The Problem
Excel and SAP see what happened yesterday, not what will be needed tomorrow
Spare parts planning is the backbone of every profitable service business. While some mechanical engineering companies have already reduced missing parts by 30%, others are still struggling with the same symptoms: missing parts in service alongside overstocked warehouses, manual forecasts that nobody trusts, and a service team constantly working in crisis mode.
The root cause is structural, not organizational.
Three voices from practice describe the problem precisely
Behind these points of frustration lie four structural failure points of traditional planning
Why the after-sales business in mechanical engineering has its own planning problem
After-sales still runs alongside the core business in many companies, not with the same planning quality as the new machinery business. Yet complexity is continuously growing: mechanical engineering companies today support old and new generations simultaneously, often across more than 30 years of product history. Global markets are fragmented, with each region having different requirements. And spare parts are highly individual and sporadic, exactly what Excel and standard SAP tools are structurally unable to handle.
Without AI-supported spare parts planning, costs of >€3 million/year arise according to PartsCloud analysis, through missing parts, customer downtime, lost orders, and reputational damage. On top of that comes a know-how problem: hardly any company has built an internal team for professional spare parts planning.
AI-supported demand forecasting closes exactly this gap. It calculates demand even for long-tail parts with sporadic demand, gives every part a clean forecast, and automatically translates that into order recommendations.
The result: 100% of parts are planned, not just the A-parts that the planner keeps track of in their head.
Rüdiger Stanzel
Unternehmen wie Hymmen, Weinig, Coperion und Fischer TireTech planen heute mit PartsOS und liefern zuverlässiger als je zuvor. In 2–3 Monaten produktiv, ohne IT-Projekt.
👉 Download the full Whitepaper
Learn how to transition from reactive Excel planning to data-driven spare parts planning with AI. Productive in 2–3 months, without an IT project.
- Why spare parts planning with Excel and standard SAP tools fails with sporadic demand
- How AI-supported demand forecasting increases spare parts availability by 15% and reduces inventory costs by 20%
- What mechanical engineering companies like Hymmen, Weinig, and Coperion do differently with PartsOS Planning and what ROI they achieve
- ROI calculation and concrete project examples from ongoing customer projects
More Insights
Explore more case studies on industry-specific spare parts planning.
AI-Powered Spare Parts Planning For Commercial Vehicle OEMs
How commercial vehicle manufacturers use AI-driven spare parts planning to reduce costs, centrally manage dealer network inventory, and prevent truck downtime achieving 5–10x ROI per year with 20% lower inventory levels.
AI-Powered Spare Parts Planning For Construction and Agricultural Equipment
How to remain able to deliver even during peak season with AI planning that anticipates sporadic demand and seasonal peaks.
FAQs
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Why does spare parts planning with Excel and SAP fail in mechanical engineering?
Excel and standard SAP functionality break down when managing thousands of SKUs with sporadic, intermittent demand patterns. The result: out-of-stock parts in the field and overstocked shelves in the warehouse, a classic problem for After-Sales managers in mechanical engineering.
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How much inventory cost can AI-powered demand forecasting save in mechanical engineering?
Machine builders achieve on average 20% lower inventory holding costs with AI-based spare parts planning, while simultaneously increasing parts availability by 15% and without any major IT project.
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How long does it take to implement an AI solution for spare parts planning?
With PartsOS Planning, mechanical engineering companies are typically live within 2–3 months. No IT project is required getting started is possible even without an ERP integration.
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Which machine builders are already successfully using AI in spare parts planning?
Companies like Hymmen, Weinig, and Coperion already rely on AI-powered demand forecasting with PartsOS Planning, reducing up to 90% of their manual planning workload.