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Augmenting an MES

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.

Integrating Lab Automation Systems

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.

Monitoring High-Value R&D Materials

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.

MES Data Informs Analytics

Manufacturing Execution Systems are a rich source of data for manufacturing analytics. They often provide information and context that are valuable for optimizing manufacturing and are not available in any other systems. However, extracting analytics data sets from these systems is challenging. Data generated by an MES is not structured and labeled for retrieval, but rather is organized in the context of manufacturing recipes which vary from site to site, product to product, batch to batch, and over time. The result is that many companies are not realizing the full value of their MES data to support analytics.

One way to attack this challenge is to hire a highly trained MES engineer to add code to export MES data and context in the format needed for analytics. While straightforward to implement, this approach is often time-consuming and expensive. It also requires constant upkeep and can result in MES and reporting performance issues. This approach can also add considerably more load on recipe authoring and testing, both initially and during lifecycle change management.

Another tactic is for the analytics tools to query the MES recipe directly. This approach can be faster to implement as it requires no recipe changes and can be tailored to the unique needs of the specific analytics platform. Unfortunately, because of the variability of recipe execution, changes to recipes over time, and limitations of the analytics software, these queries are often complex, brittle, and difficult to maintain. This solution can also result in duplicative efforts as custom queries must be crafted for each analytics tool. 

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.

Ideal Unified Namespace Pilots

Two ideal UNS pilots are Continuous Process Verification (CPV) and Machine Learning. CPV because it involves batch-to-batch comparisons that may have been manufactured in different ways or locations. Customers ask, “How can I measure the quality of my product if the critical process parameters are measured differently?” Machine Learning is a great UNS example because you’re bringing in data from multiple systems and UNS is a good way to feed those models.

Machine Learning has demonstrated the potential to extract insights and value from data; however, relative to other domains, manufacturing has been slow to realize this value. While pilot studies and proof of concept projects abound, scaling these solutions has been challenging. When asked, manufacturers cite inadequate data quality, poor data integration, and weak data governance as major reasons for the inability to easily scale solutions. Specifically, poor data integration and weak data governance are barriers that must be overcome to scale the integration of the multiple OT and IT systems required for deployment of Machine Learning solutions.

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.

MES Maintains Chain of Identity

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.

MES Enables Predictability

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.

MES Reduces Operational Costs

There are potential applications for MES in both formulation and packaging with the end goal of getting from tech transfer to production faster and with the appropriate documentation. Tracking SKUs can be a difficult task considering all the formulation types, dosage strengths, packaging, labeling, and language requirements. A strong MES solution can help with inventory and supply chain management, as well as other functional activities such as resource planning, scheduling, and managing overall equipment effectiveness. Additionally, MES can drive lean manufacturing; thereby reducing waste, re-work, and downtime.

Generics and CMOs dedicate their business to providing services such as drug development, manufacturing and packaging for pharmaceutical companies who need production scalability. A source of long development and production lead times for product lifecycle is from data—especially when manufacturers have islands of automation. An ISA 95 MES solution can help integrate all levels of manufacturing systems—instrumentation (L0- PLCs, DCS, BAS), SCADA systems (L2), LIMS (L3), and ERP (L4)—to collect, measure, and analyze data.

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.

Continua at IFPAC® 2024

Learn About the Latest Developments in Advanced Manufacturing Technologies

March 3-6 | Bethesda, MD

IFPAC 2024 ‘Navigating the Future of Advanced Manufacturing’ features key presentations from the Continua Process Systems team. IFPAC is the preeminent forum for insightful discussions about the latest trends and applications in the fields of Continuous Manufacturing, PAT, Process Control, and Data Analytics. Meet our experts at the following sessions:

Digital Twin for Continuously Fed-Batch Bioreactor

Monday 3/4 | 1:05PM-5:40PM | White Flint Amphitheater
Mechanistic models and machine learning models can capture different aspects of process behavior and knowledge. Deploying these models offers the opportunity to improve real-time bioprocess monitoring to reduce risk and optimize production. In this presentation, Continua will demonstrate the development of a digital twin for a bioreactor with both machine learning and mechanistic models running in parallel.

  • Paul Brodbeck, Chief Technologist, Continua | Presenter & Author
  • Brian Sauerborn, Lead Engineer, Continua | Co-Author
  • Dan Wasser, Industry Consultant, Continua | Co-Author

Automation and Digitalization of Drug Manufacturing Processes

Wednesday 3/6 | 1:05PM-5:40PM | Grand Ballroom H
Training and deployment of machine learning for Spectral PAT have been a staple for the likes of drug products and drug substances. As the need for data and processing power to support emerging machine learning and AI applications grows, so too does the need for modernization of the data pipes that support these applications and operations. In this presentation, we will discuss the architecture we used to deploy the mechanistic and machine learning models that made up our mammalian cell bioreactor digital twin. We will talk about moving away from monolithic applications and point-to-point integrations into distributed applications that run with much higher computational potential.

  • Brian Sauerborn, Lead Engineer, Continua | Presenter & Author
  • Paul Brodbeck, Chief Technologist, Continua | Co-Author
  • Bradley Stutzman, Control Systems Engineer, Continua | Co-Author
  • Eimantas Macys, Control Systems Engineer, Continua | Co-Author