Analytics and Blockchain for Data Integrity in the Pharmaceuticals Industry

Abstract

The data quantity explosion that we witnessed during the last two decades has lead industrial organizations to exploit this sheer amount of data for tasks that previously would seem impossible. However, larger data volumes, draw together a series of drawbacks that affect data quality and integrity and this becomes more evident in government supervised industrial settings. In many of such cases, public authorities have defined sets of principles (e.g., ALCOA+) regarding data management that industrial organizations must abide to, and either deliberate or not violation of these standards most often comes with severe legal consequences. As a matter of fact, in an effort to follow as much as humanly possible to such principles, pharmaceuticals industries invest heavily in resources to maintain high quality standards in their data urging for automated methods for calculating, monitoring and predicting compliance.Also, in complex manufacturing and production lines, data analytics provide means for real-time and continuous monitoring of large numbers of sensor variables and categorical or numerical values where higher order conclusions can be derived and taken into account when business process optimizations are considered. In this work we present an easy-to-use integrated platform for real-time raw sensor data monitoring and pre-processing in pharmaceuticals production lines combining blockchain storage for data integrity and deep-learning capabilities for data analytics. Additionally, the platform is able to calculate, monitor and predict compliance to the ALCOA+ set of principles, reducing substantially the time and effort needed to maintain and calculate such complex parameters manually. © 2022 IEEE