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PartsOS Forecasting
Hymmen: From Manual Planning to Autonomous AI-Driven Spare Parts Management
How Hymmen used PartsOS to reduce manual effort by 80%, eliminate stockouts, and fully automate spare parts planning, without a single IT project.
Hymmen's Achievement with PartsOS So Far
-80%
Less manual planning effort
-20%
Reduced stocks with AI-backed stock optimization
+40%
higher forecasting accuracy in spare parts planning
+20%
higher spare parts availability
The Problem
Hymmen is a leading manufacturer of custom industrial machinery, specializing in wood-based panel and surface technology. The company pursues a consistent service-first strategy, with a clear commitment to supplying customers with spare parts quickly and reliably.
But the reality on the warehouse floor looked different.
With many machines highly customized and no dedicated spare parts planning team in place, procurement decisions were based on experience, gut feeling, and flat-rate safety stock buffers.
The result: stockouts on fast-moving parts and overstocked shelves on slow-moving ones. Confidence in the planning process was low.
Key pain points before PartsOS:
- No data-driven basis for ordering decisions
- High manual effort in spare parts procurement
- Critical part shortages despite high inventory costs
- No dedicated planning team, disposition was handled as a side task
Our Approach
Under the leadership of Michel Ahring, Head of Division Service, Hymmen decided to shift to data-driven spare parts planning.
PartsOS was set up against Hymmen's existing proAlpha ERP data — no complex IT project required. Within just a few days, PartsOS Forecast was analyzing historical demand data and generating demand forecasts for parts, including ones that were frequently sold but not held in stock, surfacing gaps that had gone unnoticed. Hymmen's planning team reviews and releases every forecast before it reaches proAlpha.
The Result
Measurable effects were evident shortly after the go-live in the spring of 2026:
- Manual effort in the spare parts process: −80%
- Inventory levels: −20%
- Forecast accuracy: +40%
- Parts availability: +20%
Since then, critical stockouts have become the exception rather than the rule. Forecasting is automated, the planning team retains responsibility for approvals, and confidence in data-driven decisions has grown noticeably.
FAQs
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What is PartsOS?
PartsOS Forecast is the AI-powered forecasting solution for spare parts teams in mechanical and plant engineering. The system analyzes your historical demand and delivers precise demand forecasts — no IT project required, complementing your existing ERP system such as SAP or proAlpha.
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How long does the ERP integration with ProAlpha take?
Working with proAlpha data typically requires no complex IT project — data is exchanged via a configurable export, not a direct system connection.
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Which companies is PartsOS designed for?
PartsOS Forecast is an AI-powered forecasting solution for spare parts teams in the mechanical engineering sector. The system analyzes your historical demand and delivers precise demand forecasts—without the need for an IT project, serving as a complement to your existing ERP system, such as SAP or proAlpha.
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What results are realistic?
At Hymmen, 80% less manual effort and 40% higher forecast accuracy were achieved within just a few weeks. Exact results depend on the starting point, all customers receive an individual potential analysis before getting started.
More Success Stories
See how other industrial companies have transformed their spare parts planning with PartsCloud.
Coperion
How Coperion used PartsOS to virtually eliminate impending stockouts on critical components, increase parts availability by over 15%, and achieve full ROI within just three months.
EUCO Rail AG
How EUCO Rail is transitioning, together with PartsCloud, from reactive, spreadsheet-based planning to proactive, AI-powered spare parts management, ensuring critical components are reliably available across all locations.