Organisations around the world and across all industries are in the midst of a digital revolution, using technology and the Internet of Things (IoT) to make sure they are more connected and more efficient than ever. With these advancements in technology, companies can IoT-enable their energy-consuming assets to easily monitor their energy usage and gain actionable energy intelligence based on the collected data.
The saying goes that you can’t manage what you can’t measure – this certainly holds true for energy consumption. By tracking and clearly visualising the energy consumption levels of each of your assets, you can obtain valuable data that enables you to immediately identify and resolve problems, ultimately resulting in better performance and a better bottom line.
In this challenging economic landscape, having a clear view of your energy estate and obtaining the granular data you need to drive decision-making is critical to reducing costs, boosting efficiency, and driving operational performance. Read on for examples that demonstrate how energy data can provide organisations with the intelligence they need to drive decision-making and boost operational efficiency.
Predictive maintenance ensures equipment health
Continued growth of equipment automation in operational processes increases the importance of proactively maintaining critical equipment to ensure system reliability and uptime. Predictive maintenance is the practice of using data analysis and other technological tools to detect defects and fix them before a costly failure occurs. This practice is known to be a huge cost-saver with the additional benefit of simultaneously driving increased efficiency. According to the United States Department of Energy, predictive maintenance can result in 70% fewer breakdowns, 35-45% reduction in downtime of machinery, 25-30% reduction in maintenance costs and a tenfold increase in ROI.
So how can energy consumption data assist in predictive maintenance efforts? Let’s take the example of a company that has two machines that serve an identical purpose. To the naked eye, both machines are working perfectly, but without any internal data, it’s impossible to know what is actually happening under the hood.
If we were to monitor the energy usage of both machines and compare them to each other, we might well see that one machine is cycling normally, having both on and off periods based on time of day or expected operation protocols. The second machine, however, might be working much harder than it is supposed to be. It is getting the expected end-results, but by monitoring its energy usage, we have uncovered – as demonstrated in the image below – that instead of cycling on and off like its counterpart machine, it is constantly running, wasting energy, driving up costs and signaling an internal problem with the machine.