Over the years, automotive exterior parts have become more complex and substantially larger, yet are molded at faster cycle times. The transformation in design and challenging manufacturing demands have driven changes in tool design, hot runner design, material formulation and molding machine functionality. With these increasing challenges, we have to ask ourselves if conventional methods of quality control, which are typically univariate, are still effective. The short answer is no. This presentation demonstrates how multivariate analysis extracts pertinent information from large amounts of complex data. It is then able to identify the correlation structure and relationships that exist between multiple process variables and present it visually. We’ll present a project comparing univariate and multivariate approaches. These methods hold the promise to both reduce the dependency on subjective, visual inspection and make lights-out manufacturing more viable.