Looking ahead with data
Historical patterns are used to make an evidence-based estimate of what may happen next.
Predictive maintenance
Use asset data to plan maintenance earlier and more precisely.
Scheduled maintenance can come too early or too late, while unexpected failures disrupt operations. Organisations do not always use the available asset data in their planning. Predictive maintenance uses data to forecast maintenance needs or identify potential anomalies.
From an isolated possibility to a practical application
An application only becomes meaningful when it fits a specific task and the information available for that task.
Feasibility depends on the available data and demonstrable predictability. That is why we define in advance what the solution should and should not do, how its results will be checked and who remains responsible.
What it delivers
Supporting planning
The results help staff weigh up future capacity, stock or maintenance needs.
Making uncertainty visible
Predictions are tested and remain a tool to support decision-making.
Our approach
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Define the decision and forecast horizon
We define what to predict and how far ahead.
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Analyse the data
We assess historical data for quality and predictability.
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Develop the model
We start with a simple method and compare performance.
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Validate the results
We test predictions against historical periods and review them with experts.
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Integrate and monitor
We integrate the results into the process and monitor model performance.
Tailored to your organisation
This service is relevant to asset-intensive organisations. The specific setup depends on the process, the available data and the risks involved in its use.
That’s why we start with a clearly defined application and only expand it once the initial setup works in practice.
Other solutions
One data platform for fragmented sourcesBring data from separate systems together in one place, where everyone sees the same figures.
Dashboard for day-to-day operationsSee how things stand on one screen, without having to build reports first.
Predicting when maintenance is neededPlan maintenance based on what the data says, not on a fixed schedule.
Frequently asked questions
This service addresses the following challenge: predictive maintenance uses data to predict maintenance needs or potential anomalies. How it works in practice depends on the process and the data available.
The service can be relevant to asset-intensive organisations. A clear need and usable information matter more than the size of the organisation.
That depends on the application. We start with the question you need to address, then determine which data or sources are necessary and suitable.
No. For fixed, predictable tasks, conventional automation may be enough. We choose the simplest approach that addresses the challenge effectively and add complexity only when needed.
We define the scope of the application, check the results with users and put appropriate controls in place. After deployment, we monitor how it works and adjust the solution when the data, its use or the context changes.
Discover what predictive maintenance can do for your organisation
Want to explore where it makes sense to start with predictive maintenance? Together, we’ll assess the challenge, the available information and the prerequisites.