Summary:

While traditional quality analytics have provided manufacturers with a rear-facing view of operations that enable them to identify when, where, how and why issues have occurred, most have been unable to harness their quality data to predict what is likely to happen in the future. Referred to as “predictive analytics,” this capability enables a manufacturer to turn historic data into a predictive model that provides proactive and actionable information that can be used to both mitigate risk and identify those opportunities that are likely to generate the greatest value for its company, ultimately answering the question, “what will happen.”

This paper provides an overview of predictive analytics and how it can be applied to quality operations, presents use cases demonstrating how manufacturers from various industries can leverage this capability to derive valuable insights from quality data, and offers best practices for implementation.