5 benefits of predictive maintenance – TechTarget

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Unplanned downtime can get very expensive, especially for companies operating in industries such as manufacturing, transportation, energy and utilities, which rely heavily on specialized equipment. Predictive maintenance has increased in popularity over the past decade and can potentially help CFOs, CSCOs and other leaders balance cost control, risk mitigation and revenue growth.
Conventional approaches to maintenance tend to be reactive, with companies making repairs after equipment breakdowns, or calendar-based, with maintenance scheduled at routine intervals. In contrast, predictive maintenance is driven by data collected from IoT sensors. Machine learning and real-time analytics can help forecast potential equipment failures before they occur.
Here are some additional benefits of predictive maintenance.
With predictive maintenance, maintenance teams can schedule work at more convenient times rather than responding to breakdowns immediately.
The reduction of unplanned downtime can help manufacturers in particular, as predictive maintenance leads to more consistent production, which can  improve the customer experience and reduce late- delivery penalties.
Also, integrating real-time IoT sensor information with enterprise asset management  and ERP systems enables the technology to automatically generate work orders and provides improved visibility into inventory and resource availability.
Predictive maintenance can help companies spend more consistently. Emergency repairs can be expensive, as they often require overtime labor and expedited acquisition of parts and supplies.
This approach also eliminates the need for routine inspections and reduces unnecessary maintenance, further reducing costs.
Predictive maintenance can help CFOs improve operations by increasing gross margins and working-capital efficiency, as funds previously tied up in emergency reserves or excess spare-parts inventory can be redeployed.
Assets can degrade quickly if they are not adequately maintained. Early detection of problems and prompt repairs can help organizations extend the life of assets, saving money.
Downtime reduction increases equipment availability, improving the manufacturing metric of Overall Equipment Effectiveness (OEE) and can lead to increased product output, which improves a company’s overall revenue.
Early detection of issues such as overheating, structural fatigue and leaks can help companies reduce safety, quality and compliance risks.
For example, deviations in a soldering machine’s performance could negatively affect product quality, resulting in extra work, customer returns and reputational damage. Meanwhile, early detection of a failed seal in a chemical plant could prevent the release of toxic substances that could endanger staff or harm the environment.
CIOs can reap additional value from predictive maintenance by using the data from IoT sensors in their broader enterprise data strategy.
Analyzing IoT data alongside other production-related data sets can help managers make more informed decisions across various aspects of operations. For example, increased access to data from predictive maintenance can help managers better carry out execute capacity planning, which could lead to improved output.
James Kofalt spent 16 years at SAP working with SME business applications and was a product manager for integration technology at Microsoft’s Business Solutions division. He is currently the president of DX4 Research, a technology advisory practice specializing in ERP and digital transformation.
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