Smart manufacturing still remains critical challenges for pharmaceutical manufacturing. Here, an original data-driven engineering framework was proposed to tackle the challenges. Firstly, from sporadic indicators to five kinds of systematic quality characteristics, nearly 2,000,000 real-world data points were successively characterized from Ginkgo Folium tablet manufacturing. Then, from simplex to the multivariate system, the digital process capability diagnosis strategy was proposed by multivariate Cpk integrated Bootstrap-t. The Cpk of Ginkgo Folium extracts, granules, and tablets were discovered, which was 0.59, 0.42, and 0.78, respectively, indicating a relatively weak process capability, especially in granulating. Furthermore, the quality traceability was discovered from unit to end-to-end analysis, which decreased from 2.17 to 1.73. This further proved that attention should be paid to granulating to improve the quality characteristic. In conclusion, this paper provides a data-driven engineering strategy empowering industrial innovation to face the challenge of smart pharmaceutical manufacturing.
Keywords: Smart manufacturing, Data-driven engineering, Artificial intelligence, Information fusion, Process capability index, End-to-end, Quality traceability, Real-world Ginkgo Folium products
Graphical Abstract: available at https://ars.els-cdn.com/content/image/1-s2.0-S2211383522003628-ga1_lrg.jpg
The innovative data-driven engineering strategy integrated artificial intelligence (Al) algorithms was proposed for process capability and end-to-end quality traceability of intelligent pharmaceutical manufacturing.
