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How Will Data Analytics, AI and Machine Learning Impact Pharma Manufacturing?
Founder and Chief Executive Officer, Ephesoft
Los Angeles, CA
When considering the role of Artificial Intelligence and machine learning in manufacturing, we tend to think of hardware, like robotic arms on an assembly line. In drug discovery and other pharmaceutical development processes, it’s become evident that AI can expedite drug development cycles and eliminate wasteful guesswork between trials. But, pharmaceutical manufacturing can also benefit from more logistics-focused AI that helps manage data and regulatory document processes.
IT departments in pharmaceutical manufacturing – as in any industry with significant federal oversight – are burdened by regulatory processes requiring volumes of formal documentation.
Enterprise data-spheres are a growing challenge thanks to years of massive documentation and an influx of new content and images. This data can be drawn from a variety of sources, either electronically or physical: drug labels, contracts (legal), purchase orders, invoices, risks, personnel files (human resources), mail, distribution channels, and packaging information – amongst others.
The underlying enormity of the problem is that all of these documents – often millions – contain mostly information that is not easily accessible, searchable or usable until employees manually create or transform the data into a structured format. Using technology to not only automatically capture the data, but to learn (through machine learning algorithms) to recognize that data, classify it, extract the right data and export it into an existing system can impact the organization for the better.
AI-based technology, embedded with rules and learning tools, can alleviate manual data entry challenges company-wide. This kind of data digitization is among the leading initiatives for companies across all industries. Gartner recently published “2019 CIO Agenda: Global Perspectives,” a global survey whose results indicated that digital initiatives and revenue growth were ranked as the highest priorities for medium and large organizations.
Intelligent business and digital transformation projects using AI, machine learning and robotic process automation (RPA) tools are making organizations more competitive and more efficient, enhancing their ability to scale and improve customer experiences. Data is essential to feeding digital transformation processes. With accessible, structured data available, pharmaceutical manufacturing organizations can gain insight and make better business decisions, based on a comprehensive set of data. This can pave the way for data analytics and intelligent process automation (IPA).
As pharmaceutical manufacturers assess how data analytics, AI and machine learning will impact their industry, the key takeaway is that any organization with high volumes of content and data must unlock that data to undergo digital transformation. This first step is critical to operational success and strategic growth. Technology advancements with AI and machine learning, including supervised machine learning, have enabled pharmaceutical manufacturing companies to be more nimble, productive and accurate. It’s not just in the lab either – much of these large document-heavy processes are in departments dispersed throughout the organization.
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