In many industries, maintenance usually follows protocols provided by the equipment manufacturer, where recommendations for maintenance are based on hours of use or some other fixed time interval rather than on predictive analysis. This rules-based approach can be costly for manufacturers in two ways – performing maintenance when it is not needed and missing developments between intervals which could lead to breakdowns or damage of equipment.
Manufacturing companies discard 98% of all the data they can collect because they do not have the operational analytics capabilities to integrate that data into their operations
Advanced analytics raised daily production by 5 to 8 percent by allowing managers to schedule and allocate staff and equipment more effectively after predictive analysis
Avoiding unplanned outages can help manufacturers reduce maintenance costs by 5 to 10 percent
The development of data driven services for maintenance, quality and process optimization has reached a level where failures can be predicted well before a problem arises, and potential quality issues detected even though the process has been performing within specification.
The key to successful data driven services is to combine analytical capabilities and domain expertise. This will guarantee development of adequate insight and intelligence to determine the correct recommendation.
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