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Date

23.04.2026

Category

News

Author

Savannah Reif-Romero; Laxmi May

#Blog

Machine downtime costs up to €20,000 per hour, why does it keep happening again and again

A machine downtime caused by a missing spare part costs on average €5,000–20,000/hour*. How machinery manufacturers can permanently improve availability with automated spare parts planning.

Machine downtime costs up to €20,000 per hour, why does it keep happening again and again
Spare Parts Image

The spare part had been ordered. It arrived three weeks too late. The system was down for four days.

We hear this story regularly from after-sales managers at mid-sized machinery companies. Not as an exception, but as the norm.

Everyone knows the figure: an unplanned downtime costs between 5,000 EUR and 20,000 EUR per hour, depending on the industry, the machine, and the order situation. Over two days of downtime, that can quickly amount to six-figure losses, including contractual penalties, emergency deliveries, and damage to customer trust.

The question is not whether people know this. The question is: Why does it keep happening anyway?

Why does spare parts planning fail?

Spare parts planning in mid-sized companies fails not because of a lack of knowledge, but because of a lack of systematic approach.

Many companies plan with Excel. Some with standard SAP functions that were never fully configured. And some already use planning tools that are so complex that an expert team is needed to operate them.

The result: planning decisions are based on the experience of individual employees, not on current consumption data.

This creates four concrete problems:

Service managers know this result well: customers call because the machine is down. The technician is there, but the part is not.

  • Overstocking

    for parts that are rarely needed, because buyers play it safe

  • Understocking

    or high-consumption items, because coverage is not systematically monitored

  • Loss of knowledge

    when staff turn over, planning know-how lives in people's heads, not in the system

  • Reactive behavior

    instead of proactive. Orders are only placed once someone notices something is missing

What does machine downtime actually cost?

An unplanned machine downtime costs industrial companies in Germany an average of €147,000 per hour; globally it is around €116,000. This is shown by the "Value of Reliability" study, which ABB commissioned Sapio Research to conduct in July 2023 among 3,215 maintenance decision-makers worldwide. And it happens frequently: in 67% of surveyed companies at least once a month.

In capital-intensive sectors such as thermal process engineering or semiconductor manufacturing, these figures are significantly higher.

The problem: these costs do not appear in any planning budget. They only become visible once they have already occurred.

How automated spare parts planning prevents downtime

Automatisierte Ersatzteilplanung reduziert ungeplante Maschinenstillstände, indem sie Bestellbedarfe systematisch aus Verbrauchsdaten, Lieferzeiten und Sicherheitsbeständen berechnet, bevor Teile knapp werden.

This works in three steps:

  • 1. Data integration

    The planning system reads consumption history, inventory levels, and lead times directly from the ERP (SAP, ProAlpha, ABAS, etc.).

  • 2. Demand Forecasting

    Based on this data, the planning agent calculates which parts will be needed and when, taking into account seasonality, failure patterns, and supplier variability.

  • 3. Order recommendations

    The system automatically suggests what should be ordered and when, the team approves or adjusts.

This is not a theoretical model. At Fischer TireTech, this approach reduced the lead time for spare parts from 75 to under 30 days. Parts availability rose to around 80 percent. The planning department now works with the system, not against it.

Read more in the Fischer TireTech use case.

What is the difference from standard SAP functions?

SAP offers planning functions, but they are designed to manage production requirements, not spare parts inventory. Spare parts planning has different characteristics: irregular consumption, long lead times, high unit values, and sometimes unique demand situations.

Standard SAP methods in this environment frequently produce excessive safety stocks or blind spots for fast-moving items. Dedicated planning solutions use methods specifically designed for these characteristics, probabilistic demand models, ABC/XYZ classification, and automatic parameter adjustment.

What does this mean for after-sales managers in practice?

Those who work today with Excel or simple SAP planning rules have no structural visibility into which parts will be missing in 6 weeks.

This is not a question of the team's dedication or experience. It is a question of the tool.

Modern planning systems like PartsOS Planning go live within weeks, including ERP integration, without an IT project. They require no new processes. They make the existing process measurable and manageable.

The result: your team no longer reacts to downtime, it prevents it.

Conclusion: Parts availability is not a matter of luck

Parts availability is plannable. Companies that manage spare parts planning systematically measurably reduce unplanned downtime and simultaneously free up capital that was tied up in oversized inventory. The technology for this is accessible today. And it is no longer a large-scale project.

External sources:

  • *ABB Motion Services (2023)

    ABB Motion Services (2023): Value of Reliability. ABB Survey Report 2023 – Industry's Perspective on Maintenance and Reliability. Conducted by Sapio Research, July 2023. Available at: https://library.e.abb.com/public/45afcf54780c489095517e653422d157/ABB_Survey%20Report%202023_1920x1080_20231010_JL_final_edits.pdf (accessed: 23.04.2026).