Each year, approximately 100,000 employees of a large commercial airline submit dozens of thousands of safety reports in various formats. It is an analyst’s responsibility to filter through everything, evaluate the reports, and suggest the best course of action. The airline has created a number of machine learning-based predictive models using the huge data from their QMS to increase the speed and accuracy of assessments and the significance of actions. Operational effectiveness subsequently improves significantly.
Machine learning (ML) techniques are uncovering hidden patterns in data that are challenging for the human mind or sight to comprehend in all types of sectors, empowering quality and safety departments with a new form of intelligence. Machine learning, a branch of artificial intelligence, automates the creation of analytical models. With machine learning, an algorithm picks up patterns and makes judgments with little to no human involvement by learning from the data it has been taught (even though the reasoning of people will always require it to be part of the equation).
A comparison of data analytics and machine learning
All of the necessary components for using data-driven decision-making effectively are provided by traditional data analytics. All crucial data is housed in contemporary data analytics solutions for quality management systems. Additionally, it provides dashboards for data visualization and trend analysis linked to important topics including non-conformance and corrective measures, root causes, safety management, and injury analysis.
However, a quality management system that makes use of machine learning raises the level of accuracy and insight in data analysis of quality and safety measures. For instance, using data analytics now makes it relatively simple to spot problems, such as improperly maintained equipment, and address them. It is considerably more challenging to find systemic problems that go unnoticed but could pose a serious risk, such as the fact that personnel utilizing the equipment never had current training.
Because it establishes associations between disparate data in different formats like video, graphics, rich texts, and others. ML is able to achieve this in a way that humans cannot. While analytics can search for exact matches, machine learning can create correlations between items that humans may never consider. It has been argued that data analytics aids individuals in finding the items they anticipate searching for, but true machine learning can go further and utilize intelligence to reveal the items they ought to be searching for.
An Objective Method For Machine Learning
The use of intelligent trending or predictive analysis using machine learning could immediately benefit quality and safety management. Professionals can predict exactly what will happen in the future using large historical data sets, moving beyond simple extrapolation of historical counts and using hundreds of related data points, with a cone of confidence (in comparison to the cone of uncertainty) that expresses the margin of error over time. It requires a broad perspective of potential future outcomes informed by facts rather than assumptions.
Getting Started with Machine Learning
Despite the fact that most businesses believe AI is crucial to achieving their digital transformation goals, the incorporation of machine learning into quality management systems is still in its infancy. It is true that it will take some time for it to be widely used in quality management because it calls for a systemic shift in how businesses run in addition to skilled employees to implement and optimize it.
