A major commercial airline receives tens of thousands of safety reports each year, coming from nearly 100,000 employees, in various formats. It’s an analyst’s job to sort through all of it, assess the reports, and recommend appropriate action. Utilizing extensive data collected in their QMS, the airline has developed several predictive models based on machine learning to improve the speed and accuracy of the assessments, and the relevance of actions. The result is a significant improvement in operational efficiency.
In all types of industries, machine learning (ML) tools are finding the needle in the haystack of data, augmenting quality and safety professionals with a new kind of intelligence that can unlock hidden data patterns that are impossible for the human mind or eye to absorb.