Cost Optimization in Machine Maintenance—Four Strategies That Actually Work
Unplanned downtime is expensive. Depending on the industry and plant size, it costs between €40,000 and several hundred thousand euros per hour (Siemens, True Cost of Downtime 2024). Yet many maintenance departments still operate in firefighting mode: reacting instead of planning, repairing instead of preventing.
1. Planning That Actually Works
The most common misconception in maintenance planning is that you only need a plan. But just creating a plan isn’t enough—it has to be lived and continuously maintained.
Static maintenance intervals that are set once and never questioned again are one of the most expensive mistakes in maintenance. Machines change due to use, environmental conditions, and aging. A plan that doesn’t account for these factors either triggers unnecessary maintenance visits or lets real problems go undetected for too long.
What actually works is regularly reviewing and adjusting maintenance plans on the basis of real operating data. Actively schedule maintenance work during periods of low machine utilization—based on actual need, not the calendar. Store responsibilities, checklists, and required materials digitally so technicians can get started without having to ask around.
The decisive metric is Mean Time Between Failures (MTBF)—it shows whether the planning is working. If it goes up, the strategy is on track.
3. Using Data, Not Just Collecting It
Most maintenance departments don’t have a data gap. They have an analysis problem. Service reports, spare parts consumption, response times, failure logs—it’s all there, but it’s rarely brought together and analyzed systematically.
There are three metrics every maintenance manager should know:
| Metric | What it measures | Why it matters |
| OEE (Overall Equipment Effectiveness) | Availability × performance × quality of the equipment | It shows how much potential is going unused |
| MTTR (Mean Time To Repair) | Average repair duration | A high MTTR points to information or spare parts problems |
| MTBF (Mean Time Between Failures) | Average time between two failures | If it doesn’t go up, the prevention strategy isn’t working |
Consistent analysis reveals which machine types fail disproportionately often and why, where repair costs could be lowered through targeted prevention, and how much time technicians spend searching for information. According to IDC Research, up to 30 percent of working time is spent on exactly that: searching for documents, parts, and information.
Data alone changes nothing. Teams that review KPIs together once a month and derive concrete actions from them make maintenance measurable and manageable.
When Information Is Connected, All Four Strategies Work Better
These approaches have something in common—they only truly work when the right information is available at the right time—not spread across different systems, but in one consistent overall picture.
That’s exactly the approach behind Quanos InfoTwin. As a digital twin, QIT connects data from technical documentation, spare parts information, and service data into a complete picture of every single machine. Maintenance technicians see at a glance which component is installed, what its maintenance history looks like, and which spare parts are available.
The result is shorter diagnosis times, better-informed decisions, and less dependency on the knowledge of individual experts.
Want to see what that looks like in practice?
The pressure is mounting. More machines per technician, a worsening skills shortage, rising spare parts prices, and customers who expect maximum availability. Anyone who wants to permanently lower maintenance costs has no way around a structural realignment.
These four strategies have proven effective in practice.
**Common mistake:**Creating maintenance plans but not updating them. After twelve months they’re obsolete—while still giving the team the false impression that they’re working in an organized manner.
2. Predictive Maintenance: A Competitive Edge When the Conditions Are Right
Predictive maintenance is becoming a genuine competitive advantage in many industries. It’s a logical next step where downtime is especially costly or critical assets have to run around the clock. In classic mechanical engineering, however, it’s still more of a strategic goal than an everyday reality for many mid-sized companies.
The basic principle is compelling: Sensors continuously monitor condition parameters such as temperature, vibration, and power draw. If readings deviate from defined normal values, the system raises an alarm long before a failure occurs. Maintenance actions are triggered based on actual need rather than a rigid schedule.
When planning is implemented correctly, unscheduled downtime can be demonstrably reduced by 30 to 50 percent (McKinsey & Company; Gartner, 2024). Maintenance costs fall because intervention only happens when necessary. And components last longer because premature replacement is avoided.
What many underestimate is that the hard part isn’t the sensor technology—it starts afterward—ensuring data quality, calibrating thresholds sensibly, reducing false alarms, maintaining master data, and above all, change management. Technicians have to learn to trust the system. Processes need to be redefined. Who’s actually responsible when the alarm goes off should be clear beforehand.
Modern systems use AI to detect patterns in machine data that would be nearly invisible to humans. But that only works with good data, sufficient operating history, and cleanly classified failure events. This is exactly where many projects fail in practice—even before the actual analysis begins.
The realistic starting point is not the most complex machine, but with the one that fails most often or whose downtime is most costly. A pilot project with clear KPIs and a realistic timeframe for building the data foundation shows whether the investment pays off. And it delivers the learning curve that’s indispensable for later scaling.
**Common mistake:**Predictive maintenance is treated as an IT project, not a maintenance project. Without the technicians who know the machines, even the best systems fail—not because of the technology, but because of a lack of acceptance.
**Common mistake:**KPIs are measured but not acted on. Numbers without consequences are decoration, not steering instruments.
4. Spare Parts Logistics: The Underestimated Cost Driver
A technician waiting for a spare part is generating cost, even if they appear to be doing nothing. Every hour of waiting is an hour of extended downtime. At the same time, oversized inventories tie up capital that’s needed elsewhere.
Having the right spare part in the right place at the right time—without building up unnecessary stock—is a classic optimization problem. And it can be solved much more effectively with the right digital tools than with experience alone.
Fast identification is the first step in this process. Digital spare parts catalogs with clear images and parts lists make it possible to find the right part in minutes—even for technicians seeing a machine for the first time. Wrongly ordered parts due to misidentification become a thing of the past.
The second lever is data-based inventory optimization. Which parts are needed, and how often? Which have long lead times and should therefore be stocked? The answers are hidden in the consumption data, once it’s actually analyzed.
And the third: no media breaks. From the point of failure to the time an order is placed, there should be no need to switch systems. Every manual transfer is a potential source of error and costs time.
Companies that combine predictive maintenance with data-based inventory management report maintenance cost savings of between 15 and 25 percent (McKinsey & Company).
**Common mistake:** Spare parts inventories are managed by gut feeling, not data. This leads to simultaneous overstocking of standard parts and shortages of critical components.
