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.

Material Handling Manufacturer

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.

  1. 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.

  2. Preparing negotiation emails – Based on the analysis, the agent generates negotiation-ready email drafts that buyers only need to send.

  3. Focus on untouched C-suppliers – Applied specifically to low-volume suppliers that had never been negotiated with before.

  4. 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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