Key ways artificial intelligence (AI) will power integrated logistics

Without a doubt, artificial intelligence (AI) is here to revolutionise the world, logistics included.

Increasingly, AI is used to automate and optimise supply chain operations. The growing number of AI-based innovations and the increasing cost of manual labour are making the use of AI in logistics more and more attractive, fuelling the optimisation of integrated logistics. In the context of supply chain management, this transformation (also referred to as “platform change”) could transform the way we plan, execute, and optimise the movement of goods from one location to another. More specifically, by leveraging the power of artificial intelligence (AI) and machine learning (ML), businesses can gain:

• Real-time insights into their supply chain operations.
• Accurate forecasting of demand patterns.
• Optimised inventory levels.
• Reduced transportation costs.
• Improved customer service.
• High level strategy analysis and optimized financial reporting.
• Better security and risk management.

Cloud and artificial intelligence for integrated logistics

Supply chains are also becoming digitised in terms of how data is being created, stored, and analysed. Years of investment in the deployment of sensors, cameras, IoT devices, and integrations have helped to digitise the physical movement of goods and has significantly increased the volume of data created throughout supply chains. In addition, while data was traditionally stored in on-premises warehouses (that were difficult to access, integrate or innovate with), we now see the emergence of cloud-based systems. Soon, leveraging the latest cloud-based tech architectures will enable various use-cases of AI in supply chain management, enabling businesses to take advantage of integrated logistics offerings and seamlessly manage the flow of their cargo between sea, land, and air.

Upcoming uses of AI for supply chain management

Here below, are the areas in which the implementation of AI and machine learning will soon reshape processes and modus operandi:

• Fraud detection: These innovations can be used to detect and prevent fraudulent activities in the supply chain, such as theft, counterfeiting, and unauthorized access to sensitive data.
• Demand forecasting: These tools can help with predicting demand patterns more accurately by analysing historical sales data, market trends, weather, and possible disruptions. This can enable better inventory planning and reduce stock outs.
• Predictive maintenance: By monitoring equipment and assets in real-time, potential issues can be identified before they occur. This can help reduce downtime and maintenance costs, as well as improve overall equipment effectiveness. The concrete applications here are relevant for all assets owners optimising “up-time” such as warehouse owners, terminal operators, fleet managers, etc.
• Real-time supply chain monitoring and adjustment: AI can provide real-time visibility into the entire supply chain, from raw materials to finished products. This can help identify potential bottlenecks and delays, enabling companies to proactively take corrective actions. The digital twin technology (a virtual, realistic replica of a physical asset or system, like a truck, warehouse or supply chain) will help with both performance-improvement and strategic purposes. Digital twin simulations can be used to visualise the performance of supply chain operations, to manage potential disruptions, and to stress-test supply chain resilience by modelling scenarios such as changing distribution flows. This acquired visibility into supply chains will improve risk management, decision-making as well as customer experience.
• Warehouse and transportation automation: With the use of AI and ML, it is possible to automate and optimise warehouse and transportation operations. This includes software systems used for forecasting, inventory management and route planning, as well as automated assets like robots, picking arms, drones, forklifts, and trucks. This can help companies reduce costs and improve efficiency, while also improving their customer service levels. “This is a gamechanger” says Jannik Pedersen, Head of Strategic Insights at Maersk, adding “we currently see constrained autonomous vehicle (AV) applications at yards, warehouses, and terminals. However, we believe that starting from 2025 we will start see “hub-to-hub” AV applications on highway corridors changing trucking forever and leading to better logistic integrations”.
• Personalisation: AI can be used to generate personalised recommendations for customers based on their purchase history, business fundamentals (e.g., industry, size, and value chain) search history, and other data. This can help improve customer experience and increase sales, by predicting next best actions and/or optimization ideas.
• Autonomous processes: With the implementation of AI and AI agents (which are AI tools with the ability to autonomously perform consecutive AI tasks and can provide multiple outputs with just one input) whole work processes with multi source data input, and workflows that require inputs from those processes, can potentially run autonomously.