Artificial Intelligence for Efficiency Sustainability and Inclusivity in TradeTech 2025

Predictive analytics: AI excels in using voluminous data to predict outcomes of logistics operations. Such predictions can help firms anticipate cascading effects, such as the ripple effects of flooding in a rural province or a strike at a single port. AI can integrate vast amounts of both real- time and historical data, such as: –Trade trends –Weather conditions –Port traffic  –Geopolitical events  –Economic indicators  –Social media trends AI can also generate new insights on interrelations, thereby helping logistics providers plan routes and forecast demand. Such predictive analytics improve businesses’ ability to optimize inventory and reduce waste. Moreover, pairing AI with other technologies like internet of things (IoT) improves firms’ ability to plan maintenance schedules and anticipate equipment failure (see Box 7). By organizing timely repairs, firms can reduce unexpected downtime and extend assets’ lifespans. These analyses become more accurate over time as AI continuously learns from and adapts to past predictions and new data.  Digital twins and simulation: Modelling and simulations have long been a tool for logistics providers, and AI enhances these traditional tools by feeding real-time data into evolving simulations. The technique of creating “digital twins” allows logistics operators to replicate supply chain networks in digital models to represent ports, warehouses, distribution centres and other transit nodes. By incorporating sensors, IoT and other data sources, digital twins allow logistics operators to manage potential disruptions proactively. Designing and simulating supply chains – rather than just accepting inheriting real supply chains – gives users more flexibility to craft intelligent responses.  These analyses become more accurate over time as AI continuously learns from and adapts to past predictions and new data. Digital twins by Maersk BOX 9 Maersk has adopted AI in pricing, route optimization and forecasting models. Its digital twin modelling allows port operators to assess responses if storage facilities approach capacity, strikes loom or weather changes. Digital twins, powered by data from sensors, can optimize how the operator should plan the loading and unloading of containers under such conditions.AI-powered simulations enable the analysis of historical and current data to maximize efficiency. Moreover, such tools increase overall safety by improving training. Port workers and vessel crews can use digital twins to simulate high-risk scenarios, thereby reducing accidents and improving resilience.  Source: Maersk 17 Artificial Intelligence for Efficiency, Sustainability and Inclusivity in TradeTech