Using Predictive Maintenance analytics to drive down costs and revenue loss
Research shows that manufacturers on average lose 800 hours of productivity per year due to machine downtime. The average cost of downtime across all business verticals is in the hundreds of thousands of dollars per hour. For some organizations, four hours of downtime can equate a cost of over $2 million. Hoping to minimize the likelihood of a machine’s failure, manufacturers often follow a regular preventative maintenance schedule, if they are aware of when their equipment is due for maintenance, upgrade or replacement.
Minimizing the cost of downtime can be vastly improved when data generated by a machine can predict it’s likelihood of failure before that failure occurs. This data, combined with machine learning and predictive analytics algorithms allows manufacturers to move from a repair and replace maintenance cycle to a predict and fix maintenance model.
KnowledgeSTUDIO’s Predictive Maintenance (PdM) capabilities ahelps manufacturers perform preventative or corrective actions using insight found in data generated directly from their equipment which extends the lifecycle of equipment, reduces operating costs, and decreases unplanned and unnecessary planned downtime