Scalable Plant Wide Predictive Maintenance

Use Case

Improving connectivity to factory assets and machine data

Unplanned downtime is a challenge for many manufacturers. When a production line stops for unplanned maintenance, costs are incurred for workers waiting for maintenance to finish.

This challenge is large enough that 24% of manufacturing costs are attributed to unplanned downtime. 90% of maintenance work is categorized as “crisis work” responding to unforeseen issues with machinery.

With limited visibility to machine data and an over-reliance on human inspection, many manufacturers have been forced into a reactive posture for maintenance activity. To counter this situation, condition based monitoring and analytics can provide the visibility necessary to take a more proactive stance to machine maintenance.

Supported Industries

We have a solution

Condition based machine monitoring enables predictive maintenance

This solution use case, provided by Senseye on the MindSphere® platform, provides production machinery condition monitoring that enables scalable plant wide predictive maintenance.

This real-time analytics engine, tied to condition monitoring for all plant assets, drives lower maintenance costs, increased productivity, a decrease in unplanned downtime and an increase in downtime forecasting accuracy. With better scheduling for downtime, organizations avoid costly break-fix scenarios where production staff is waiting for maintenance to complete emergency repairs.

Maintenance managers and plant managers can align their maintenance and production activity more closely than ever before, improving productivity and driving down costs.

Features

Real-time production asset condition monitoring for all connected plant assets.
Algorithms designed specifically for predictive maintenance of industrial assets.
Integration with factory floor sensors, MES, CMMS, ERP, and the MindSphere platform.

Benefits

Increase production availability.
Lower maintenance costs by an average of 40% and improve maintenance productivity by an average of 55%.
Improve downtime forecasting and scheduling by an average of 85%.
Reduce costs by optimizing staffing levels of your production and maintenance teams and minimize emergency response work.

How it works

Learn more about the applications and services that power this solution.

Senseye™

Senseye™ is cloud-based software for Predictive Maintenance. It helps manufacturers and industrial companies to avoid downtime and save money by automatically forecasting machine failure without the need for expert manual analysis.

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Senseye Professional Services

Senseye offers a range of professional services to deploy this solution, depending on your needs, and accelerate your digital transformation. Services may include consulting, implementation, integration, customization and training.

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