Gartner’s study reveals that 75% of organizations implementing IoT already use digital twins or plan to within a year. This includes pharma companies that do drug manufacturing in-house.
There are obvious reasons for manufacturing plants to reduce their workforce during and post the pandemic. The pharmaceutical industry, which is working towards building effective drug and medications to fight COVID-19, can’t stop production and hence need innovative technology to continue. Pharma digital twins aim to predict quality batches, induce waste management, and adhere to the protocols of social distancing during drug manufacturing processes.
Machines in production lines generate vast amounts of data. This data delivers critical insights using sophisticated analytics and algorithms. Decision-makers can weaponize these insights to improve the overall manufacturing process and succeed within aggressive timeframes.
Unfortunately, more than 95% of pharma manufacturers do not have access to this crucial data. Worse, those who possess the data lack the expertise to derive actionable insights.
Gramener’s pharma digital twin solution enables manufacturers to extract data from their machines and use them to improve production.
Production managers can also install IoT devices and sensors on the machines, gaining additional insights and delivering golden batch drug yield.
How? Let’s find out.
Find out the use cases, examples, and Solutions of Digital Twin
Digital Twin Technology Overview Guide
Table of Contents
Digital twins are such virtual constructs that mirror the functioning of a physical construct – an object or a process. The digital twin separates different systems from physical things. Digital Twin for predictive maintenance in the manufacturing industry is by far the best use case.
For example, a process digital twin of a pharma supply chain could contain real-time data for the different parts of the supply chain. While there may be different claimants for data for different parts of the chain, all the data is centrally contained in a single repository, and all claimants can access the same place for getting the data, thereby avoiding waste or duplication of effort and resources.
The Gartner survey shows that digital twins are not restricted to any single industry. Examples of digital twins include logistics, retail, utilities, industrial manufacturing, consumer goods, pharma manufacturing, healthcare, etc. The popularity of digital twins is increasing because they can decrease the complexity of IoT environments while increasing their efficiency.
Process digital twins can also ease human decision-making when the digital twin data is combined with business rules, algorithms for optimization, or prescriptive analytics technologies. An example of predictive analytics in the pharma industry is the digitalization of medical products such as insulin pens.
New insulin pens are designed from a digital platform. With the help of historical data and predictive analytics, it can be found whether the new variant fits the existing production equipment and what changes, if any, might be needed in terms of design.
As per a 2019 Gartner survey, 75% of organizations implementing the Internet of Things were either already using or planning to use digital twins within a year. A 2021 Gartner report says that 25% of large enterprises will deploy 1000 digital twin templates. 1 million digital twin instances by 2025.
By the same year, it is expected that 25% of new data and analytics investment in large enterprises will likely be used for digital twin implementation and support. The same report also predicts that digital twins created using the best practices in data and analytics through 2027 will most likely retain their value 50% longer than the ones using current methods.
The rapid adoption of digital twins is primarily due to technology vendors’ teaching and marketing. However, it is also because the digital twin model delivers business value and is increasingly becoming a part of digital strategies and enterprise Internet of Things. Moreover, digital twins serve multiple business objectives making the technology highly scalable across different industries. It also includes digital twins in pharma manufacturing.
Simulation twin applications allow pharma manufacturers to simulate their manufacturing process and identify anomalies in real-time. Factory managers can digitally visualize their shop-floor operations and monitor the drug production process in real-time.
Operation twin solutions enable production teams to digitally simulate the entire manufacturing process and influence the batch yield outcome by changing the parameters in real-time.
This solution allows manufacturing teams to eliminate the physical trial and error methods to deliver the best quality batch yield, saving resources, time, and effort. Production teams can use the information from simulations to accurately predict the machine settings and process parameters to yield the best quality batch.
Digital twin solutions allow manufacturers to prevent downtime by anticipating problems, leading to reduced costs. It can guide operators with their maintenance work using real-time data analysis to avoid long-term damage.
Ensuring that minor technical problems do not lead to larger failures over time can avoid expensive repair work.
