising expectation: 95% of data-driven decisions will soon be at least partially automated, according to the Gartner study. But to fully benefit from AI while mitigating potential risks, organizations must define a comprehensive AI strategy for the supply chain that not only improves competitiveness but also ensures responsible use.

How to build this strategy? A robust strategy must be built on four key pillars: vision, value, risks, and adoption. This involves aligning AI with business objectives, overcoming organizational barriers, addressing issues such as governance and cybersecurity, and prioritizing high-impact, feasible use cases. A clear vision and strong cross-functional collaboration are essential to unlock AI's full potential.

Why does Generative AI play a central role?

Among the various AI applications, Generative AI (GenAI) plays a central role in the current transformation. What is its real impact? According to a revealing NTT DATA report, 95% of 500 manufacturing leaders across 34 countries report that GenAI is directly improving efficiency and financial performance in their organizations.

"For me, tools like ChatGPT or Copilot are comparable to smartphones or social media: technologies that are adopted massively because they fit with the culture of immediacy and are extremely accessible. The challenge is to promote responsible and productivity-oriented use, which requires training that is not only technical but also ethical," highlights Ruth Pablo, Head of Digital Transformation at the Port of Barcelona.

How can supply chain leaders effectively implement GenAI? It is recommended that Chief Supply Chain Officers (CSCOs) align short-term use cases with four key objectives: strategy, technology, organization, and performance. While many leaders are eager to adopt GenAI, few have established structured frameworks to evaluate and prioritize its use.

Suggested short-term applications include content creation, summarization, chatbots, software coding, and data classification. These functions can support strategic planning, technology management, talent development, and performance monitoring. However, leaders must carefully assess feasibility, impact, and potential risks, such as data quality issues, intellectual property exposure, and long-term skill degradation, when developing a balanced portfolio of GenAI initiatives.

In this regard, Ruth Pablo adds that generative AI "has high potential in any organization; in the port sector, it can simplify processes, reduce bureaucracy, and improve the experience of users, customers, and employees. However, when we talk about business processes, its implementation requires a clear strategy, effective data management, and a governance model that allows scaling and maintaining pilots over time."

How to classify AI investments according to their impact?

Supply chain leaders view AI as a strategic investment to gain competitive advantage. What areas are they investing in? Key areas of AI investment include labor (automation and engagement), intelligence (decision-making through ML and GenAI), edge computing, and cybersecurity.

How to categorize these initiatives?

AI initiatives fall into three categories: Everyday AI (productivity-focused), Boundary-pushing AI (operational improvements), and Game-changing AI (market leadership and innovation).

Success depends on organizational readiness, which is why studies like Gartner's recommend a five-step approach: identify high-impact use cases, secure adequate talent, collect relevant data, match techniques to needs, and scale after initial pilots.

As Javier Garrido explains, "AI adoption requires more than technology: it demands cultural change. We are investing in training and building multidisciplinary teams to lead this transformation. As a key supply chain node, we provide high-quality data through tools like our Open Data Portal, offering real-time APIs to support AI development. This empowers our ecosystem to innovate with confidence."

The prioritization framework: AI Use-Case Prism

How to systematically prioritize AI investments? Gartner also provides a guide titled AI Use-Case Prism for Supply Chain, which seeks to offer a framework for prioritizing AI investments in supply chain operations. The prism evaluates use cases based on their business value and feasibility, presenting 18 proposed applications.

These cases span various stages of the supply chain and can also be adapted to the maritime sector. Key supply chain activities addressed include planning, sourcing, manufacturing, delivery, and customer fulfillment.

How do Port Authorities apply these concepts?

Considering the role of a Port Authority, we can focus on the "Source" and "Deliver" stages of the supply chain. Why are these roles relevant? In the "Source" phase, Port Authorities play a key role in managing the flow of raw materials and components through their terminals, ensuring efficient coordination with other stakeholders. In the "Deliver" phase, the port enables the movement of goods, serving as a critical hub for companies to distribute products to their final destinations.

Due to their dual role in source and delivery activities, we identify some cases as especially relevant to ports:

• Data cleansing: the invisible but critical foundation

Data cleansing is especially important in the port environment, as it ensures accurate cargo information, smooth customs clearance, and efficient logistics coordination. It also improves integration with shipping lines, customs, and inland transport, which is crucial in the complex, multi-stakeholder port environment.

