Building Process Understanding for Vaccine Manufacturing Using Data Mining

Abstract

The production of vaccines is a complex biological process, with long cycle times and high variation in raw materials, growth rates, and test methods. Hundreds of variables are monitored for every batch of vaccine produced; however, the relationships between product quality and process variables are difficult to quantify. We describe how mining historical process data using random forests and partial least squares techniques enabled us to identify the drivers of variability in bulk vaccine yield and to implement new controls which significantly reduced the variation.

Churn management optimization with controllable marketing variables and associated management costs

“…In a recent study (Laitinen, 2008), PLS is utilized in building a predictive system with some success to assess failure probability in small-and medium-sized Finnish firms using financial and non-financial variables and reorganization plan information. In several other studies (Qin and McAvoy, 1992;Wiener et al, 2010;Lee et al, 2011;Kim et al, 2012), PLS prediction models not only showed superior or comparable performance against other data mining algorithms, but also showed the usefulness of identifying key input variables for biology and marketing applications. However, we have not seen papers that apply a PLS model both as a prediction model and as an optimization model for churn marketing management.…”