Digital Twin allows floor managers to simulate the conditions of a production line before the actual manufacturing process begins. This enables them to analyze every aspect of the physical process and anticipate issues before they occur.
By taking measures to prevent the issues from occurring, manufacturers can dramatically improve the quality of their products.
Digital simulations allow manufacturers to combine operational and financial data with the production process. This helps them visualize the scarcity of raw material and labor shortages throughout the manufacturing process.
With advanced knowledge of demand for raw materials and manpower, factory managers can plan their downtimes to optimize the manufacturing process, saving both time and money.
Digital twin simulations enable manufacturers to conduct product testing and quality assurance virtually. This helps engineers identify product limitations that are likely to occur before the actual manufacturing process begins.
Using the data from the digital twin simulations to tweak the parameters of the factory production line, floor managers can drastically reduce the shortcomings of the goods and increase efficiency.
By simulating the manufacturing process on a digital twin application, operators can visualize at what point in the production stage a machine or one of its parts is likely to break or malfunction.
Floor managers can utilize this knowledge to schedule maintenance and improve the overall lifespan and health of their equipment, thereby increasing efficiency.
Visualizing the entire production end-to-end on a digital twin simulation allows factory managers to identify stages in the process where defects are most likely to occur. Armed with this information, operators can monitor the production line more efficiently and spot defects with high success rates.
Producing the “golden batch” or the best quality drug yield against a pre-defined set of parameters is a challenging process. It requires close coordination between the process, product, and machine settings.
The pharma digital twin application helps manufacturing teams leverage sensor and machine data to gain insights not available through conventional physical methods. These insights can help factory managers make well-informed decisions, vastly improving their production capacities.
Using Gramener’s signature low-code platform, Gramex, operators can accelerate production up to 30% faster.
One of our top clients, a global pharma manufacturer, wanted to predict the quality of their products by gaining more visibility from their plant operations. Gramener built a Chemical Synthesis Process Monitor Digital Twin Application. Which helped the operator choose the right process parameters to optimize yield.
Based on pre-determined parameters or conditions, our digital twin solution can predict the quality of the drug yield. It can also monitor operations and alert the plant operators during a sudden failure.
If the quality drops, floor managers will receive alerts on their mobiles. Operators can also track drops in quality in real-time through a large screen display on the plant floor.
Operators can simulate the production of a batch using the digital twin solution and predict its quality even before manufacture. They can also change the parameters in the simulations to improve the quality of the successive batches. We helped our client save $2 Mn by improving the Golden Batch yield.
Manually setting up machines for drug production is time-consuming. It can also lead to monthly wastage of more than 10,000 tablets.
Gramener developed a Pharma Digital Twin solution to automate the process for a global drug manufacturer. Operators can ensure correct tablet weight, thickness, and hardness using a data-driven approach. They can also set up the compressor correctly.
A pharma major wanted to increase the yield quantity of their product. Using Exploratory Data Analysis and three months of historical data, Gramener understood the various manufacturing process patterns.
We applied regression and classification models to gauge which material and operational parameters are essential. Our solution helped the client gain improved visibility on the parameters driving the yield of the product.
It also helped them achieve a Golden batch output of 117 kg, increasing from 114 kg the previous year.
Vaccinations prevent millions of deaths every year. Increasing the reliability and quality of pharmaceutical manufacturing, reducing batch waste, and accelerating time to market can save countless lives.
To overcome these challenges, one of the world’s biggest pharmaceutical brands wanted to transform its development and manufacturing process digitally. By installing in-line sensors at every step the factory floor managers could collect data and track the process in real-time.
Merging physical, biological, and chemical models with sensor data, a digital twin of the pharmaceutical process
The resulting solution, a live in-silica simulation of the physical process, helped optimize operations and offer fresh insights for development. It also enabled the manufacturer to have complete control over the production process.
The deployment of digital twins in the pharmaceutical industry, especially in pharma manufacturing, is still unsatisfactory (Yingje Chen et al., 2020).
