Smart logistics: How AI is solving modern supply chain problems?

One key shift happening today is how AI is moving from the background to the very heart of supply chain operations. Three use cases, in particular, are starting to stand out. First, predictive analytics help in anticipating disruptions before they materialise. Companies today are moving away from guesswork, using real-time data to forecast demand, anticipate disruptions and plan inventory more accurately. Today, AI-based forecasting reduces errors by 20–50 per cent compared to traditional spreadsheet methods.³ Second, digital twins are allowing organisations to simulate entire supply chain networks, test different scenarios and make better, more informed decisions before disruptions actually occur. And third, AI agents are beginning to automate routine decisions, whether it is rerouting shipments, managing warehouse flows or responding to demand changes in real time.

What’s interesting is how fast some regions are moving from concept to execution. Look at the UAE, for instance. A leading global logistics and supply chain company is embedding AI into its day-to-day operations. For example, the company has created a real-time replica of Jebel Ali Free Zone, which gives operators visibility into every moving part of the port – from gate movements to equipment health – while allowing them to simulate different scenarios before taking decisions. Similarly, they are also using predictive analytics to forecast cargo flows, optimise yard movements and predict equipment failures in advance, helping reduce delays and improve efficiency. Today, AI-driven systems at the Jebel Ali Port have helped eliminate unnecessary container moves and improved truck turnaround times by 20 per cent.⁴

Similarly, AI agents are also now quickly moving from concept to operations, with a major ports and maritime group in the UAE deploying several agents to streamline trade activities. For instance, one of its flagship agents gives real-time guidance to a fleet of over 270 vessels.⁵ By factoring in weather and port congestion, it can recommend slowing down when a berth is not available, leading to fuel savings while also reducing emissions and engine stress. Another agent predicts where empty containers will be needed and proactively repositions them, replacing what was earlier a manual and time-consuming process with faster data-driven decisions.

What stands out is not just the technology itself but how it is being applied on a scale. AI, today, is being embedded into core operations, improving speed, visibility and coordination across the value chain. And that is redefining modern logistics.

For India, the opportunity is similar but the starting point is different. Our supply chains are far more fragmented with several stakeholders, varying levels of digitisation and infrastructure gaps that still need to be addressed. Considering this, investing in emerging tech, especially AI, can have a huge impact.

Take demand planning and inventory management, for instance. With sectors like retail, e-commerce and agriculture seeing frequent demand changes, predictive analytics can help manage inventory in a much more structured way. Similarly, digital twins can play an important role in India’s large logistics ecosystems, such as ports, industrial corridors and multimodal networks, by allowing simulation of disruptions and optimise flows before they happen. With initiatives like PM Gati Shakti already creating a unified infrastructure view, deploying AI on top of this can significantly improve coordination and decision making.

For India to truly harness the power of emerging technologies, we must move from isolated pilots to ecosystem-wide AI adoption. This starts with strengthening our digital infrastructure, accelerating ULIP and building a national logistics data stack that integrates real-time cargo, vessel and multimodal information – giving AI the foundation it needs to optimise flows end-to-end. India’s success in building population-scale digital public infrastructure – such as Aadhaar, UPI and Goods and Services Tax Network (GSTN) – offers a proven blueprint for how logistics data can be standardised, interoperable and innovation‑ready. The government can catalyse this shift through targeted incentives, while encouraging start-ups to build deep-tech, logistics-focused AI solutions. Aligned efforts under the National Logistics Policy, Digital India and the IndiaAI Mission can further accelerate adoption, especially among MSMEs and fleet operators. At the same time, AI needs to be scaled across our core logistics assets, especially ports, where digital twins, predictive congestion modelling and automated documentation can transform efficiency. Estimates suggest AI could save nearly INR20,000 crore in cargo handling and another INR15,000 crore annually in broader port logistics – value we can unlock only if AI is deployed consistently across all major ports and corridors.⁶ Beyond ports, India must bring AI into trucking, warehousing and multimodal operations to create predictive logistics corridors that stay ahead of demand and disruption. With the right policy support, an AI-ready workforce, and a strong domestic innovation ecosystem, India can build a logistics network that rivals the world’s best – faster, smarter and far more resilient.

As supply chains grow more complex, the future will be shaped by intelligence and not just infrastructure. Those who scale AI effectively will define the generation of resilient supply chains.