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ThyssenKrupp Rasselstein in Andernach, Germany is one of the largest manufacturers of packaging steel in Europe. The company, with around 2,400 employees (as of 2023), operates in a market segment that is characterized by a high level of complexity and enormous cost and competitive pressure. To address the complexity problem and reduce costs, the company is pushing ahead with digital transformation and process optimization, in particular the development of a data-supported, networked supply chain. The steel manufacturer is using the process intelligence platform from German provider Celonis for this purpose.

The process intelligence platform combines process data with business context to create a digital twin of the steel company’s supply chain. This is done with the help of process mining and artificial intelligence. Celonis is now used in over ten processes at Thyssenkrupp Rasselstein and supports more than 50 use cases. These include procure-to-pay (P2P), order-to-cash (O2C), inventory and warehouse management and receivables management.

Gaining a centralized view of processes

“Before we implemented Celonis, we had a limited overview of the processes along the supply chain. This made it difficult to react quickly to bottlenecks,” explains , Chief Financial Officer of Thyssenkrupp Rasselstein. “Today, we have a real-time view of our processes, which allows us to intervene in the event of problems, reduce excess stock and increase delivery reliability. This allows us to make more informed strategic decisions and ensure seamless production planning.”

The digital twin links data from parent company Thyssenkrupp Steel Europe with Thyssenkrupp Rasselstein’s internal systems for production planning. At the Andernach production site, the largest of its kind in the world, more than 300 systems work together. This high level of complexity creates the risk of fragmented processes. Furthermore, many central processes are closely integrated with the parent company, which supplies the majority of raw materials. The digital twin creates a central database that provides all users with well-founded, easily implementable recommendations for action. 

Optimizing sales and avoiding waste

The platform’s AI-powered Process Copilot enables employees to leverage data and make decisions faster, even without in-depth analytics expertise. For example, the Process Copilot provides near-real-time information on the availability of production materials at manufacturing sites.

Additionally, Thyssenkrupp Rasselstein uses Celonis’ Process Orchestration feature to minimize waste from unsold materials. The AI Annotation Builder automatically identifies potential buyers for surplus inventory, with parallel employee validation. The Orchestration Engine then creates structured email offers that customers can accept, decline or adjust with a click. Their feedback is automatically captured, categorized and analyzed.

Benefits and next steps

With Celonis, Thyssenkrupp Rasselstein is able to monitor material flows in real time and identify potential risks early on. This allows, for example, material shortages to be proactively avoided. Further benefits include improved delivery reliability and more precise forecasts. This, in turn, increases customer satisfaction. Thanks to the real-time view of the supply chain, the company can also reduce material overstocks without compromising production reliability.

The manufacturer managed to save tens of millions of euros in working capital thanks to more efficient inventory management and streamlined financial processes. By using Celonis Action Flows, routine tasks such as creating purchase orders can be automated. “Celonis brings digitalization from IT to the business departments – where it belongs,” summarizes Michael Pullen.

Next, Thyssenkrupp Rasselstein is preparing to introduce Object-Centric Process Mining (OCPM) to enable a more connected, end-to-end view of the entire supply chain. This, in turn, will form the basis for further measures that will enable the steel producer to further increase transparency and operational efficiency.

This article . It was machine translated using DeepL and Google Translate and edited for clarity.

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