When an organization is manufacturing anything, it relies highly on the performance and efficiency of its machines. Unplanned equipment failure will directly affect organization’s 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.
With SIA platform (Softweb Intelligence and Analytics), 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 helps them to constantly improve the machine performance.