This pharmaceutical customer has a large amount of data that needs to be contextualized and brought into their MES, but that’s not what an MES does best. Our project entails bringing that data into a Unified Namespace (UNS) ecosystem and then back into the MES, once contextualized. If you want to use your data outside of the MES, for instance in an ERP or other business systems, then the number of links in and out can grow exponentially. Our eventual goal is to get away from proprietary software solutions that sit in the middle of your stack. When you move to a UNS, you can use any software and it’s easy to upgrade or switch, as well as much more flexible. Contrary to what many people may think, security is better too. Each layer has a firewall and the data is well-protected; if you open more ports, there’s inherently more risk. By the nature of the way a UNS works, getting IOT data out is less risky because you’re publishing out with no need to open inbound ports. In fact, Gartner is now recommending this architecture in lieu of the traditional Purdue model.

Every pharmaceutical company uses manual laboratory automation systems, like the Nova FLEX cell culture analyzer, and frequently they need to perform calculations based on the results. Continua worked with this company to integrate their Nova FLEX data with their plant control system to automate the required actions based on the analysis results and to publish this data into the UNS for visibility across the business area.

This biotech company had already started its Unified Namespace (UNS) journey with a proof-of-concept project in mind, our recommended approach. As one of the most advanced companies in their industry, they wanted to use the UNS technology platform as a way to move a manual process—that of monitoring and tracking its highly valuable R&D materials data throughout the earliest stages of drug development—to a cloud-based, contextualized, and accessible asset. UNS is so new, that it is not well understood by many companies and is too risky to roll out on a larger scale. Both the customer and Continua, while equally committed to UNS architecture, feel that these projects are best approached in an agile, iterative way, with engineering adjustments made to find the best path forward.

A third solution is to create a data presentation or abstraction layer, for example a Unified Namespace (UNS), as multiple systems want similar data and context from an MES. This third approach takes time and thought and may bring with it new systems and data flows—you will likely want to start with a pilot project. Done properly, the result can be a comprehensive and scalable solution that is robust and efficient to maintain. Depending on your situation, any of the above methods might be best.

At Continua, our focus is to understand what data you need and how you want to use it. That understanding then informs the solutions we recommend, whether it be a targeted point solution that addresses an immediate need or a broader, holistic solution to provide a foundation for a wide range of analytics goals.

The concept of a Unified Name Space (UNS) directly addresses these barriers. UNS draws on modern distributed technology & architecture and combines it with hierarchical data naming and organization.  The result is a centralized and standard presentation of OT data to enterprise systems. UNS is a scalable approach that addresses data integration and governance issues, removing two of the main impediments to the wide deployment of Machine Learning models.

While UNS can enable wider deployment of Machine Learning in manufacturing, its value extends far beyond any single use case. Having a single source of truth for manufacturing data can reduce the cost, simplify the deployment, and improve the robustness of any analytics or reporting solution that depends on manufacturing data. UNS should be considered as a key component of any digital transformation strategy.

With Cell/Gene Therapy, the treatment must be provided ‘vein to vein’ in a limited period of time—with the same rigorous controls as any other biologic. This manufacturer, not unlike others in this industry, also had additional process challenges to accommodate cell count variations in the starting material. In addition, this customer’s autologous process requires maintaining strict chain of identity across the supply chain with no room for error.

In Cell/Gene Therapy, the uniqueness of each batch requires it to be adjusted for every patient’s starting material. That’s where MES comes in: MES maintains control over a complex manual process; MES automatically tracks materials usage to an individual patient lot and maintains identity of the lot; and MES recipes help accommodate varied growth timelines for varying starting material.

It takes weeks or months to release a vaccine batch to the FDA for review. This big pharma customer previously had a complex batch record consisting of over 1,200 pages and taking over 30 days to complete. Every page had multiple opportunities for human error including incorrect entries, miscalculations, and executing steps out of order. Administrative controls as well as rigorous and time-consuming batch record reviews were required at the end of production to ensure product quality.

By implementing an electronic batch record in MES, this pharma manufacturer practically eliminated opportunities for error—enabling more predictable batch release cycle times, and ultimately reducing finished goods inventory.

Managing the challenges of data integration for efficient manufacturing is one aspect of Generic and CMO business. The most impactful use case for an MES solution is compliance. In 2022, the second most Inspection/FDA 483 observation was FDA 21 CFR 211.192 Cite ID 2027—investigations of discrepancies and failures. By sufficiently collecting, measuring, and analyzing data with an ISA 88 MES solution, batch reporting can be completed sooner, but most importantly, to meet compliance. Put our decades of MES experience to work for your data integration, recipe authoring, and batch reporting needs—regardless of your platform.

Here’s the big deal:
When you’re doing 5,000 production orders per year, saving 2 hours per order translates into 10,000 hours saved.