This month’s figure: 40

As in the almost 40 million litres of fuel that UPS has saved annually by using an AI-enabled model

With supply chain bottlenecks occurring in numerous industries (semiconductors, automobiles, sportwear, capital goods, and others), we are currently weathering a scarcity-based economy, whose difficulties have little chance of receding for another several quarters. Against this backdrop, major freight companies are more than ever the key gears of an economy that is having a hard time staying in balance. This is an opportunity for us to understand how artificial intelligence is now used in the freight industry, with the hope that it will help relieve the temporary imbalances in the global economy.

Predictive analytics – a revolution in logistics made possible by artificial intelligence: the examples of DHL and UPS

A tool that forecasts demand and adjusts network capacity accordingly is the dream of the entire logistics sector. Anticipating demand better makes for a better allocation of vehicle fleets and, hence, a reduction in operating costs. This business challenge is often expressed in multi-variable equations that artificial intelligence is best at resolving. To cite one example, DHL has created a machine learning model for its air freight business, consisting of 58 internaldata parameters. This allows the US logistics group to forecast week-on-week whether transit times will rise or fall, as well as to model the impact of weather and operational factors on delivery times. Other global logistics leaders, such as UPS, have tried to develop these functions in-house by investing in data science teams to develop their own machine learning models. UPS has thus saved almost 40 million litres of fuel annually by using algorithms to optimise its last-mile delivery model. Smaller logistics groups wanting to use AIenabled predictive analytics can call on solutions such as ClearMetal, a start-up that has made artificial intelligence a cornerstone of its efforts to optimise freight models.

AI-enabled digital quote platforms: the example of Kuehne+Nagel

Kuehne+Nagel , the European seaborn and air logistics leader headquartered in Switzerland, in 2019 launched a digital freight platform run by an AI-enabled engine called This offers shippers a simplified quote and comparison system. Once shipment details are known, artificial intelligence simplifies and streamlines the freight cost quote system and offers clients an optimum offer after processing a complex series of data.

Selected shippers possess a dedicated platform allowing them to allocate their capacities optimally. After initially launching it in Thailand in 2019, the Swiss group rolled out in Singapore. When releasing its Q3 2021 results (this month), Kuehne+Nagel reported strong adoption of its IT-enabled platforms, eTrucknow and SeaExplorer, which are more necessary than ever as the Swiss group faces harbour congestion planet-wide – 740 ships, or 12% of its fleet are currently waiting in line to enter their destination ports.

Computer Vision: the ultimate in inspecting goods and unloading cargos

Having a good pair of eyes is essential in logistics inspections, in particular if those eyes are attached to the AI derivatives called machine vision and computer vision. This AI-enabled logistics inspection system allows leaders like DHL to spot and classify transport-caused damage to packages. Similarly, Amazon manages and moves some of its inventories within 30 minutes, down from several hours previously without the use of these technologies. There are several US computer vision and machine vision leaders.

They include companies like IBM Watson (which already has customer references in rail transport), as well as US pure players such as Cognex and Zebra Technologies, both of which are publicly traded. There are also some machine vision modules at large robot and cobot makers, such as Fanuc, for example.