Use Cases Concerning AI in Warehouse Logistics – How Artificial Intelligence Increases Efficiency
June 29, 2023 | Reading time: Approx. 7 minutes
Dual students at FIS
Alexa, how do I optimize my logistics processes? In this way or other, presumably everybody has already used artificial intelligence. AI does not only provide support in private life. It also offers various application options in the field of warehouse logistics. This article describes these options and how they can be used.
Everyone should be aware that artificial intelligence is no longer just a trend but has become the norm.
Be it language assistants such as Alexa, Siri and Co. or the face recognition of modern smartphones. However, these little helpers in everyday life are only the beginning: AI has already entered warehouse and transport logistics and has long been used in many fields.
Due to the large availability of data, the logistics industry in particular offers many use cases for the application of artificial intelligence.
From goods receipt and processes within the warehouse to goods issue: a multitude of data is generated and processed, be it information on pick paths or times, material data such as weight, measurement or position of the product.
According to a survey conducted on behalf of the Digitalverband Bitkom, two thirds of the companies polled believe that AI-based systems can do a lot of logistics work in the future. However, the full potential of these technologies is still far from being exhausted: only 6 % of the companies have already used AI actively. (cf. “Digitization of Logistics” Bitkom e.V., 2019)
Artificial intelligence in warehouse logistics
Every day, large datasets available in different forms are generated in the warehouse logistics area of a company. This mass of information is the perfect basis for the application of artificial intelligence.
AI enables the development of new potentials within the scope of various application options. This includes, above all, the acceleration of logistics processes or the reduction of errors.
Moreover, AI is also a game changer for logistics planning. Future demands, for instance, can be forecast or capacities optimally planned. Altogether, the use of AI enables a more flexible and cost-efficient way of working.
The following use cases describe specific AI applications in the field of warehouse logistics:
Drive units
Within the scope of warehouse logistics, “drive units” are used to execute routine tasks.
The autonomously driving vehicles are guided via QR codes on the ground. Here, a centrally controlled AI can be used to determine the most efficient route for each vehicle so that they can maneuver through the warehouse without any problems. This procedure simplifies the transportation of goods to the different warehouse zones.
However, time cannot only be saved by calculating the most efficient route. The fact that drive units are not bound to working times also ensures a constant flow of materials.
Quality assurance
Continued quality assurance within the warehouse, such as the control of incoming goods, is very time-consuming. The warehouse workers often cannot see all defects with the naked eye.
The use of AI trained for the recognition of articles makes quality assurance more precise and faster.
For this purpose, AI checks whether articles show metal impurities, scratches or similar defects. Advanced image recognition software can also be used to examine goods with complex surface quality more thoroughly than would be possible with the human eye.
In this way, AI helps put away defective materials or remove them from stock and open up time and cost potentials.
Pick by voice
Artificial intelligence can also provide support when it comes to the picking of goods. Goods to be picked as well as their corresponding storage bin can be queried via language assistant without carrying along a picklist. As a result, pick by voice can be used to directly communicate with the EPR system.
The language assistant can be contacted by either wearing a vest with built-in microphone and loudspeaker or using a mobile headset.
Any information needed by an employee can be obtained from AI on request. This does not only simplify the pick process but also the initial job training of new employees as all information can be queried via the assistant.
AI-supported gripper arm
AI-based robot gripper arms can also be applied in pick processes. Perhaps you already transport picked articles on conveyor belts through the warehouse. A robot gripper arm then distributes them to secondary conveyor belts.
However, some articles or materials are sensitive or fragile such as glass or ceramic. A conventional gripper arm would destroy such articles as the goods do not sustain the high force of the arm.
This is where AI comes into play: via reinforcement learning, it can train the gripper arm on how to grip specific articles without deforming or destroying them. After a training phase, the robot knows all products and is able to handle them accordingly.
Packaging
Before being loaded on the truck, the picked goods have to be packed correspondingly. Here, it must be ensured that as little material as possible is used. Information such as dimensions, weight and other important data required for packaging the articles is defined in the ERP system. It is the basis for the selection of the appropriate packaging.
This process as well can be optimized using AI. Example: A 3D scanner measures the article and proposes a cardboard box as well as the required amount of adhesive tape on the basis of its experience through training data.
If several articles are to be sent in one container, the AI algorithm calculates the appropriate total packaging on the basis of size, form and weight.
Conclusion
In most cases, the advantage resulting from the use cases is a reduction of time expenditure through which significant cost savings can already be achieved in the short term.
However, training the AI solution at first should not be neglected. This requires controlling master data of high quality. If this cannot be ensured, you will not benefit from the aforementioned advantages.
Therefore, you should definitely check whether the investment in AI is worthwhile for you and whether the advantages of the use cases outweigh the disadvantages. As a general rule, however, the use of AI in warehouse logistics plays a more and more important role in the future and many other use cases are to be expected.
Questions about this topic? Our team will be happy to assist you personally.
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