PREDICTIVE MAINTENANCE
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investment in predictive maintenance is accelerating, supported by broader digital transformation across industrial sectors. “There is also a commercial advantage on both sides. Predictive maintenance enables more predictable service models, long-term cost reduction and improved operational continuity in increasingly complex and unpredictable environments.”
Contributors
IT team computers to monitor the device fleet across that client’s site – including remote sites if VPN connectivity is in place,” he says. “This is followed by threshold definition, setting custom alert triggers such as alerting the helpdesk if a service issue arises or messaging facilities to replenish consumables such as paper or ink. “Integration is also key, connecting your alerts to a Professional Services Automation tool or ticketing system via email or SNMP for rapid responses from first line IT technicians, which improves customer confidence in the long-term.” Pitfalls But there are pitfalls that MSPs should be aware of. “The biggest pitfalls are not technical, but operational: starting too broad, lacking structured asset data and failing to integrate insights into existing workflows,” says Berend. “Predictive maintenance only works when the underlying data is clean and when engineers trust the insights enough to act on them. Technology should simplify decision making, not complicate it.” A sensible starting point is identifying critical assets where failure would have the greatest operational or financial impact, says David. “From there, MSPs can deploy sensors and connect them to monitoring platforms that provide real- time visibility,” he adds. “One of the most common pitfalls is overengineering the solution at an early stage. It is more effective to start small, demonstrate measurable value, and scale gradually. Another challenge is a lack of in-house expertise to interpret data, which can lead to missed insights or false alarms. Finally, integration with existing systems is essential — if predictive maintenance operates in isolation, it becomes significantly harder to act on the information effectively.” Greg agrees, saying a big potential pitfall is trying to do too much too quickly.
Berend Booms
Setting up With the benefits of predictive
maintenance becoming ever clearer, MSPs should look to offer it – and doing so is easier than ever, thanks to modern IoT sensors and AI enabled maintenance platforms, according to Berend. “Predictive maintenance success depends on structuring the data correctly, understanding asset behaviour and embedding it into service workflows,” he says. “The key is to begin with one asset class and one clear use case, prove the value quickly and scale from there.” David agrees that predictive maintenance is more accessible than many businesses assume. “The core technologies, including sensors and remote monitoring platforms, are well established,” he says. “The main challenge is not installation, but integration and interpretation of data. MSPs must ensure they are capturing the right signals and, critically, converting them into actionable insight. Without this, there is a risk of generating data without delivering meaningful value.” Greg adds that platforms that bring together monitoring, automation and service management make it much easier to move towards predictive models. “When everything is integrated, MSPs can automate responses, streamline workflows and act on data insights in timely manner,” he says. To implement effectively, MSPs should follow a clear roadmap, notes Rick. “Deployment by installing the device management tool on a central server or
ultimo.com
Greg Jones
kaseya.com
When everything is integrated, MSPs can automate responses, streamline workflows and act on data insights in timely manner. “ ”
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