Is there a benchmark example? A good example is the Port of Los Angeles tool, "The Signal", an important tool that provides easy data access for all supply chain stakeholders.

• Digital twins: simulating the future

This data can also support Digital Twin applications, as seen at the Busan Port Authority, where an initial version was developed for one terminal (see video above). What is it used for? It is used to track vessels, optimize schedules, and simulate scenarios.

Javier Garrido also points this out, telling us that at the Port of Barcelona they are implementing "different Digital Twin use cases to simulate port operations and anticipate disruptions. AI complements human expertise, enhancing strategic decisions with predictive insights. Our goal is to build a smarter, more resilient port through data-driven innovation."

Finally, autonomous supply chains, combining AI, robotics, IoT, and automation, are becoming a key focus for port authorities seeking greater efficiency and resilience.

• Predictive ETA: the Rotterdam case

One of the most discussed topics is predictive Estimated Time of Arrival (ETA). What is the most notable example? A notable example is the Port of Rotterdam's collaboration with PortXchange Shiptracker, which leverages big data and machine learning to provide more accurate ETA predictions.

• Predictive maintenance: Savannah's success

Another key AI application in delivery is predictive maintenance, which helps reduce both costs and downtime by identifying issues before they occur. What are the real results? For example, the Port of Savannah claims to have reduced unplanned downtime by 30% by integrating big data into a centralized platform and applying advanced analytics.

• Revolutionary automation: Asian cases

How is AI driving automation in delivery activities? Ports like Qingdao have implemented mobile robot control and autonomous systems. Its Qianwan Container Terminal is Asia's first fully automated terminal, declaring they achieved a 70% reduction in labor costs and a 30% improvement in efficiency.

Similarly, Shanghai's Yangshan Port uses AI to optimize the operation of intelligent cranes, Intelligent Guided Vehicles (IGVs), and yard logistics. What other European examples exist? The Port of Felixstowe has also deployed autonomous trucks within its terminal operations.

Sustainability and AI: the green future of ports

Can AI also support sustainability goals? At the Port of Tanjung Pelepas in Malaysia, an AI-powered system developed with Innovez One aims to improve efficiency and reduce emissions. The MarineM platform uses machine learning to automate and optimize the scheduling of port, tug, and pilotage services.

How does this system work? The system efficiently dispatches pilots and tugboats and reallocates resources when vessel ETAs shift. According to its developers, it helps maximize fuel efficiency, reduce greenhouse gas emissions, and minimize congestion for arriving ships.

Are there other sustainability-focused innovations? Another sustainability-focused example is Clean Sea Solutions, a company providing autonomous marine cleanup and real-time environmental impact tracking. Their systems monitor and report on environmental conditions in line with EU standards, helping reduce marine waste and support regulatory compliance.

Diversity of AI technologies in the port sector

AI is transforming the port sector, optimizing operations, improving efficiency, and ensuring security. David Serral, CIO (Chief Information Officer)of the Port of Barcelona, highlights that Barcelona, with its inGenIA project, "positions itself at the forefront of this technological revolution, leading the way toward a more efficient and secure future" in which, he explains, there are different types of AI applicable to the sector: "Another type of AI is Machine Learning, which uses data to identify patterns and make predictions. This technology optimizes the supply chain and inventory management, helping to foresee and mitigate possible disruptions. It also allows analyzing customer data and behaviors to offer personalized experiences."

"Computer Vision is another AI technology that allows machines to process visual information. In the port sector, this technology can be used for capacity and access control, ensuring an orderly and safe flow of people and vehicles. Additionally, Computer Vision facilitates the management and coordination of security and emergency incidents through the integration of cameras and systems from different security bodies," he adds.

From successful pilots to systemic integration

The examples highlighted here show that AI is not just a buzzword—it's already delivering tangible results. Currently, however, most initiatives remain local in scope: ports are moving forward independently, shaping their projects around isolated use cases for specific objectives.

What is the real opportunity? A landscape of diverse, often isolated solutions has been created, each valuable in its own right but not yet connected to a broader whole. Over time, the real opportunity may lie in bringing these pieces together—first within individual ports, where different systems will need to interact seamlessly, and then across the broader supply chain, where integration could amplify benefits well beyond the port gates.

How is the future of AI in ports defined? The future of AI in ports will be defined not only by successful pilots but by the ability to weave them into the complex fabric of global logistics. This integration represents both the greatest challenge and the greatest opportunity for port authorities seeking to lead the next revolution in logistics management.