A chemical plant often uses lab analyses to monitor product quality and to adjust the production process. These are, however, only conducted every few hours. In between two analyses, production quality is not known and can deviate. Furthermore, quality variations are sometimes hard to distinguish from measurement errors. As a result, plant operators do not always know which process parameters to change if the quality deviates. In some cases, the issues described above can be dealt with by developing an inferential model for the product quality. For a particular production plant in BASF Antwerp, such a model has been developed in JMP Pro. The model has been developed from a raw data set with more than 8,000 process parameters containing numerous missing values and outliers, and has good predictive power. The techniques used include data cleaning with imputation and outlier detection, feature selection with bootstrap forest partitioning, key parameter identification with multivariate analysis and variable clustering, and elastic net regression with training and validation data for final model development. This presentation will provide an overview of the general approach, specific tips and tricks in developing the model with JMP, and using JMP as a tool to discuss results with plant personnel. The analysis helps the plant to better understand the parameters that affect the product quality, while the predictive model offers decision support on when and how to adjust to production process.