7 uses for real-time machine data

7 uses for real-time machine performance data

When you connect industrial assets – machines in your operation or machine products you have placed on customer sites – to the internet of things (IoT), they immediately generate data streams. You can assess just about any machine condition in real time.

Here are some practical scenarios for making that data pay off.

#1: Implement predictive maintenance

One of the most common and effective usages for machine data is predictive maintenance. By combining real-time data with machine histories and maintenance records, you can spot the subtle changes that may indicate a potential breakdown in the future. A technician can perform the repair while the machine is still working properly. You avoid the costs and productivity disruption of unscheduled downtime.

Data analysis, together with your technicians’ expertise, will help you pinpoint the best time for initiating maintenance – not so early that it is wasteful, nor so late that you risk a breakdown.

#2: Improve machine performance and productivity

When you rely on machine data to implement predictive maintenance, your primary concern is probably the machine’s uptime. But you can also use real-time data to assess your machines’ performance – usually measured in terms of throughput – and quality output.

Together, uptime or availability, performance, and quality make up the compound metric of Overall Equipment Effectiveness (OEE), a critical value as you improve the productivity of your operation.

#3: Enhance operator skills

Machine data can tell you a lot about the skills of your operators – especially when a particular machine appears to be performing better or worse for different people. If the machine itself works as it should, operators, materials, and environments can make a huge difference in its productivity.

Maybe some of your workers have found ways to work more effectively with a machine with better handling or timing. Others may achieve lower performance or quality with the same machine. Real-time machine data, together with close observation of the workers’ actions, may indicate how you could make an improvement.

Especially if the machines in question are specialty vehicles or construction equipment, data regarding their use of fuel and uptime may help you identify where operator training can cut costs and increase productivity.

#4: Revolutionize customer relationships

Would your customers welcome more value? You can provide predictive maintenance based on real-time machine condition data as a service that is both profitable and valuable. Using the same data, you might also help them exceed their quality goals and optimize their throughput performance.

Evidence-based proactive intervention in your customers’ best interests can make a big difference in increasing customer loyalty and protecting your accounts from competitive threats.

#5: Generate more revenue

If you can help your customers make better use of their machinery, maybe you can also use machine performance data to improve the engineering of the machine products you sell them. Some customers might be willing to work with you to design machine improvements that they benefit from exclusively. Or you may gather the right insight to enhance your product offerings and make them more compelling.

So far, the leading and larger manufacturers are the ones using real-time machine data to engage with customers in evolving their products. There is still a lot of opportunity to sharpen your competitive edge by adopting this practice.

#6: Achieve more with the IoT 

Using real-time data to make your own or your customers’ machines perform better is an excellent use case for the IoT. Lots of companies know there is value potential in the IoT, but they are grappling with exactly how to translate that into their business. You’re already ahead of them.

Once you’ve proved the value of IoT data in your operation, see whether it makes sense to extend it – to other machines, lines of machines, and your production facilities and their infrastructures. The risks of doing so are low, but the potential savings and productivity improvements can be substantial.

#7: Introduce process and supply chain improvements

Your machines run in the context of many business processes that different teams contribute to. Both downstream and upstream, a change in performance or quality based on real-time machine data evidence may drive efficiencies.

You may need to change warehouse operations to keep up with production, for example. Or it may become necessary to source materials and schedule their shipments differently.

To make your machines’ quality and productivity improvements sustainable, you might need to make adjustments in your supply chain. Choosing a vendor who can provide higher-quality parts and materials, or ship parts in a more timely manner, could be one more positive outcome of your machine condition monitoring.

Learn how condition-based monitoring enables proactive maintenance