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.
Automated Data Review in Secondary Pharmaceutical Manufacturing by Pattern Recognition Techniques
MENEGHETTI, NATASCIA;FACCO, PIERANTONIO;BEZZO, FABRIZIO;BAROLO, MASSIMILIANO
2016
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.
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