Predictive Maintenance

Use Artificial Intelligence to predict and prevent equipment failure

Even for manufacturers already using machine data to inform production and maintenance decisions, it can be nearly impossible to hit industry-leading levels of Equipment Availability. 

That’s because often, equipment failure is caused by multiple factors occurring together that can’t be managed by traditional Statistical Process Control and Analysis tools that are typically used to prevent machine failure.

That’s where Predictive Maintenance comes in. With advances in Industry 4.0 initiatives, many automated manufacturing operations are incorporating machine learning into their strategies to take machine data to the next level and:

• Achieve over 95% + Equipment Availability

• Find patterns that predict catastrophic downtimes so that they are avoided altogether

• Reduce Time to Repair by up to 20% for downtimes where there is potential for Plastic Deformation of equipment parts

• Add millions in revenue per year due to an increase in productive time

Is your facility ready for Predictive Maintenance?

While the potential gains associated with Predictive Maintenance are impressive, not every manufacturer can implement machine learning to predict failures just yet. In fact, Predictive Maintenance will not begin to make an impact until you’ve reached 85% OEE. 

At MAJiK Systems, we recommend facilities follow an Industry 4.0 roadmap that looks like this: 

1. Implement the Visual Factory Module to map out the factory floor and collect, view, track, and improve KPIs

2. Use a combination of machine data and human input to improve product quality with the Quality Control and Management Module

3. Build an Availability Improvement plan with MAJiK’s Downtime Management Module

4. Consider Predictive Maintenance when previous modules have been implemented and OEE is at least 85%