Digital twin technology has gained popularity in recent years and has been regarded as a potential solution for life sciences manufacturers looking to predict process conditions.

Simply put, digital twins are virtual models that replicate the behavior of a physical asset, process, or product. It’s a concept that has been around for some time, but advancements in machine learning (ML) and the availability of massive historical datasets have made digital twins more accessible for a wide range of applications.

Building a digital twin requires a large historical dataset, high data quality and granularity, fast data access, a large GPU for model development and real-time predictions, and a supporting data structure to manage the development, deployment, and maintenance of ML models. With these requirements met, digital twins can be applied to areas like process optimization, equipment life cycle management, energy reduction, and safety improvements.

For life sciences manufacturers, digital twins have several benefits, such as replicating and tracking manufacturing processes to monitor attributes more efficiently for FDA regulations. They also improve data integrity by providing access to secured and audited data, allowing organizations to move from batch manufacturing to Process Analytical Technology (PAT) continuous manufacturing. Such a move can enhance productivity, reduce downtime, enable secured data transfer between CMOs and tier 1 manufacturers, and reduce infrastructure costs by moving data to the cloud.

 

Digital twin industrial technology and manufacturing automation technology.

 

Digital twin technology is especially useful in batch processes. By using 30-second interpolated data and a window of past data, the technology can predict future data points within a 5-minute interval. Offering more than just the ability to predict process conditions, digital twin technology also provides an unparalleled level of explanatory power by revealing the underlying dynamics that drive the process.

The availability of enterprise-level data historians and deep learning libraries enables digital twins to be implemented on an equipment and process level throughout manufacturing, providing insights into process dynamics that were not previously available. This improves data integrity and access, and leads to increased trust and data transparency with partners.

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Overall, digital twin technology offers life sciences manufacturers a promising solution to streamline processes and improve data quality and integrity. By implementing this technology, manufacturers can benefit from reduced downtime, increased productivity, and a more efficient data management system.

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Nordic contact

Lone Harboe serves as a consulting leader of Cognizant Life Sciences in the Nordic region, where she advises clients on digital transformation to improve business outcomes and gain competitive advance.

Email: lone.harboe@cognizant.comAbout Cognizant

Cognizant’s Life Sciences Manufacturing group provides end-to-end digital transformation solutions that keep systems running, improve supply chain efficiencies, and support clients’ Manufacturing 4.0 initiatives. We specialize in delivering solutions and services across batch automation, data infrastructure & intelligence, MES, lab automation, CSV and digital technologies to manage, control and optimize manufacturing. Our team has deep life sciences expertise, and we work closely with our clients to achieve a shared vision of advancing science and improving patient outcomes. Cognizant’s global network comprises more than 30,000 skilled life sciences professionals who work across global delivery centers in 37 countries to deliver and support our clients’ digital transformation initiatives.