While finance and healthcare get the headlines for embracing AI, a number of the most lucrative use cases are on the roads. Logistics is the backbone of world trade, and executives are catching on—in 2024, 90% of supply chain leaders said technological capabilities are top aspects when selecting freight partners. The rationale? AI is popping an industry notorious for inefficiency into businesses’ upper hand over the competition.
Historically reliant on paper-based processes, logistics has been a blind spot for supply chain leaders. This lack of visibility fuels the bullwhip effect: small retail demand changes inflate as they travel up the provision chain, reaching raw material suppliers. Coupled with long lead times, this forces each stage—retailers, wholesalers, distributors, and manufacturers—to overorder, exacerbating the issue.
But let’s imagine for a second that as an alternative of filling trucks and warehouses with semiconductor chips just for PC demand to say no, logistics had real-time tracking and provide chain visibility. What if they might predict demand fluctuations with 99.9% accuracy? And supply flexible logistics solutions like on-demand transportation in response?
With AI and machine learning, this ideal won’t be so far as business leaders think.
Supply Chain Visibility Explains the Unexplainable
When asked ”Which of freight forwarders’ technological capabilities do you discover most respected?”, 67% of respondents voted for real-time shipment tracking.
Web of Things (IoT) devices revolutionize cargo tracking, providing granular visibility and real-time alerts in regards to the condition of products—crucial for time-sensitive or temperature-controlled shipments like food and pharmaceuticals which have strict verification regulations. Not only can supply chain leaders learn the way much stock they’ve and where it’s situated at any moment, but they also can find out about its condition. Shippers can monitor and share details about whether goods are hot, cold, wet, or dry, they usually can see if doors, boxes, or other containers are being opened. These insights explain abnormalities with food items arriving perished, minimizing future waste.
Moving over to the electronics industry, corporations can assure customers that products like laptop motherboards are real when items are tracked and traced. Warehouse and inventory managers can scan barcodes and QR codes to trace stock levels, or use radio frequency identification (RFID) tags attached to things to trace high-value assets with no need to scan them. More advanced RFID tags offer real-time alerts when conditions (corresponding to temperature) deviate from pre-set thresholds.
Item-level visibility has grow to be a must for shippers and their supply chain partners. Logistics providers must quickly adapt to disruptions and demand changes and this visibility increases resilience. These insights allow businesses to have a holistic view of inventory and make informed decisions in real-time, reducing waste and improving resource utilization.
Demand Forecasting and Reliable Lead Times
IoT sensors’ usefulness extends much further than simply tracking items and updating customers in real time. They supply data that fuels demand forecasting algorithms.
Take Coca-Cola, for instance. The soft drink giant leverages IoT to observe and gather data from its vending machines and fridges, tracking real-time metrics for stock levels and consumer preferences evaluation. This permits Coca-Cola to make informed predictions about demand for specific product types and flavors.
Freight forwarders increasingly use the same method to predict freight volume in specific lanes, allowing them to optimize fleet deployment and meet service level agreements (SLAs). Excellent news for businesses as they profit from more reliable lead times, which suggests lower inventory costs and fewer stockouts.
There are two overarching ways logistics corporations use forecasting:
- Long-range (strategic): For budgets and asset planning (6-month to 3-year plans).
- Short-range (operational): Most useful for logistics, predicting ground freight transportation as much as 14 days prematurely, and 1-12 weeks for ocean shipping.
For instance, DPDgroup’s courier company, Speedy, predicts demand by combining historical shipment data (parcel size, delivery times, customer behavior, etc.) with external aspects like holidays, retail peaks (Black Friday), etc. Under the brand new system, AI-powered demand forecasting allowed Speedy to quickly discover and cancel unnecessary trips and line hauls. This led to a 25% hub-to-hub cost reduction and a 14% increase in fleet utilization. McKinsey found similar ends in supply chain management, with forecasting tools reducing errors by 20 to 50%.
Load-to-Capability Matching: Stop Hauling Air
Uber Freight reported in 2023 that between 20% and 35% of the estimated 175 billion miles trucks drive within the US every year are likely empty—draining fuel and labor budgets. Now that AI, ML, and digital twin technology are mainstream, a truck that just made a delivery in Dallas shouldn’t deadhead back to Chicago. AI-driven load-matching platforms analyze freight demand, truck availability, and route patterns to make sure every truck is hauling at maximum efficiency.
Logistics corporations take the gathered freight information utilized in demand forecasting tools (load size, weight, dimensions, type—whether it’s perishable, hazardous, etc.) and cross-analyze this with their capability. AI-powered analytics can review the truck size, features, location, and availability, together with driver hours of service regulations, to attach shippers and carriers in real time. Digital twin technology can potentially take this a step further, simulating virtual scenarios to make sure the optimal match.
To illustrate a shipper enters details about their upcoming load right into a digital platform. The system analyzes available carrier capability and matches the load with the best option, considering the optimization aspects mentioned earlier. The transaction is processed, and the shipment is tracked throughout its journey.
By tracking assets, predicting demand, and matching loads, logistics corporations are saving huge amounts. They’re minimizing empty miles, maximizing vehicle utilization, and eliminating carbon footprint—ultimately improving customer relationships with more reliable deliveries.
The advantages extend beyond logistics. This level of supply chain visibility allows retailers and manufacturers to optimize production schedules and reduce inventory holding costs. They will plan shipments more efficiently, minimizing delays and storage fees, and reducing transportation expenses by ensuring optimal truck utilization and minimal wasted capability.
Any industry coping with resource allocation—airlines, manufacturing, even cloud computing—can learn from how logistics AI is streamlining operations.
