Eberspächer Group

The Eberspächer Group, a globally leading system partner in the automotive industry specializing in exhaust technology, vehicle heating, and climate systems, operates supply chains of significant complexity across its international footprint. Managing a broad product portfolio with highly diverse planning requirements stretched analytical resources thin, making consistent, data-driven demand planning difficult at scale. Manual forecasting processes consumed planner time that could otherwise support strategic decisions, while forecast errors translated directly into either excess inventory tying up working capital or shortfalls that risked disrupting automotive customers with zero tolerance for delivery failures.

Eberspächer implemented pacemaker.ai's AI-powered Demand Forecasting solution — a platform built on machine learning algorithms that generate forecasts by modeling internal factors alongside time series data on delivery history. The implementation followed an agile, collaborative structure: problem identification originated within an internal Innovators Challenge, followed by roughly three months of technical exchanges before the formal onboarding kickoff. From that kickoff, both teams worked in close partnership to reach production deployment in just five weeks. The solution automated analytical processes across the full product portfolio, integrating directly into existing supply chain planning workflows and eliminating the manual data work that had previously consumed planner bandwidth. pacemaker.ai, a thyssenkrupp subsidiary holding ISO 27001 certification, provided both the platform and implementation expertise.

The deployment delivered measurable improvements across the core supply chain planning objectives identified at project outset:

• Forecast accuracy: Improved across all items in the product portfolio through ML-driven automation replacing manual analysis
• Inventory reduction: Lower inventory levels achieved as a direct consequence of more reliable demand signals
• Planner productivity: Teams freed from routine analytical tasks to redirect effort toward strategic supply chain decisions

Tatjana Sauter, Director Supply Chain Management at Eberspächer, confirmed the dual benefit: improved service levels and better working capital utilization delivered simultaneously. The five-week implementation timeline itself demonstrated that enterprise-grade AI deployment need not require multi-quarter programs.

• De-risk adoption with a scoped value case first: Eberspächer's internal Innovators Challenge identified clear optimization potential before any vendor engagement, accelerating stakeholder buy-in and keeping scope focused.
• Pre-kickoff technical alignment compresses deployment time: Three months of informal technical exchanges before the formal kickoff meant the five-week go-live was realistic, not rushed.
• Agile, equal-terms collaboration between vendor and customer is a deployment accelerator: Open communication and shared commitment — not just vendor-led implementation — drove the speed.
• Demand forecasting improvements compound: Better forecast accuracy reduces inventory and improves service levels simultaneously, making it one of the highest-ROI entry points for AI in supply chain.