Structured data frameworks needed to enable efficient knowledge management.
Updated: Apr 8, 2020
While many industries have moved to adopt the latest technologies and practices to improve their operational efficiency and lower costs of production, the pharmaceutical industry has been slow to keep pace, especially with respect to manufacturing. In a McKinsey report discussing the digital maturity of nine major industry sectors, the pharma industry ranked 8th only ahead of the public sector.
Recent initiatives within the industry to improve digital maturity have focused specifically on incorporating Industry 4.0 principles with increased attention to data-driven approaches (aka Pharma 4.0). However, one of the barriers to the adoption of Pharma 4.0 is the lack of integrated knowledge and quality risk management systems that facilitate collaboration and access to this data. Companies still rely heavily on Microsoft Office solutions and electronic quality management systems to catalog and track vast amounts of information related to the development of their manufacturing processes. With the lack of integration and structure, valuable data and information are dispersed in many files and buried in documents reducing visibility into key risks and hindering robust process understanding.
A solution to this chronic problem is to move away from the unstructured data frameworks of documents and spreadsheets to structured data frameworks, such as databases, to catalog and track large amounts of data related to the development of complex manufacturing processes. Structured frameworks simplify the development and analysis of multi-dimensional data sets and the tracking of the evolution of these data sets over time. Data in different dimensions can be linked in ways not currently possible and integrated with analysis tools for the data-driven understanding of manufacturing processes leveraging the new tools of AI/ML.
Furthermore, structured frameworks enable the visualization of large data sets. Instead of parsing through unstructured narratives in documents and rows and columns of spreadsheets, information can be visualized as nodes in a multi-dimensional network graph. Network graph technology leverages the principles of visual perception (colors, shapes, clusters, connections, etc.) to display large amounts of information in a way that quickly communicates key aspects whether it be risk, significance, or cause and effect relationships providing much-needed transparency into complex processes and visibility into key risks.
The practical significance of this shift is to simplify heretofore difficult tasks such as
Traceability between patient, product, and process requirements
Integration of risk assessment with analytics for data-driven resource allocation
Development of rational control strategies
Historical tracking of process development for regulatory assessment
Technology transfer throughout the product lifecycle
Post-approval change management
Ultimately, this enhanced transparency and visibility bring the opportunity to break down information silos, improve collaboration, and minimize the compliance burden to speed new therapies to market with reduced development costs.
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