ANIPLA TECHTALK – Data analytics applied to predictive maintenance
Most organizations compete in technology-disrupted markets, and industrial organizations are no strangers to the wave of digital transformation. The preferred approach is holistic, to identify use cases and business value across the enterprise and emerging technologies.
The introduction of IoT, Cloud, Advanced Analytics and Machine Learning changed the landscape of industrial machine maintenance. The convergence of these new technologies is helping companies move from reactive to predictive maintenance strategies. We can improve availability, reduce costs, increase security, and ultimately eliminate unplanned downtime.
The goal is to absorb the costs of upfront or excessive maintenance, as the reward is worth the risk. And also to try extending the maintenance interval a bit. The question then becomes, is it worth it? If a failure occurs, more expensive parts may need to be replaced, requiring expensive labor and taking a long time.
The cost associated with downtime is severe. Unplanned maintenance can cost 12-15% more than planned maintenance, and the added urgency required to bring operations back online can further increase costs and risk.Predictive maintenance takes into account estimated maintenance intervals while leveraging data-driven insights based on measurement of operating conditions. By using statistical thresholding or modeling and forecasting to calculate when repairs are needed, the adoption of predictive maintenance strategies are able to better manage parts and labor costs along with resource availability.
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