Key benefits of Predictive Maintenance Software

Key features of predictive maintenance software

Failures are identified

Using deep learning algorithms or IoT sensors to identify faults, Oneserve can automatically alert schedulers to an identified issue. This gives your team complete visibility and enhanced control over the repairs process, pre-empting issues before they happen and negatively impact your productivity.

A resolution is booked

Oneserve assigns jobs to operatives based on predefined parameters including qualifications, skills and location. This means that Oneserve automatically schedules the relevant activity, parts and the best suited operative to resolve the identified issue.

Information is continually used

Dashboards linked to your predictive maintenance provider, record all failure events and the information can then be used to learn for the future. This may help you identify patterns within your failure events which can be used to guide your future maintenance schedules.

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An overview of Predictive Maintenance

Traditionally, your repair work will have been reliant on a part failing or a tenant notifying you of a problem. Without this, you wouldn’t have even known there was an issue to resolve. This process means that whatever has failed, has already had an impact on your service delivery and the cost to rectify the issue can quickly escalate.

Oneserve’s job management software can easily connect with your predictive maintenance solution, whether that uses Internet of Things (IoT) or Artificial Intelligence (AI) learning as it’s source, to ensure your organisation can be proactive rather than reactive in its approach to identifying repairs.


Predictive maintenance software uses the Internet of Things (IoT) or Artificial Intelligence (AI) to monitor the efficiency of an asset based on the data that is collected. Anomalies in asset performance can be identified so that processes can be put in place to fix them before they result in asset failure.

From the perspective of building maintenance, the Internet of Things (IoT) uses connected devices and sensors to monitor asset performance. This provides precise, real-time data, which helps us identify anomalies and trends in asset performance, and gives us the insight needed to make the most informed and proactive maintenance decisions. This predictability and asset visibility, lowers the overall amount of downtime and reduces maintenance costs.

Predictive maintenance reduces unscheduled asset downtime that has been caused by a fault or system failure. With less asset downtime, your operatives will spend less time completing emergency repairs so their time can be utilised more effectively. Your organisation costs associated to preventative asset maintenance will be reduced and the lifespan of ageing assets can be increased as real-time monitoring will highlight any issues before they evolve into major, costly problems.