Toddlers talk with Alexa, adults ask chatbots what they should cook for dinner. Artificial intelligence (AI) has entered our everyday lives. The remarkable advances of recent months suggest the future will see major changes through AI. It is also opening up entirely new possibilities in intralogistics – enabling greater speed, simplicity and precision. We present some examples in this article.
The more parameters are involved, the more difficult it is to find a solution. And it is in exactly these situations that artificial intelligence (AI) can make a difference. Not because it is more intelligent than people, but because it can consider all factors simultaneously, and therefore makes the right decisions with greater reliability and speed. AI learns almost in real time, and can apply ongoing changes to processes nearly instantaneously. As a result, AI can make the many activities encountered in warehouses simpler and more efficient at high speed.
The ideal solution at the touch of a button
Better timing, shorter distances traveled, fewer mistakes, less void space in parcels, greater load stability on the pallet – AI can deliver concrete improvements and take into account many factors: it can learn from experience, understand voice commands, interpret images and recognize patterns, identify anomalies, and monitor ongoing events, plans and changes. As a result, AI can find the best way to pick and pack goods. And it can calculate the ideal time to order replenishments. And locate the best storage positions. But that is not all.
Machines in the driver’s seat
When tasks performed and paths taken within warehouses are repetitious – for example, when supplying materials to a production department – there is great potential for streamlining the corresponding processes. For instance, instead of having human workers move the same goods from A to B, it is possible to deploy AGVs (automated guided vehicles). Whereas a normal forklift or tow tractor is operated by a person, an AGV uses e.g. automated geonavigation or laser technology. And AI, in conjunction with sensors, ensures safe operation, in line with all rules and regulations.
So – warehouse vehicles capable of operating fully autonomously 24/7, and that know what they have to do? In many instances, it is a vision not yet fully realized. But automated intralogistics devices from Linde Material Handling already offer many remarkable capabilities: they are able to recognize their surroundings with great precision and as collaborative robots can significantly improve warehouse processes – relieving the workload on human workers and improving cost-efficiency. The data generated by these devices are stored and analyzed centrally and used to manage their actions and movements, ensuring they do not get in each other’s way. It is also possible to harness AI for better communication with other warehouse assets, for example roller doors.
Simpler and swifter picking through automation
AI plays a pivotal role in robotics and simplifies many things. Smart machines are able to complete tasks in an instant that would be hard, time-consuming work for people. Above all, this applies to actions that need to be fast, precise and frequent. A robot does not tire and does not lose concentration, so makes few mistakes and requires, apart from occasional maintenance, no breaks. These are ideal capabilities for picking. Equipped with sensors, grippers and a “mind” that can learn on the job, robots, such as those from Dematic, are able to work entirely independently. They handle objects in much the same way as humans would, and improve over time, as they are able to learn from their hits and misses. They are able to identify requested goods, to remove them from a convey belt or a rack, and to pick and collate SKUs to create effective combinations. Robots can therefore offer exceptional precision, reliability and efficiency for seamless and automated around-the-clock operations.
“Some of the biggest software advances in robotics at Dematic are based on artificial intelligence. AI is now available in the form of machine learning and deep learning – technologies that, over time, grant robots ever more dynamism in their area of deployment because they constantly learn and are consequently able to perform their tasks better and better.”
(Crystal Parrott, Vice President of the Robotics Center of Excellence, Dematic).