While finance and healthcare are making headlines due to AI adoption, some of the most lucrative use cases are on the roads. Logistics is the backbone of global trade, and leaders realize it: in 2024, 90% of supply chain leaders said that technological capabilities are the main factors when choosing freight partners. The reason? AI is transforming an industry known for its inefficiency into an advantage for businesses over the competition.
Historically dependent on paper-based processes, logistics has been a blind spot for supply chain leaders. This lack of visibility fuels the bullwhip effect: small variations in retail demand swell as they move up the supply chain, reaching raw material suppliers. Combined with long delivery times, this situation forces each stage (retailers, wholesalers, distributors and manufacturers) to overorder, thus making the problem worse.
But let’s imagine for a moment that instead of filling trucks and warehouses with semiconductor chips only for PC demand to decline, logistics had real-time tracking and supply chain visibility. What if they could predict demand fluctuations with 99.9% accuracy? And provide flexible logistics solutions like on-demand transportation in response?
With AI and machine learning, that ideal may not be as far away as business leaders think.
Supply Chain Visibility Explains the Unexplainable
To the question “What technological capabilities of freight forwarders do you find most useful?” », 67% of those questioned voted for real-time shipment tracking.
Internet of Things (IoT) devices are revolutionizing cargo tracking, providing granular visibility and real-time alerts on the status of goods, which is crucial for time-sensitive or temperature-controlled shipments, such as food and pharmaceutical products, which are subject to strict verification regulations. Not only can supply chain managers know how much inventory they have and where it is at any time, but they can also know its status. Shippers can monitor and share information about whether goods are hot, cold, wet or dry, and they can see if doors, boxes or other containers are open. This information explains anomalies in the arrival of perishable food products, thereby minimizing future waste.
By moving into the electronics industry, companies can assure their customers that products such as laptop motherboards are authentic when items are tracked and traced. Warehouse and inventory managers can scan barcodes and QR codes to track inventory levels, or use radio frequency identification (RFID) tags attached to objects to trace high-value assets without the need to scan them. More advanced RFID tags offer real-time alerts when conditions (such as temperature) deviate from predefined thresholds.
Item-level visibility has become essential for shippers and their supply chain partners. Logistics providers must adapt quickly to disruptions and changes in demand and this visibility increases resilience. This information allows businesses to have a holistic view of their inventory and make informed decisions in real time, reducing waste and improving resource utilization.
Demand forecasting and reliable delivery times
The usefulness of IoT sensors goes far beyond just tracking items and updating customers in real time. They provide data that powers demand forecasting algorithms.
Take Coca-ColaFor example. The soft drinks giant is leveraging IoT to monitor and collect data from its vending machines and refrigerators, tracking real-time metrics on stock levels and analyzing consumer preferences. This allows Coca-Cola to make informed forecasts about demand for specific product types and flavors.
Freight forwarders are increasingly using a similar method to predict freight volume on specific lanes, allowing them to optimize their fleet deployment and meet service level agreements (SLAs). Good news for businesses as they benefit from more reliable delivery times, which means reduced inventory costs and fewer stock-outs.
There are two main ways logistics companies use forecasts:
- Long range (strategic): For budgets and asset planning (6 month to 3 year plans).
- Short range (operational): Very useful for logistics, planning for land transport of goods up to 14 days in advance and 1 to 12 weeks for sea transport.
For example, Speedy, DPDgroup’s courier company, predicts demand by combining historical shipping data (package sizes, delivery times, customer behavior, etc.) with external factors such as holidays, peak retail sales (Black Friday), etc. Under the new system, AI-driven demand forecasting allowed Speedy to quickly identify and cancel unnecessary trips and transportation. This led to a Reduced hub-to-hub costs by 25% and a 14% increase in fleet utilization. McKinsey found similar results in supply chain management, with forecasting tools reduce errors by 20 to 50%.
Load-capacity adaptation: stop carrying air
Uber Freight reported in 2023 that between 20% and 35% of the estimated 175 billion miles trucks travel each year in the United States are likely empty, depleting fuel and labor budgets. Now that AI, ML, and digital twin technology have become mainstream, a truck that just made a delivery to Dallas should not return to Chicago. AI-powered load matching platforms analyze freight demand, truck availability, and route patterns to ensure each truck transports with maximum efficiency.
Logistics companies take the information collected about freight used in demand forecasting tools (load size, weight, dimensions, type – whether it is perishable, hazardous, etc.) and cross-analyze it with their capacity. AI-powered analytics can examine truck size, features, location and availability, as well as driver hours of service regulations, to connect shippers and carriers in real time. Digital twin technology can potentially go even further, simulating virtual scenarios to ensure optimal matching.
Let’s say a shipper enters information about their upcoming load into a digital platform. The system analyzes the available transport capacity and matches the load to the most suitable option, taking into account the previously mentioned optimization factors. The transaction is processed and the shipment is tracked throughout its journey.
By tracking assets, forecasting demand and matching loads, logistics companies realize huge savings. They minimize empty miles, maximize vehicle utilization and eliminate carbon footprint, improving customer relationships with more reliable deliveries.
The benefits extend beyond logistics. This level of supply chain visibility allows retailers and manufacturers to optimize production schedules and reduce inventory holding costs. They can plan shipments more efficiently, minimizing delays and storage costs, and reducing transportation expenses by ensuring optimal truck utilization and minimal capacity waste.
Any industry responsible for allocating resources (airlines, manufacturing, even cloud computing) can learn from how logistics AI streamlines operations.