Artificial intelligence and machine learning in assembly intralogistics: both are becoming increasingly important.
Not only in process optimization but also to support the location of products in warehouses, artificial intelligence can be an enormous help. The more data available, the better the system is able to learn and thus provide for future optimizations and improvements in processes.
The use of machine learning in assembly intralogistics ensures analytical forecasts for, among other things, staffing requirements, market demand, or even returns processing. These predictions are therefore not only based on the available data, but also on recurring patterns that emerge over time through machine learning.
As things stand, artificial intelligence and machine learning have so far been used primarily in larger companies. Medium-sized and small companies often find it more difficult to integrate new technologies and do not see any potential in artificial intelligence or machine learning for their own processes. Nevertheless, it is worth taking a look at this advanced technology, because self-learning algorithms are ideal for increasing efficiency in assembly intralogistics. Ultimately, this is the way to create the basis for a continuously traceable, digitally controlled supply chain. And this is one of the most important criteria for companies in every dimension to improve the quality of deliveries and drive process optimization.