Glass company predicts breakdowns with 85% accuracy, two days in advance

An international glass manufacturer was experiencing equipment failures that led to unexpected costs and low production availability. Using the Neal Analytics Predictive Maintenance solution, which includes Microsoft Azure Machine Learning, the company can identify variables and anomalies that influence equipment failure. This solution enables the company to predict breakdowns two days in advance with 85% accuracy and take appropriate action, resulting in lower maintenance costs and less unplanned downtime.