Breaking Down Barriers to Implement AI

An impressive example of an AI-driven solution

Martin Friedrich, an AI trainer at the SME Digital Centre Ruhr-OWL, describes one positive example: the implementation of AI-driven solutions in the field of container management at Mühlhoff Umformtechnik GmbH. By using advanced technologies to optimize their logistics processes, not only did the automotive supplier company from Uedem am Niederrhein increase efficiency, they also improved transparency and traceability throughout the entire production process. Container management at Mühlhoff encompasses a wide range of processes – from storage and transportation to tracking and managing containers. In the past, this has typically been a labor-intensive and error-prone field. “With the introduction of artificial intelligence (AI), this has fundamentally changed. AI-driven systems analyze large quantities of data in real time and provide accurate predictions regarding the demand for and availability of containers. Machine learning algorithms can detect patterns and anomalies that human eyes often miss,” says Martin Friedrich. This enables proactive planning and reduces bottlenecks and overstocking. The mplementation of AI technology at Mühlhoff has already yielded some remarkable results. The efficiency of its container logistics has increased significantly, resulting in cost savings and greater customer satisfaction. In addition, by optimizing transportation routes and reducing the number of empty runs, the company has minimized its environmental impact. “Overall, Mühlhoff Umformtechnik GmbH is an impressive example of how AI-driven solutions can revolutionize container management,” Friedrich adds. “By combining innovative technology and well-founded industry knowledge, the company is setting new standards in industrial logistics.”

AI-optimized delivery time forecasts 

The haulage and logistics company ECS GmbH is also on the road to implementing a digitalized arrival time forecast system. As part of a transfer project with the SME Digital Centre Ruhr-OWL, the medium-sized company implemented an AI-driven ETA service for calculating transportation routes and forecasting the expected arrival and departure times. “We’ve been working closely with the company managers throughout the entire project,” says Martin Friedrich. The researchers recorded and analyzed the processes at the Kreuztal-based transportation company. The goal was to optimize delivery time forecasts using AI-driven software, simplify workflows and improve customer service. In order to achieve this, the SME Digital Centre Ruhr-OWL used the findings from an ETA service developed in Fraunhofer IML’s Silicon Economy initiative. “Here we are developing an open source infrastructure for the platform economy of the future,” explains Helena Piastowski. “The ETA service is a wonderful example of how basic AI-driven functions can be made easy to access and use in a financially viable way thanks to open source systems,” she adds.

Josef Kamphues believes that two factors are essential for successfully applying AI: “First of all, when considering AI applications, we need to start with the use case and work backwards. That’s why it’s so important to begin by thoroughly analyzing the specific application options at each company. And secondly, companies should not be afraid to draw on the experience of other companies and leverage the expertise of research institutions and innovative start-ups. This kind of dialogue and collaboration is precisely what we need to avoid some stumbling blocks and thus achieve the desired results from the project in question more quickly.”