Predictive Maintenance

Traditionally, you have been reliant on a part failing or a customer notifying you of a problem before you are able to resolve the issue. This means that whatever has failed has already had an impact and the cost to rectify the issue can quickly escalate.

Oneserve highlights a potential failure and schedules an engineer to resolve the issue before it causes any downtime. This creates a much more efficient service that will exceed customer expectations.

An overview

Our predictive field service solution leverages big data, AI (Artificial Intelligence) and IoT (Internet of Things) to continually monitor assets and learn from job histories. Oneserve infinite then predicts problems or potential failures to significantly increase efficiencies.

Key benefits

Optimise machine performance

Oneserve predicts when a part within a machine is likely to fail before any downtime is occured. This enables you to carry out any essential maintenance and continue operating with minimal disruption.

Manage critical assets remotely

Continually monitor your assets using our intuitive predictive services dashboard and be alerted whenever a fault occurs. A site visit can then be arranged to resolve the problem before it has any impact.

Maximise KPI performance

Using deep learning algorithms, Oneserve will alert your schedulers if a job is likely to be impeded, for example, probable access difficulties. They can then make corrections or amendments to ensure the job succeeds.

What you can expect from Oneserve’s Predictive Maintenance

Step 1: A failure is identified

Using either deep learning algorithms or IoT sensors, a fault is identified. Oneserve alerts the nominated person and gives them a full update and complete visibility.

Step 2: A resolution is booked

The system will automatically schedule the relevant activity, parts and engineer to resolve the issue. (Oneserve does this within predefined parameters.)

Step 3: Information is continually used

Dashboards record all failure events and the information is used to learn for the future. This may identify patterns within failure events or help form future maintenance schedules.