Data-driven process adjustment policies for quality improvement

Common objectives in machine learning research are to predict the output quality of manufacturing processes, to perform root cause analysis in case of reduced quality, and to propose intervention strategies. The cost of reduced quality must be weighed against the cost of the interventions, which depend on required downtime, personnel costs, and material costs. Furthermore, there is a risk of false negatives, i.e., failure to identify the true root causes, or false positives, i.e., adjustments that further reduce the quality. A policy for process adjustments describes when and where to perform interventions, and we say that a policy is worthwhile if it reduces the expected operational cost. In this paper, we describe a data-driven alarm and root cause analysis framework, that given a predictive and explanatory model trained on high-dimensional process and quality data, can be used to search for a worthwhile adjustment policy. The framework was evaluated on large-scale simulated process and quality data. We find that worthwhile adjustment policies can be derived also for problems with a large number of explanatory variables. Interestingly, the performance of the adjustment policies is almost exclusively driven by the quality of the model fits. Based on these results, we discuss key areas of future research, and how worthwhile adjustment policies can be implemented in real world applications.Originally included in thesis in manuscript form.Volume 237, Part B.