Speakers
Description
We describe a case study for modeling manufacturing data from a chemical process. The goal of the research was to identify optimal settings for the controllable factors in the manufacturing process, such that quality of the product was kept high while minimizing costs. We used structural equation modeling (SEM) to fit multivariate time series models that captured the complexity of the multivariate associations between the numerous process variables. Using the model-implied covariance matrix from SEM, we then created a prediction profiler that enabled estimation of optimal settings for controllable factors. Results were validated by domain experts and by comparing predictions against those of a thermodynamic model. After successful validation, the SEM and profiler results were tested in the chemical plant with positive outcomes; the optimized predicted settings pointed in the correct direction for optimizing quality and cost. We conclude by outlining the challenges in modeling these data with methodology that is often used in social and behavioral sciences, rather than in engineering.