Components and raw materials are a significant cost item for manufacturing companies. The faster they are processed and delivered to customers, the faster they turn from costs into revenue. This makes it all the more important to keep their dwell time in the warehouse and production as short as possible. Elisa Industriq has identified three steps that artificial intelligence can take to make material flows more transparent and efficient.

Exploding material costs, a shortage of skilled workers and increasing price pressure from customers are forcing companies to permanently optimize their processes. A decisive lever for efficiency and competitiveness lies in material flows. Artificial intelligence (AI) opens up new possibilities: It analyzes data from different sources, makes recommendations for improvements and implements them independently if desired. Elisa Industriq, a provider of software for operational intelligence, outlines three practical steps for optimizing material movements with AI - from transparency in the material flow to warehouse logistics and inventory planning.

Creating clarity about material flows

Until now, companies have found it difficult to track exactly where a workpiece or assembly is in production and when. This was often only possible with a great deal of manual effort. A precise answer to the question "How much of a certain raw material is still in stock at the moment?" was also virtually impossible. The relevant data was either not available for all process steps or was distributed across different systems, for example in manufacturing execution systems (MES), ERP or warehouse management systems (WMS). Today, AI solutions provide the necessary overview. This is because they access the warehouse management system and other applications, analyze the relevant data and identify which object is currently at which point in the process. Such a real-time monitor for the material flow forms the basis for identifying optimization potential in the processes.

Optimizing intralogistics

Michael Fatum, Managing Director of Elisa Industriq in Germany. © Elisa Industriq

If companies know where raw materials, components and end products are located and when, they can work on shortening transport routes and reducing transport times. For example, driverless transport systems and robots are already moving materials from one production step to the next in many companies today. However, the control systems in the background are often not connected to each other. If a transfer is necessary, for example from an automated guided vehicle to a robot, the synchronization between their control systems is usually done manually. This takes time and is prone to errors. With artificial intelligence, these material flow systems can be synchronized automatically. The result: the transport systems and robots independently organize the exchange of components with each other and the process is accelerated.

AI systems also analyze the movement and access patterns in the warehouse: materials with a high turnover rate are then automatically stored close to the picking or production areas. This also saves distances, reduces picking times and increases efficiency.

Plan material quantities with foresight

The entire stock of a particular material is not always in one place in the warehouse. Often a small part of the stock is located close to production, while the larger part is stored in an external warehouse, for example. If the stock near production drops, replenishment is taken from the external warehouse. This is usually done on the basis of observations and empirical values. Industry-specific AI solutions help to plan ahead more precisely. They analyze company-wide material movements in real time and determine typical consumption patterns. They also take orders and production plans into account. In addition to historical data, they also incorporate current data, such as geopolitical events or climate change, into the resulting demand forecasts - which significantly increases the accuracy of the forecasts. As a result, the artificial intelligence recognizes potential bottlenecks at an early stage and can suggest or even trigger automatic stock transfers or repeat orders - even before there is a risk of production downtime.

"AI speeds up processes and helps to reduce costs. In a study by the German Economic Institute, more than 80 percent of companies reported savings through AI, averaging 13 percent. And that's just the beginning," says Michael Fatum, Managing Director of Elisa Industriq in Germany. "Those who optimize their material flows with AI now still have the chance to secure their own competitiveness."