Predictive maintenance

Go beyond historic analytics with AI-powered predictive maintenance

Predictive maintenance

When an organization is manufacturing anything, it relies highly on the performance and efficiency of its machines. Unplanned equipment failure will directly affect the organizations’ ability to meet customers’ demands or expectations. Companies need to know how their assets are operating currently and when they might need an upgrade to keep them running without interruption.

Data science presents businesses with an opportunity to drive innovation and stay ahead of the curve. Thanks to the recent advancements in machine communication technologies and sensors, predictive maintenance has come to the forefront. Machines are monitored continuously, data is gathered and machine learning algorithms are used to identify looming faults and calculate the optimal time for the next maintenance by performing predictive analysis. In this way, enterprises can get deeper insights and predict situations that could become a bottleneck in the future and take necessary steps.

Using machine learning to achieve optimum asset utilization

With data intelligence and analytics services, our goal is to empower companies with a complete and highly accurate picture of their assets in a variety of environments to eliminate blind spots, avoid unexpected machine failures and increase customer satisfaction. Having a prescriptive analytics program powered by technologies like machine learning and AI in place will not only predict required maintenance in machines but also suggest the course of action to be taken. Today, a lot of companies are reaping the benefits of predictive maintenance as it facilitates fault detection during early stages and constantly improves the machine’s performance.

Make the most out of your data with data intelligence

  • Failure prediction – Companies can now predict the machine failures even before they occur. This will reduce the number of emergency breakdown calls.
  • Service time prediction – Businesses will be acknowledged by the time taken by a technician to complete one call and can allocate work to him based on his workload.
  • Smart allocation – From the data, it can be derived which technician is ideal for fixing a particular type of machine issue. This helps in allocating the most competent technician for a particular service call.
  • Inventory acquisition – The parts that need to be replaced more often can be kept in stock so that the customers do not have to wait for days for the right part to arrive.

Talk to us about your data complexities and let data intelligence address them