Artificial intelligence and predictive analytics are empowering organizations to confidently predict future issues — from the manufacturing line to the hospital floor.
Power of AI-When it comes to weather events that may affect operations, today’s enterprises have great insights into the future — thanks to satellites and advanced forecasting systems that continue to advance technologically. The same holds true for sales and revenue forecasting, as companies leverage sophisticated predictive analytics to gain a clearer view of their financial future.
Now, enterprises are taking their predictive capabilities to new heights, thanks to the power of artificial intelligence applications driven by high performance computing systems. This new breed of predictive applications is a cornerstone to making better business decisions, keeping systems and equipment in top shape, understanding the movement of markets and much more. In many cases, these forward-looking applications are both predictive and prescriptive, meaning they tell you what’s likely to happen and recommend steps you can take to address emerging issues and influence outcomes.
Let’s look at some specific use cases for AI-driven predictive applications across a range of industries.
Enabling predictive maintenance
In many industries, predictive systems driven by machine learning techniques are helping operators keep equipment up and running at an optimal performance level while reducing maintenance costs. These systems monitor the performance and condition of equipment to anticipate failures and enable proactive maintenance.
A few examples:
- Smart manufacturers are using AI systems in conjunction with data from sensors and the Internet of Things to predict and prevent machine failures. The goal is to use predictive maintenance to avoid issues on the manufacturing line, resolve problems quickly and proactively, and minimize disruption to operations.
- Wind-energy producers are using AI systems in conjunction with data from sensors and the Internet of Things to predict the likelihood of wind turbine failures and proactively address issues that may arise.
- Telcom providers are using machine and deep learning systems to guide preventative and predictive maintenance related actions to reduce downtime of mission-critical systems, such as telephone billing clusters.