Aiming for a scalable and predictive operating model
Kemira operates more than 60 production plants worldwide. To manage maintenance consistently and sustainably in such a large and diverse environment, the company needed an operating model that works just as well at a single plant as it does on a global scale.
Together with Asensiot, the following objectives were set:
- improving maintenance risk management across the entire asset base
- supporting decision making with data, not just alerts
- providing field teams with clear, actionable information
- enabling fast and effortless deployment across different plants
The solution selected was the MyAsensiot Condition Screening® operating model, which combines broad measurement coverage, AI driven analytics, and information that integrates directly into maintenance processes.
Maintenance driven by data — not assumptions
The operating model builds on existing monthly field rounds, during which vibration and sensory measurements are collected from rotating equipment. The measurements follow predefined routes, and RFID identification ensures that each data point is linked to the correct asset.
The collected data is analysed using AI powered screening and then validated by experts. Crucially, the system does more than generate isolated alerts — it turns raw data into prioritised, maintenance relevant insights:
- which assets show increasing risk
- which maintenance actions should be addressed first
- which operations will have real impact
All results are integrated directly into Kemira’s SAP/HANA system, making them part of normal maintenance and work order management. This enables more proactive planning and reduces unexpected failures in production.
New metrics to support risk management
Alongside traditional KPIs, Kemira adopted new metrics that improve risk visibility. These help track:
- fault progression over time
- early stage bearing and lubrication issues
- so called “bad actor” assets with the highest business impact
This shifts maintenance from reactive repair work to proactive risk reduction. The goal is not to do more maintenance, but to do the right work at the right time.
Fast deployment and measurable results
The first deployment phase included 10 production plants across several countries. In total, more than 1 500 rotating assets were brought under monitoring. The rollout was straightforward and required minimal plant specific preparation — a two person team completed the field work in roughly two weeks.
The results speak for themselves:
- 45 unplanned shutdowns avoided
- 14 cases where asset lifetime was extended
- estimated savings of approx. 2,26 million euros (around 7× ROI)
In 2024, Kemira expanded the operating model to 26 production plants in total.
Maintenance leadership as part of sustainable operations
Kemira’s experience shows that developing maintenance is not just a technology project. Real value emerges when data becomes part of leadership, decision making, and everyday maintenance work.
With a realistic and up to date overview of asset condition:
- maintenance can be planned more sustainably
- risks are identified earlier
- unnecessary work decreases
- asset lifetime increases
- production reliability improves
The work continues. The predictive and scalable maintenance model keeps expanding all the while strengthening the role of maintenance as a core part of sustainable, efficient production.
