Fischer TireTech: From 75 Days Delivery Time to Under 30 with AI-Powered Spare Parts Planning

PartsOS Planning

Fischer TireTech: From 75 Days Delivery Time to Under 30 with AI-Powered Spare Parts Planning

How Fischer TireTech used PartsOS Planning to reduce 60% of orders to under 30 days delivery time, increase spare parts availability by nearly 80%, all without reimplementing their ERP.

DRAG

> -50%

of orders saw delivery times reduced from 75 days to under 30 days through AI-powered spare parts planning

+10%

increase in spare parts revenue driven by higher parts availability in mechanical engineering

+3%

increase in machine sales through improved customer trust in spare parts supply

The Problem

Fischer TireTech is the world market leader in the manufacture of tire production equipment, supplying customers worldwide with highly specialized machines and systems. For decades, the company's strong market position had kept it in a comfortable place: with many spare parts being proprietary and exclusive, delivery speed had never been a decisive competitive factor.

That changed fundamentally. With increasing competitive pressure from Asia and South America, customer expectations around service quality and delivery times rose sharply. Customers who once waited for parts now simply choose the faster alternative supplier. Fischer TireTech had no structural answer to this challenge.

The starting point in numbers:
Average delivery time: often over 75 days
No central spare parts warehouse
No clear inventory strategies or reorder points
ERP fully configured for production — no separate planning logic for service processes
No visibility into availability or demand
No dedicated spare parts planning team

The result: dissatisfied customers, lost after-sales revenue, and a structural problem that could not be solved with the existing ERP setup alone.

Our Approach

Together with Fischer TireTech, PartsCloud developed an integrated approach that enabled the transition from production-oriented to service-oriented spare parts planning, without any ERP reimplementation.

Three core building blocks of the solution:

  1. AI-Based Forecasting and Stockflow Engine
    The PartsOS forecasting and stockflow engine was integrated into the existing ERP setup. The system automatically translates demand signals into concrete safety stock levels and reorder points, passing these directly to the ERP, which processes the recommendations automatically.
  2. Phased Rollout from Semi-Manual to Fully Automated
    The implementation took place in two phases. First, semi-manual: planners reviewed and approved AI suggestions. Then, the system was upgraded to a deeper ERP integration with automated purchase order recommendations. This approach maximized user acceptance and avoided any planning disruptions during the transition.
  3. Fully Autonomous Agent Mode
    Today, Fischer TireTech runs in Agent Mode: PartsOS independently generates order recommendations, triggers required actions directly in the ERP, and keeps spare parts supply running continuously, without a dedicated planning team and without manual intervention.

The result

Just months after go-live, results were measurable across all dimensions:

  • Delivery time: −60%
  • Spare parts availability: +~80%
  • Spare parts revenue: +10%
  • Machine sales: +3%

That last point is remarkable: more machine sales driven by greater customer confidence in spare parts supply. It shows that spare parts planning is not just a logistics topic, it is a sales argument.

Fischer TireTech has made the shift from reactive spare parts planning to proactive availability. The combination of data-driven forecasting, seamless ERP integration, and strong user adoption made this possible in a short time, with measurable after-sales revenue growth to show for it.

"We greatly appreciate the PartsCloud team’s deep mechanical engineering expertise and hands-on experience. They quickly understood our specific challenges and translated them into a smart solution that fits perfectly within our limited ERP setup. With PartsOS, planning now runs automatically and reliably, giving me personally a real peace of mind.”

Rüdiger Stanzel

Head of Aftersales

FAQs

  • How long were delivery times at Fischer TireTech before implementing PartsCloud?

    Before PartsOS Planning, average delivery times at Fischer TireTech were often over 75 days. There was no central spare parts warehouse, no dedicated planning system, and no planning team. The ERP was set up exclusively for production, not for after-sales service.

  • What results did Fischer TireTech achieve with PartsOS Planning?

    After implementing PartsOS Planning, 80% of orders were reduced to under 30 days delivery time. Spare parts availability increased by nearly 80%, spare parts revenue grew by 10%, and, remarkably, machine sales increased by 3%, as customers regained confidence in spare parts supply.

  • Did Fischer TireTech need to reimplement their ERP to use PartsOS?

    No. PartsOS Planning was integrated directly into the existing ERP setup without any reimplementation. The rollout was phased: first semi-manual, then fully automated in Agent Mode, with no planning disruptions and no dedicated planning team required.

  • What is Agent Mode and how does Fischer TireTech use it?

    In Agent Mode, PartsOS operates fully autonomously: it independently generates order recommendations, triggers required actions directly in the ERP, and keeps spare parts supply running continuously, without manual intervention. Fischer TireTech runs PartsOS entirely in Agent Mode, with no dedicated planning team.

  • Why did Fischer TireTech have a delivery time problem despite being a world market leader?

    Fischer TireTech is the world market leader in the manufacture of tire production equipment. Its strong market position had long masked the fact that its spare parts planning were not structurally designed for fast delivery. As competitive pressure from Asia and South America intensified, long lead times became a critical disadvantage and PartsCloud provided the solution.

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