While finance and healthcare are headlines to embrace AI, some of the most profitable use cases are still on the road. Logistics is the backbone of global trade, and executives are attracting – 90% of supply chain leaders said that technical capabilities when choosing a freight partner are the biggest factor. reason? An industry notorious for its inefficiency in AI has the upper hand in the competition.
Historically, logistics has been a blind spot for supply chain leaders. Lack of visibility can promote the bullhead effect: small retail demand changes as the supply chain moves, when it reaches raw material suppliers. Coupled with the long lead time, this forces overorder at each stage (retailers, wholesalers, distributors and manufacturers), exacerbating the problem.
But let’s imagine logistics with real-time tracking and supply chain visibility rather than filling trucks and warehouses with semiconductor chips. What if they can predict demand fluctuations with 99.9% accuracy? And provide flexible logistics solutions such as on-demand transportation?
With AI and machine learning, this ideal may not be what business leaders think.
Supply chain visibility explains what is unexplainable
When asked “The technical competence of the most valuable freighters do you think?” 67% of respondents voted for real-time shipping tracking.
Internet of Things (IoT) devices revolutionize cargo tracking, providing granular visibility and real-time alerts about commodity conditions, which are critical for time-sensitive or temperature-controlled shipments such as food and medicines with strict verification regulations. Supply chain leaders can not only find out how much inventory they have and where they are at any time, but also understand their status. Shippers can monitor and share information about whether the goods are hot, cold, wet, or dry, and can see if doors, boxes, or other containers are opened. These insights explain abnormalities in food reaching extinct, thus minimizing future waste.
Turning to the electronics industry, companies can assure customers that products such as laptop motherboards are real when tracking and tracing items. 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 track high-value assets without scanning them. More advanced RFID tags provide real-time alerts when conditions (such as temperature) deviate from preset thresholds.
Project-level visibility has become a necessity for shippers and their supply chain partners. Logistics providers must quickly adapt to disruptions and changes in demand, and this visibility increases resilience. These insights allow businesses to have a holistic view of inventory and make informed decisions in real time to reduce waste and improve resource utilization.
Demand forecast and reliable delivery time
The practicality of IoT sensors is more than simply tracking projects and updating customers’ scope in real time. They provide data on fuel that requires prediction algorithms.
Take Coca-Cola as an example. Soft drink giants use the Internet of Things to monitor and collect data from their vending machines and refrigerators, tracking real-time metrics of stock levels and consumer preference analysis. This allows Coca-Cola to make informed predictions about the needs of specific product types and flavors.
Freight forwarders are increasingly using similar methods to predict freight volumes in a particular lane, allowing them to optimize fleet deployments and meet Service Level Agreements (SLAs). This is good news for businesses as they benefit from more reliable delivery times, which means lower inventory costs and less inventory.
There are two general ways for logistics companies to use predictions:
- Remote (Strategic): Budget and asset plan (6-month to 3-year plan).
- Short distance (operation): The most valuable logistics, predict ground freight transportation 14 days in advance and marine transportation 1-12 weeks.
For example, DPDGroup’s courier companies can predict demand by combining historical freight data (package size, delivery time, customer behavior, etc.) with external factors such as holidays, retail peaks (Black Friday), etc. With the new system, AI-driven demand forecasting allows Speedy to quickly identify and eliminate unnecessary trips and Line Trips and Line Trips and Line Trips and Line Hauls. This resulted in a 25% reduction in hub-to-center cost and a 14% increase in fleet utilization. McKinsey found similar results in supply chain management, with forecasting tools reducing errors by 20% to 50%.
Load to capacity matching: Stop hauling air
Uber Freight reported in 2023 that 20 to 35% of the estimated 175 billion miles of trucks driving in the U.S. each year may be empty — exhausting fuel and labor budgets. Now that AI, ML and Digital Twin Technology have become mainstream, trucks that have just delivered in Dallas shouldn’t be back in Chicago. The AI-powered load matching platform analyzes freight demand, truck availability and route patterns to ensure that every truck is hauled with maximum efficiency.
Logistics companies use collected freight information for demand forecasting tools (load size, weight, size, type – whether perishable, hazardous, etc.) and perform cross-analysis with their capabilities. AI-driven analytics can review truck size, functionality, location and availability, as well as driver-hour service regulations to connect shippers and operators in real time. Digital dual technology can further evolve it to simulate virtual scenes to ensure the best match.
Suppose the shipper enters its upcoming load into the digital platform. Taking into account the aforementioned optimization factors, the system analyzes the available carrier capacity and matches the most appropriate options. The transaction was processed and the cargo was tracked throughout the journey.
Logistics companies can save a lot of money by tracking assets, predicting demand and matching loads. They are minimizing empty mileage, maximizing vehicle utilization, and eliminating carbon footprint, thereby improving customer relationships with more reliable delivery.
The benefits outweigh logistics. This supply chain visibility allows retailers and manufacturers to optimize production plans and reduce inventory holding costs. They can plan shipments more efficiently by ensuring optimal truck utilization and minimal waste capacity, minimizing delays and storage costs, and reducing shipping costs.
Any industry that deals with resource allocation – aviation lines, manufacturing and even cloud computing – can learn how to simplify operations from logistics AI.
