Traditionally, manufacturers relied on reactive maintenance. It was the practice of repairing equipment as a reaction to equipment breakdown. This resulted in unexpected and longer downtimes, and hefty bills. The trend has now changed to predictive maintenance.
What is Predictive Maintenance?
It is a technique of anticipating the maintenance requirements in machines and repairing them before they break down.
When you analyse the operational data, you will see patterns emerging. These patterns allow the operators to identify and predict when the equipment may need maintenance. Thus, operators can plan maintenance without disrupting the manufacturing operations.
Predictive maintenance is important as it detect faults at early stages. It reduces unscheduled downtime and increases productivity. The quality of manufacturing processes also improves.
6 Advantages of Predictive Maintenance
• Optimizes Planned Downtime
Planned downtime allows you to keep the equipment running smoothly. This is when you can clean the equipment, oil the machine or replace parts that regularly fail. Planned downtime reduces the chances of unplanned downtime.
With the data collected on machine operations, you can schedule preventive maintenance regularly. Plan them when there will be the least impact on production. Planned downtime also extends the life of the equipment and results in cost savings.
• Minimizes Unplanned Downtime
Unplanned downtime costs businesses billions of dollars each year. You can use predictive maintenance to minimize unplanned downtime and associated expenses.
Scheduled preventive maintenance ensures that the equipment runs smoothly. Predictive maintenance consists of monitoring the machines digitally and collecting the data. Analysing the data will reveal patterns on any machine.
Pattern detection based on historical data helps in identifying a machine that shows signs of a possible outage. This information helps in proactively planning equipment maintenance.
• Extends Equipment Life
Preventive maintenance also helps in identifying when the machine may reach the end of its life.
The functioning of a machine will change as it ages and the way it responds to production stress.
Data patterns will reveal a machine’s tipping point between cost and performance. You can forecast and plan whether to repair large parts or an entire unit or replace them based on its performance. This helps in increasing the life of the equipment.
• Increases Employee Productivity
Predictive maintenance optimizes employee productivity as well.
Reduced breakdowns mean that employees don’t have to waste time on repairs that otherwise could have been prevented. It also minimizes accidents. The systems can alert the personnel or even halt the equipment when it poses danger.
This promotes safety within the factory and minimizes work-related injuries. This boosts employee morale and reduces the stress that comes with addressing an unexpected downtime.
• Reduces Labour Costs
When equipment breaks down unexpectedly, you would have to reach out to technicians on an urgent basis. They would then have to spend time on identifying the cause of the breakdown. This is an expensive process.
With preventive maintenance, you know exactly when to repair a machine. You also can identify the kind of repair it would need. This means you know when to call the technicians. You can give them focused and specific tasks. This reduces labour costs drastically.
• Increases Revenue and Efficiency
Predictive maintenance allows you to fix potential problems before the equipment fails. This increases efficiency as machines run smoothly without breaking down often.
You can do quicker repairs of the faulty parts and spend less maintenance on good components. All the repairs can be handled quite efficiently and reduce the overall repair time. This helps in saving money and increasing your revenues.
In today’s manufacturing environment, predictive maintenance is necessary, not an option. Want to learn more about how predictive maintenance can help your facility? Contact our experts at EzyTrader.