D iagnosis Failure Cause of complex Pharmaceutical System by Bayes Learning for Decision Support

This work proposes a real application of diagnosis protocol for complex pharmaceutical process drifts. Main challenge is to identify and classify failure causes of production process. The model which we have proposed is structured in the causal graph form, named “Hierarchical Naïve Bayes” (HNB) formalism. Our contribution is the presentation of a methodology that allows developing flexibility in particular complex pharmaceutical production context. A data extraction and processing prototype is performed in this paper from real pharmacy company to build Bayesian model. Diagnosis results are decision support elements that built based on HNB probabilities. Furthermore, this work can be applied in order to improve production quality in businesses competition.