In just the last 10 years, biopharmaceutical supply chains have changed dramatically. The single-source manufacturing model is becoming much rarer, making way for multi-enterprise manufacturing collaborations and partnerships to better meet the changing needs of the life sciences marketplace.
In collaboration with other life sciences leaders, Emerson’s Michalle Adkins contributed to a BioPhorum paper outlining a vision for digital maturity in the integration between biomanufacturers and partner organizations. The paper explores the benefits for improved digital integration to improve the patient experience while simultaneously improving business outcomes.
Digital integration breeds standardization
At the heart of this transition to improved digital integration is standardizing approaches to integration. Currently, many organizations operate under highly complex digital integration architectures. As the paper details,
“This current ad hoc point-to-point solution approach is ineffective and unsustainable in the long term as biopharmaceutical supply chains become more complex and event driven.”
The experts suggest that an industry standard approach to data and information sharing is the key to improving operations and maintaining scalability as the trend toward collaborations and partnerships continues to shape the life sciences landscape.
At the heart: tech transfer
The paper focuses on four key areas where accurate and timely data exchange is critical to the success of more collaborative manufacturing:
- Supply Chain
- Technology Transfer
All these areas can benefit from digital technologies to improve performance, particularly as digital maturity varies widely in each area between different organizations. Technology transfer strategies, for example, vary wildly in digital maturity between organizations. While most life sciences manufacturers have moved from paper records to digital, there are many digital solutions of varying levels of efficiency and compatibility with external systems. The experts elaborate,
“All the member companies work with partner organizations at a different level of digital maturity. Even within one partnership, it is normal to have integration points at a number of levels of digital maturity.”
Finding a standardization path
Fortunately, new tools and technologies are improving alignment between groups. This means improved bidirectional sharing of data and process knowledge, both externally and internally. First and foremost, groups across the production pipeline need improved methods to standardize data so it can be used easily by anyone at any time. Technologies like Process and Knowledge Management™ (PKM) software can bridge the information gap in technology transfer.
PKM software digitizes the recipe development process, creating a consistent, standardized repository of information across the drug development pipeline, from early-stage research through late-stage commercial manufacturing. Global sites, cross-functional teams, and contract manufacturing organizations can all work together more easily to seamlessly execute technology transfer for faster speed to market.
Coupled with industrial information management software like inmation industrial information management, teams can view all relevant information in real time via integrated performance dashboards. Critical data is always available on a wide array of devices, making sure all key personnel have immediate visibility of essential information when and where they need it.
Bringing business together
The BioPhorum paper concludes that manufacturers need improved and more standardized digital integration between organizations to successfully ensure timely delivery of critical treatments to patients in need. Software available today can help organizations begin this journey but must be implemented properly to avoid increasing overall complexity for manufacturers and their partners.
To learn many of the additional benefits the BioPhorum experts lay out for the life sciences industry, you can download and read the paper in its entirety. And I’d love to hear some of your strategies for standardizing and normalizing data to make it more useful across your entire development pipeline. Feel free to comment below to share your ideas and experiences.