Supply chain · Production

thyssenkrupp Rasselstein uses Celonis to gain end-to-end supply chain transparency and prevent material shortages

The problem

thyssenkrupp Rasselstein had no visibility into parent company Steel Europe's systems and operated across more than 300 internal systems that created data silos, leaving production plans vulnerable to raw material delays and making proactive supply chain management nearly impossible.

Workflow diagram · grounded in source
1
Connect cross-company supply chain data
integration
“connected near real-time data from Steel Europe with Rasselstein's Production Planning systems inside Celonis, creating what the team calls the "connected supply chain"”
2
Single source of truth dashboard
output
“This gives stakeholders a single source of truth for easy-to-action insights. "A few seconds and we have a dashboard to answer all of our questions"”
3
ML delivery date calculation
ai_action
“Celonis machine learning algorithms help teams to more accurately calculate and communicate delivery dates for end customers”
4
Proactive at-risk order steering
human_review
“production planners can proactively steer deliveries from Steel Europe to prioritize orders at risk – before they turn into a problem or materials run out”
5
Automate purchase order creation
integration
“Action Flows are rule-based automations that can trigger actions directly within operational systems like SAP, Oracle, and ServiceNow. Here's how these work at Rasselstein. Pre-Celonis, the team was putting a lot of manual effort into cr…”
Reported outcome

Rasselstein achieved drastically improved delivery date prediction, lower safety stocks, and predicted working capital improvements in the double-digit million range, while automating manual purchase order creation to save effort and raise productivity.

Reported metrics
Working capital improvementsdouble-digit million range
Delivery date prediction accuracyDrastically improved
Safety stock levelsLower safety stocks
On-time delivery predictionExtremely precise
Show all 6 reported metrics
working capital improvementsdouble-digit million range
delivery date prediction accuracyDrastically improved
safety stock levelsLower safety stocks
on-time delivery predictionExtremely precise
manual effort reductionsave manual effort and raise productivity
material shortage preventionProactive Prevention of material shortages
Reported stack
Celonis Process Intelligence PlatformCelonis Action FlowsSAPOracleServiceNow
Source
https://www.celonis.com/solutions/stories/thyssenkrupp-rasselstein-supply-chain
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Rasselstein achieved drastically improved delivery date prediction, lower safety stocks, and predicted working capital improvements in the double-digit million range, while automating manual purchase order creation to…

What tools did this team use?

Celonis Process Intelligence Platform, Celonis Action Flows, SAP, Oracle, ServiceNow.

What results were reported?

Working capital improvements: double-digit million range; Delivery date prediction accuracy: Drastically improved; Safety stock levels: Lower safety stocks; On-time delivery prediction: Extremely precise (source-reported, not independently verified).

How is this supply chain AI workflow structured?

Connect cross-company supply chain data → Single source of truth dashboard → ML delivery date calculation → Proactive at-risk order steering → Automate purchase order creation.

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