In today’s globalized world, every business becomes another node in a long and increasingly complex chain. With partners and customers demanding higher efficiency and the competition accelerating, it is more important than ever to look for areas of improvement and adopt a data-driven approach.

According to experts at SSI SCHAEFER, a leading German logistics company, additional pressure is put on the industry by online services that promise consumers benefits like same-day delivery and constant goods availability. That, in turn, encourages the trend of ordering smaller quantities more often.

In the times of economic turmoil caused by unforeseen circumstances, flexibility is the name of the game. This is why artificial intelligence in logistics is gaining momentum as the technology that supports fast decision-making based on valuable experience and real-time data.

AI solutions are being implemented on every level of logistics, from the moment an order is placed to last-mile delivery and customer handling. While far from covering the entire range of available know-hows, this post turns the spotlight on the most impactful applications of cognitive computing in the industry today.

AI in supply chain optimization

Supply chain management workflow

Supply chain management workflow

Exchanges between the members of the supply chain are recorded and become a source of data. Artificial intelligence in logistics thrives on large volumes of data, allowing businesses to leverage the potential they already have to boost productivity and cut costs. A good example of this can be found at the material planning stage. The predictive capabilities of AI can be used to enhance factory scheduling and production planning, which is a task of critical importance for the build-to-order approach.

By calculating storage capacity and predicting demand with a high level of accuracy for extended periods of time, AI logistics is also helping large-scale retailers like Otto avoid delivery bottlenecks and cut delivery times. The algorithms are trained on billions of data points, including previous orders and returns, weather changes, public holidays, and social media trends.

With this information processed into actionable insights, Otto can contact the right suppliers, adjust the number of cargo vehicles, and direct them to locations where they will be needed. A reliable delivery service keeps customers happy, improving the retailer’s reputation. Less packages are returned and less fuel is spent bringing them back, all of which reflects positively on the retailer’s bottom line.

The caveat here is that the data in the logistics sector is oftentimes incomplete and comes from a great variety of sources. Supply chain transparency and the ensuing lack of clean data is an issue for many logistics companies trying to enrich their workflows with data-driven technology. Data cleansing and data integration emerge as prerequisites for digital transformation in logistics, followed by specialized AI solutions designed to create viable data sets from incomplete and unstructured records.

Smart warehousing

Warehouse management has become one of the hotspots for AI-driven optimization. Even the smallest time and efficiency gains in fulfillment or stock tracking become significant when scaled to the entire network.

While AI tools are being used in warehouse design and labor management, the biggest trend in smart warehousing today is robotics. Self-driving robots locate and move inventory in warehouses, track items, sort packages, and box customer orders. The sophistication and accessibility of such systems is growing. Robots are able to perform complex tasks with increasing speed and dexterity, with artificial intelligence guiding their actions and creating optimal strategies for the arrangement and maintenance of goods.

Toyota warehouse robotics

In some places, robots are used to carry out high-risk tasks instead of human workers. In others, AI works alongside humans and analyzes their activities with computer vision tools to determine best practices and implement them more efficiently across the entire operation.

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Artificial intelligence in transportation

Fully autonomous trucks are still a remote concept, just like cargo delivery drones. However, AI in transportation is already being used to facilitate the daily routine of drivers with features like lane-assist, assisted braking, and highway autopilot.

On top of that, companies benefit from applying AI in transportation to optimize the routes for their fleet using weather and traffic conditions data. For example, UPS manages to save 10 million gallons of fuel annually just thanks to route optimization. In a method called platooning, technology helps multiple trucks to drive efficiently in formation to avoid accidents and lower fuel consumption.

IBM applying AI in transportation

Source: IBM
IBM applying AI in transportation

Elsewhere, DHL is using computer vision-assisted AI in transportation to visually inspect packages. The technology powered by IBM is installed along train tracks to assess damaged train wagons, determine damage type, and recommend necessary actions to maintenance teams on-the-fly.

The bright future of AI logistics

Logistics is still trailing behind other industries where digitalization is concerned, but it’s also an industry ripe for change. Considering the scale of the challenge and the low level of data integrity many companies start off with, artificial intelligence in logistics is just the right technology for this disruption.

Trends like anticipatory logistics, automated warehousing, intelligent fleet management, and computer vision inspection are set to greatly increase productivity and increase value for every member of the supply chain.

At the moment the examples of AI in logistics are for the most part isolated instances, like AI in supply chain management systems. In the future such systems will become more and more widespread and integrated, creating flexible AI-based ecosystems of product manufacturers, cargo carriers, freight forwarders, and distributors.