Life Sciences Pulse

Process and Product Innovation in Pharma- Digital Twin at Play

 
September 24, 2018

Gartner states Digital Twin (DT) as one of the top 10 strategic technology trends for 2018. It is interesting to note that within the life sciences industry, digital twin is fast becoming a force to reckon with. Digital human organs are being increasingly prototyped and are likely to become the norm in the near future. Moreover, digital twin is a helpful concept in creation of business models and differentiated value of services. As an example, a customer might be looking for using a medical device equipment for basic functionalities, but later on may desire to use advanced features. This can be enabled using a “touchless” processing approach by invoking the change in the digital twin, which in turn, “switches” on the physical product and updates the latest firmware for that feature as well as integrate it to billing functions.

Twin, while being integrated to the physical asset, can also be integrated to other twins, to a system of record, or to a system of intelligence.

Below are some of the key areas where applicability of digital twin may bring out fruitful outcomes for life sciences industry:

Model-based evaluation, verification, and optimization of shop floor

For the manufacturing space, digital twin can be deployed for a critical equipment, system, cell or even the whole plant to drive asset efficiency, de-bottleneck analysis, and drive consistent quality. Coupled with augmented reality, the digital twin enables viewing KPI In real time, enabling collaborative diagnosis between manufacturing and engineering teams to diagnose problems and resolve them.  

Extending and sweating the assets better

DT capabilities can be applied to health assessment as well as predictive maintenance of assets in manufacturing ecosystem. DT simulations, coupled with IoT, generate data constantly about assets’ performance. When this data is fed back in system, it can be helpful in gauging equipment in terms of design improvements, performance optimization, and key issues during lifetime of assets. For example, Sanofi has for its Geel plant deployed several sensors that measure 5000 parameters, generating $1 Billon data points for every manufacturing cycle.

De-bottlenecking material flows

While offline simulation has been used to debottleneck resource constraints to obtain higher throughputs, DT offers the ability to carry out the same in the real world. This can provide rapid feedback on how much time and resources in a warehouse will be needed when “peak conditions” are hit for smaller time window. It also helps to work out and plan flow paths when the entire warehouse operations are represented by digital twin.

Driving operational excellence of the new product from scale up to transfer to commercial production

Digital twin can be used to model the product process and use that information to get earlier full start up commercial production or drive higher sustainable yield levels and shorter set up times by utilizing the real time sensor data along with the simulations. The “golden batch” can become the digital twin to start with, and when coupled with other information like environmental data, operational patterns, and input material date, can be used to predict and influence the current production batch to the golden batch.

Capturing integrated regulatory information across product lifecycle

When digital twin is used for the product in the regulatory context, combined with the concept of digital thread, it can be established that right versions of the related documents are connected and completed. For example, it can assure that the right version of the Core Data Sheet is linked to the country version of the Product Insert, which in turn is linked to the right artwork or label version. The digital thread thus helps to predict or detect potential mismatch of product insert, which is being used to package a batch at the packaging line in the specified country.

The concept can also be used to create Digital Twin enabled Services like Remote Condition Monitoring. Quality Monitoring, Analyzer or Device Configuration updates, Asset Efficiency, User Experience (e.g. AR/VR based Instructions for Use, Learning Aids)

The Road Ahead

 

A digital twin requires an ecosystem to enable maximization of the value of investments. While organizations seek to develop the digital twin, it is important to understand the way data is collected and analyzed using models leading to aggregation, views, and controls. A combination of technologies such as AI, Cloud, and AR/VR would accentuate the returns on digital twin through new business or financial models.

Yezhuvath Vinesh Balakrishnan works with the Tata Consultancy Services (TCS) Life Sciences unit, focusing on supply chain management. He has over 22 years of experience in supply chain management, manufacturing, process excellence, and IT management across the pharmaceutical and chemical industries. He combines process orientation and analytical abilities with an in-depth understanding of technology to develop IT solutions that drive productivity, efficiency, and governance in the life sciences supply chain and manufacturing domains. Vinesh is actively involved in numerous supply chain and outsourcing transformation initiatives, and has helped conceptualize and develop innovative solutions, and enabled process optimization. An alumni of Birla Institute of Technology and Science (BITS Pilani), Vinesh holds a graduate degree in Chemical Engineering and a postgraduate degree in Mathematics.