February 27, 2026
Predictive maintenance: Seeing problems before they stop production
Digitalization has transformed industrial maintenance, but one area stands out for its impact on both reliability and cost efficiency: predictive maintenance.
Predictive maintenance is becoming a strategic capability that strengthens operational control and long-term asset performance. For global manufacturing leaders, it offers something traditional maintenance models cannot – the ability to see failures before they happen and act before production is at risk.
From firefighting to foresight
Most predictive maintenance initiatives begin with familiar ambitions: reduce costs, improve availability, and move beyond constant firefighting. But installing sensors or collecting more data is not enough.
According to Patrik Nilsson, Group Product Manager at Quant, real impact requires the ability to connect data, operational context, and maintenance expertise into one decision-ready framework.
“We combine IoT data, analytics, and hands-on maintenance expertise into a structured operating model. This enables consistent, fact-based decisions on asset health, risk exposure, and prioritization of actions, reducing uncertainty and improving reliability across the entire asset base.”
One unified process
Sensors capture measurements such as vibration, temperature, current, pressure, airflow, or humidity. This data is securely transmitted through gateways and cloud services into an integration layer, where it becomes part of a structured decision process.
As Nilsson explains, the value does not come from collecting the data itself, but from acting at the right time.
“When raw signals are processed through statistical models and signal-processing algorithms, patterns become visible and actionable. A symptom becomes an alert. The alert becomes a service request. The maintenance team plans and executes the work before the failure escalates. That is where predictive maintenance shifts from insight to real operational impact.”
At the core of Quant’s approach is the Maximo Application Suite, the backbone of maintenance operations. It connects asset lifecycle management, work order flows, spare parts, planning, and scheduling, into one unified operating model.
“This is what makes predictive maintenance scalable: one consistent process across assets and sites, independent of sensor type, manufacturer, or equipment. The technology may vary, but the operating model remains the same.”
Meaning for decision-makers
For predictive maintenance to deliver business value, improved decision-making is a key area. And better decisions depend on reliable data. As Nilsson puts it:
“Clean data leads to clear insights, and clear insights lead to better decisions.”
In practice, the impact is visible in fewer unexpected interruptions, higher reliability, longer asset lifetime, and a lower total cost of ownership. Planning horizons become clearer, and resources can be allocated where they create the most value.
“The shift from reacting to predicting also reshapes budgeting, risk management, and resource allocation. It supports sustainability targets by reducing waste and unnecessary replacements. Just as importantly, it strengthens collaboration between production and maintenance. When both teams operate from the same data and interpret the same signals, discussions move from assumptions to shared facts”, Nilsson says.
Case 1: Reduced maintenance effort and significant savings
At one of our customer sites, 49 vibration sensors were installed on 16 critical assets, replacing frequent manual measurements and external monitoring. By moving to structured, sensor-based condition monitoring, manual work and outsourced services were significantly reduced, while visibility into asset health improved across the site.
“During the first phase, labour-related measurement work was reduced by approximately €6,000 annually. In the second phase, the savings increased to around €11,000. When avoided production losses were included, the total annual impact was close to €67,000”, Nilsson says.
Beyond the significant cost savings, the site achieved more predictable planning and continuous insight into asset condition, supporting more informed maintenance and investment decisions.
Case 2: Early detection prevented major loss
Another case comes from a heavy industry production line, where a vibration sensor monitored a critical electric motor driving an industrial oven fan. As resin accumulated in the fan’s moving components, vibration levels increased and temperatures began to rise. The system generated an early alert, enabling the maintenance team to intervene before the failure escalated.
“The alert allowed us to act before the motor failed. We estimate that we avoided a €5,000 motor replacement, approximately €100,000 in raw material losses, and unplanned downtime,” Nilsson says.
The case demonstrates how timely, data-driven intervention shifts maintenance from reactive repair to structured risk management, protecting production continuity and reducing financial exposure.
Future of predictive maintenance
Predictive maintenance makes the previously invisible visible. As technology evolves, its strategic role continues to grow. According to Nilsson, the next wave of development will move beyond identifying potential failures.
“AI will increasingly predict not only that an asset is likely to fail, but when and under what conditions. This fundamentally improves planning accuracy and cost optimization. Smart energy monitoring will also play a growing role, offering deeper insight into equipment health, energy consumption, and sustainability performance. As these capabilities converge, predictive maintenance will expand from failure prevention to performance optimization across entire production ecosystems.”
Whether the priority is operational stability, improved cost efficiency, strengthened safety, or enhanced sustainability, predictive maintenance is becoming a core capability for industrial decision-makers.
