How artificial intelligence optimizes assembly intralogistics through machine learning

Digitalization has made significant progress in recent years - in all areas of the economy. The main reason for this is, of course, the constantly increasing amount of data that needs to be processed. In this context, the topic of machine learning in assembly intralogistics is becoming increasingly important

<p><em><strong>In the fourth industrial revolution, machines, employees and production sites are digitally networked with each other. This takes Industry 4.0 production to a new level in order to improve manufacturing processes efficiently, purposefully and successfully.</strong></em></p>

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This is where Artificial Intelligence - AI for short - comes into play to capture various correlations from enormous amounts of data so that subsequent programming and thus additional effort can be minimized. montratec offers numerous technical solutions for this.

Artificial intelligence and logistics - what impact does it have?

Artificial intelligence has had a major impact on the logistics industry in recent years. Warehouse technology and management, in particular, has recently benefited significantly from automation with the help of AI. For example, in smart warehousing, the IIoT (Industrial Internet of Things) is playing an increasingly important role, as it allows all components to be digitally linked together

Artificial intelligence can help to optimize and shorten work processes, also advance order picking through the corresponding pre-calculation of all required materials, and also significantly simplify inventory management. Thanks to improved networking, productivity can be sustainably increased. Because resource planning can be directly controlled by AI, personnel can be reduced in this way - which saves a lot of costs. montratec can advise you comprehensively in this regard and provide you with suitable, tailored solutions.

How does machine learning work in assembly intralogistics?

Machine learning uses powerful algorithms that analyze and process large amounts of data to learn from it. Here, software collects examples and can recognize and filter out recurring processes and patterns based on self-learning algorithms. This software gradually receives new data sets and corresponding feedback from programmers, who can continually optimize the algorithm. The following applies here in principle

The more data that can be collected and processed by the algorithm, the more intelligent the system becomes over time

In general, a distinction is made between two variants of machine learning in assembly intralogistics when providing data. There is supervised and unsupervised machine learning. Both methods focus on recognizing recurring processes and correlations and drawing conclusions for the future so that the system can create forecasts for various scenarios in the future.

In principle, machine learning is only a collective term that describes the task of supporting machines in finding solutions by enabling them to learn from existing data sets. This can also be a simple algorithm. It is, therefore a matter of the software or the machine being able to adapt independently to different circumstances and situations and to update itself on the basis of the available data continually.

So what role does machine learning play in intralogistics?

In today's world, many companies are asking themselves how intralogistics can be made increasingly efficient in the coming years. Sustainable process optimization requires reliable and correct master data so that the potential of machine learning can be used in the best possible way. Even in picking and transport planning, AI-based systems can use machine learning to collect and process vast amounts of data to derive recurring patterns.

With the help of machine learning, material transports as well as the duration for loading or unloading can be planned and predicted much more accurately

Flat times are thus a thing of the past, as is still the case today in many logistics companies when transporting pallets, for example. Machine learning independently recognizes the products to be loaded and can correctly plan the scheduling accordingly - of course, other factors such as the available personnel or even special days of the week are also taken into account.

In this way, machine learning can help to make processes much more efficient and avoid bottlenecks altogether. The advantages also come into play when unexpected disruptions or failures occur in usual operating processes, such as a delayed delivery due to a production stop. Here, a self-learning system can recognize on its own which measures are required to continue the processes and solve the problem.

Why predictive maintenance in assembly intralogistics can offer a major advantage in the market

Competitive advantage is huge in the logistics industry these days, as the market is highly competitive. Companies that are to gain them can do so through Predictive Maintenance. Here, analytical applications in machine learning support personnel in determining the maintenance and servicing dates for vehicle fleets and technology precisely in advance, thus ensuring optimal resource planning. In this way, machine idle times can be completely avoided and costs saved

In fact, predictive maintenance is not only important for the logistics industry, but also for other economic fields, such as transportation.

Likewise, machine learning offers optimized image recognition - this tool is particularly valuable when online trade is growing strongly. In this way, mail order companies benefit from optimal inventory management. Capture and scanning processes are based on intelligent image recognition and can continuously keep an eye on the current stock on the shelves as well as the product quality. Furthermore, they help to determine the location of certain materials or to find out where on the premises certain people or machines are currently located. If the worst comes to the worst, this can even prevent accidents in the warehouse.

Where machine learning can be used in intralogistics

Artificial intelligence with machine learning has long since ceased to be a niche product and has become an important tool for companies in the context of supply chains. It can also be used to uncover supply chain complications before they cause disruptions or delays in operations. If the right algorithms are used, the possibilities for exploitation are almost endless

For example, it is possible to reduce freight costs by identifying specific synergies in shipping networks, as well as to increase the just-in-time reliability of suppliers. Overall, the economic risk can be drastically reduced

Especially in supply chain management, a lack of synchronization or even the complete failure of individual instances within a supply chain is fatal and often leads to high, completely unnecessary costs.

By networking different algorithms in supervised and unsupervised machine learning, it is possible to identify those factors that have the greatest influence on the supply chain. Based on this information, appropriate safety precautions or preventive measures can then be defined.

Another classic application of Artificial Intelligence with Machine Learning is, among others, the reduction of inventory or operating costs as well as the forecast of demand for certain products. Thanks to the precise planning of upcoming maintenance, the

Deep Learning and Artificial Intelligence with Machine Learning - what is the difference?

In Deep Learning, Artificial Neural Networks are used for the independent processing of information or for the detection of patterns. Artificial Neural Networks (KNN) are special algorithms that have been designed based on the way the human brain works. Due to the huge increase in digital data in recent years, Deep Learning has become increasingly important - this is where the system is also fed information. These pass through a grid of artificial neurons in several layers, such as the input layer, the hidden layers and the output layer, among others

In general, the more complex the hidden layer, the deeper the learning process.

Deep learning is heavily computational. Training can take several months to be able to make accurate predictions afterward. Thanks to Deep Learning, conflicts in the area of text, image and speech recognition can be solved - this is not possible without a special algorithm. Especially with a large amount of data (Big Data), Deep Learning is much more efficient than Machine Learning with a traditional algorithm

The biggest difference between classical machine learning and deep learning is the ability to analyze data without structure using artificial neural networks.


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.