Now that it’s clear that predictive maintenance is an assured way to avoid unplanned downtime and incur higher manufacturing costs, the question is: how do you implement a predictive maintenance plan?
First, get to the crux of what problem you’re trying to fix:
Are you more concerned about unplanned downtime or the cost of component failure?
Are some machines on your floor more ‘important’? ie: used more frequently, central to more orders, etc...
Then you need to assess your existing status, or create a baseline of data on machine performance. For this, you can use your own standards, OEE standards or other industry standards. Review each machine to see what the historical performance levels have been: how often has it been down, what components fail regularly, how often is maintenance currently scheduled and so on.
Second, examine the historical data for patterns and what metrics will indicate a problem, what deviations from the baseline should flag an operator and so on.
Finally, once you are using these patterns and the data, relative to your baseline performance measurements, you need to institute a process for continually updating the data and reviewing it to ensure that it continues to reflect current status and will flag deteriorating patterns that clearly signal a need for maintenance. This is the key: you can’t predict what you can’t analyze. Accurate data is essential!
Minimizing unplanned downtime, at least as it relates to the functioning of the machines, is a huge cost savings and will prevent delays to market that will also impact the bottom line. In today’s manufacturing environment, predictive maintenance is not a ‘nice to have’. It’s a necessity.
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