EU nations have committed to spending €800bn on defense by 2030 and European manufacturers are responding with the fastest capacity expansion in decades. MBDA has doubled its missile output since 2023. Dassault has opened its first new facility since the 1970s. Rheinmetall is targeting 1.1 million artillery shells annually by 2027, up from 70,000 in 2022. Even automakers have started producing drones.
The question that receives less attention is whether this capacity can be sustained. The answer depends heavily on unplanned downtime.
Running production lines harder and longer makes each unplanned stoppage proportionally more damaging. In defense manufacturing specifically, that damage compounds in ways commercial production doesn’t experience. Low-volume, high-complexity systems, strict certification requirements, long supplier lead times and milestone-based contracts all combine to ensure that stoppages don’t stay local.
The effects vary by sector. In munitions, a single equipment failure can stop an entire production campaign. In land systems, downtime clusters around bottleneck assets such as CNC machining centers and robotic welding rigs. In aerospace, where cycle times are long and integration is complex, a disruption in composite curing or avionics testing can push schedules back by weeks.
Why calendar-based maintenance falls short
The specifics differ by sector, but the conclusion is similar: a small number of critical assets determines throughput and downtime carries substantial financial consequences. Industry benchmarks put the direct cost of unplanned downtime in aerospace final assembly at several hundred thousand euros per day. Add milestone penalties, which are standard in most European defense contracts and reputational damage in multinational programs, and the full cost is considerably higher.
Servicing equipment at fixed time or usage intervals has obvious limitations in this kind of environment. It either pulls functional assets offline too early, reducing available capacity or misses developing faults that don’t align with the maintenance schedule.
The US Government Accountability Office has put the distinction plainly: preventive maintenance replaces a tire every 30,000 miles regardless of condition, while predictive maintenance uses sensor and historical data to find the right moment: not so early that it wastes resources, not so late that it invites failure.
Machine learning is making this approach practical at scale. By analyzing sensor data, historical performance records and machine learning models, predictive maintenance systems detect degradation patterns and flag anomalies before they cause failures. In munitions plants, vibration and thermal monitoring tracks mechanical wear; In aerospace machining, spindle performance data signals tool degradation before dimensional tolerances drift; In electronics manufacturing, environmental monitoring catches control deviations early.
The data foundation
Predictive maintenance only works with coherent underlying data, and this is where many European defense manufacturers face a practical gap. Asset data in typical facilities is spread across ERP platforms, maintenance logs, engineering databases and spreadsheets. Different departments frequently apply different naming conventions and hierarchies to the same physical equipment.
A mature enterprise asset management (EAM) and asset performance management platform addresses this directly.
Octave Attune EAM, for instance, provides structured asset hierarchies linked to engineering and configuration data, so maintenance events are traceable to specific program outputs and root cause analysis can span operations. It also preserves lifecycle intelligence: the decades of maintenance, modification and performance records that defense assets accumulate and that no organization can afford to lose.
Getting the conditions right
With that data layer in place, Attune EAM lets operators model scenarios: how throughput responds to changes in maintenance intervals or how specific equipment performs under increased production tempo. Supply chain analytics can forecast parts demand and reduce emergency procurement, which currently disrupts schedules.
The technology is ready, but the harder challenge is organizational. Asset data must be standardized and governed before analytics can be trusted and engineering, operations, IT and maintenance teams need to work from a shared view rather than separate ones.
Cybersecurity also needs to be built into the architecture from the outset, not added later. A successful attack on a production system has the same operational effect as an equipment failure. And the workforce needs investment in data literacy, reliability engineering and digital skills to use these tools effectively.
Europe is spending heavily to expand defense production capacity. Whether that investment yields reliable output depends on getting the operational fundamentals right. Capacity that stops unexpectedly is just expensive inventory.