Material handling / intralogistics
How a global material handling manufacturer unlocks up to €700,000 in annual savings potential with AI agents
This case study shows how targeted use of AI agents turns previously untapped savings potential into real results – with minimal effort and maximum impact. The pragmatic combination of intelligent automation and human approval is what makes the difference.

Customer
Material Handling Manufacturer
Industry
Material handling / intralogistics
Result
€350,000–700,000 savings p.a.
About the company
This case study is about a global leader in material handling solutions headquartered in Europe. The group operates >15 plants worldwide and runs a central procurement strategy for both production and non-production material. Part of that focus is the so-called long tail – a large number of smaller suppliers with low purchasing volume, where conventional negotiations are often skipped.
The challenge
Existing spend analytics identify savings potential through proactive notifications (e.g. when raw material prices change) that had never been acted on.
More than 400 C-suppliers with small purchasing volumes (under €50k p.a.) go untouched for lack of capacity.
Manual negotiations don't scale, even though the data is there.
No standardized way of realizing the identified potential.
Buyers' strategic focus leaves no room to also work through C-suppliers.
On many orders, C-suppliers deviate from negotiated payment terms or Incoterms.
The insight: The long tail in particular holds substantial savings potential – but it can only be captured through consistent automation.
The solution
Together with Zalion, the company introduced an AI agent to prepare negotiations with C-suppliers automatically and carry them out efficiently – based on existing spend data, without deep IT integration. The focus is on price adjustments and payment terms.
Identifying potential – The agent automatically analyzes and filters incoming savings opportunities for relevance and feasibility. Once prioritized, negotiation approaches are identified using a library of 13 levers. It also automatically flags any deviation from payment terms or Incoterms.
Preparing negotiation emails – Based on the analysis, the agent generates negotiation-ready email drafts that buyers only need to send.
Focus on untouched C-suppliers – Applied specifically to low-volume suppliers that had never been negotiated with before.
Human approval – Nothing is sent fully automatically – buyers review and send the emails, which increases acceptance and safety and provides valuable feedback.
Good to know: The rollout started in three pilot plants and is now being extended step by step to all 17 plants and to further supplier groups (B-suppliers, non-production material) – with no additional IT interfaces or drawn-out rollouts.
Results
5–10% savings with C-suppliers
€350,000–700,000 annual potential
No additional workload for buyers
Standardized negotiations at the push of a button
100% compliance for payment terms and Incoterms
Conclusion
This case study shows how targeted use of AI agents turns previously untapped savings potential into real results – with minimal effort and maximum impact. The pragmatic combination of intelligent automation and human approval is what makes the difference.
“
With the AI agent we're finally unlocking the potential our spend analyses have been pointing to for years – fast, scalable and with no extra effort for our team.
VP Global Purchasing
Material Handling Manufacturer
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