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How can IoT help minimize Asset Failure with Predictive Maintenance?

Manufacturers can no longer afford unplanned downtime, in fact this is the single largest source of lost production time for most of them. In the current world of IoT, having a preventive and predictive maintenance methodology is very critical for every organization. Real-time data processing can provide intelligent insights for detecting failures proactively and increase productivity and efficiency. The two major costs all manufacturers incur today; high cost of downtime in the existing planned maintenance approach and cost of unplanned downtime which is 10 times greater. The existing approach of planned maintenance has cost impact and limitations which impact the overall production and pose a variety of problems:

  • Production and productivity losses from unplanned downtime.
  • Excessive costs and risk of failure created by inefficient maintenance cycles.
  • Lack of Insight into asset performance history and root cause of failure.

Why Predictive Maintenance is important?

Predictive maintenance is the practice of collecting, analyzing and acting on health and performance information in real-time. Insight into live operational data of assets and historical failure patterns can help predict and prevent asset failure more effectively.

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cost savings compared to scheduled repairs

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reduction of overall maintenance cost

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reduction in asset breakdown

The implementation of predictive maintenance results with reduced operational cost, prevention of unexpected downtime, assets optimization and increased productivity and efficiency.

MindSphere, the open IoT operating system, enables you to streamline predictive maintenance adoption with the ability to easily connect your plant or manufacturing unit and collect and analyze real-time asset data.

  • Establish baseline patterns of assets.
  • Compare real-time behavior to baseline.
  • Alert users of abnormal behavior.
  • Take precise action based on recommendations.

What MindSphere users have been realized after implementation:

30% savings on service maintenance

15% reduction in asset downtime

8% increase in manufacturing output

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