Automated Data Review in Secondary Pharmaceutical Manufacturing by Pattern Recognition Techniques

Abstract A methodology is proposed to support the periodic review of manufacturing data in the pharmaceutical industry. Pattern recognition techniques are employed to isolate and analyze operation-relevant data segments to the purpose of automatically extracting the information embedded in large databases of secondary manufacturing systems. The results achieved by testing the proposed methodology on two six-month datasets of a commercial-scale drying unit demonstrate the potential of this approach, which can be easily extended to other manufacturing operations.