Predictive Analytics

At AIT we use historical data, statistical algorithms and machine learning techniques to predict future events. The historical data is used to build a mathematical model that captures important trends. The goal is to go beyond knowing what happened in the past, to providing a best assessment of what will happen in the future. We can then suggest actions to take for optimal outcomes. Predictive analytics is therefore a powerful tool for detecting fraud, predicting customer behavior, improving operations and reducing risk.

Maintenance

Predictive maintenance refers to a holistic approach to monitoring the history and condition of industrial equipment in order to determine when maintenance activities should be carried out. AIT trains and applies machine-learning models to detect degradations of machines or machine parts, allowing them to predict future equipment failures. This provides an informed basis for proactive decisions by maintenance planning and can thus reduce the costs related to unexpected machine breakdowns. For example, maintenance can be brought forward, or additional resources can be requested. An additional cost saving occurs when maintenance is avoided for machines in a healthy condition.