But the U.S. Food and Drug Administration has the vision to develop a productive and flexible pharmaceutical manufacturing sector that can produce high-quality drugs without large-scale regulatory slips.
In this regard, the pharmaceutical industry is adopting the general digital trend. With the help of regulatory agencies and academic institutions, it is adopting digital twin concepts and applying them to drug research and development, supply chain management, and other manufacturing practices.
The industry is on course for developing Pharma 4.0, the integrated manufacturing control strategy and operating model in line with the International Council for Harmonization (ICH) guidelines and Industry 4.0.
Discover how Gramener is revolutionizing industries with cutting-edge Industry 4.0 technologies in our latest video!
Digital twins are gaining popularity in the pharmaceutical business. Production process digital twins, production building digital twin, product digital twin, and patient digital twin, also called the 4-P model, are the most commonly modeled digital twins. Optimizing individual objects through digital twins is possible.
Process digital twins can help in finding better solutions to process-related hurdles. For example, MIT, in the United States, has developed an advanced mathematical model of a regular process. Whereby one can estimate how efficient a mixing plant is in mixing different granules for tablet production.
An example of a patient digital twin is in cell therapy, where a patient’s modified cells are used for treatment. In this case, the information extracted from a patient is digitally collated and then channeled safely to the production equipment for analysis. For example, gene analysis is carried out to produce a personalized drug for the patient.
The virtual replicas of patients have been created and used for surgical operation training and health monitoring. The data collected from the same has also been used to study the health of a country’s population.
The Living Heart project is an example of digital twins in life sciences. In this project, the FDA, leading cardiovascular researchers, medical device manufacturers, practicing cardiologists, and educators are involved in the analysis of blood circulations and visualization of the anatomy to develop devices faster.
A global biopharmaceutical company recently optimized its processes and operations by getting insights from both its data sets – external and internal. For this, it first optimized its country footprint and site selection as these activities affect the speed, cost, and quality of trials.
The company built models that identified past performance drivers and machine learning algorithms that predicted patient recruitments and quality events. This process delivered a huge impact as time for patient enrollments dropped by 10 to 20 percent. The cost of trials fell by 10 to 15 percent, and the site selection process of the company improved by nearly five times.
The pharma industry is facing numerous challenges such as increasing costs, declining profit margins, market saturation, the need for fast-paced supply chain models, and tighter restrictions. This has prompted the pharma industry to turn to digital solutions for innovation, scalability, and for keeping in step with market demand.
Digital twin technology has transformational power and can help bring down business expenses and make products and assets more durable. While digital twin technology has been around for about two decades, digital twins in pharma manufacturing are relatively new.
As per a Gartner survey of 2019, the digital twin is a top 10 trend in technology as it has the power of disruption in IoT solutions.
Early adopters of digital twins in pharma manufacturing are using process digital twins in drug development. This is helping faster drug development and is likely to see a huge monetary upside. This is because, with every cycle, the predictive capability of pharma manufacturers increases by several notches. They have better insights on product performance which can help in altering or enhancing future product performance.
Digital twin technology provides scalable data analysis opportunities to pharma companies. Pharma data analytics, along with cloud computing and machine learning, delivers insights for the pharma companies, to develop global market strategy. Companies can understand their competitors and develop data-backed insights for internal systems through pharma data analytics.
Digital twin technology can be used for the entire supply chain. The pharma industry can use it to generate real-time data for research and development, regulatory and safety adherence, supply chain planning, network optimization, quality monitoring, commercial spend optimization, tailored customer engagement, and competitive intelligence.
Digital twins can facilitate smart manufacturing in various phases of process development and production. It can be used in the process design stage to speed up the manufacturing route and its unit operations. Further, digital twin simulations enable a better understanding of process variations. This also helps in predicting product quality, productivity, and process attributes, thereby decreasing the time and cost for physical experiments.
Smart manufacturing through digital twins requires real-time system monitoring and control, continuous data acquisition from equipment and products, and a global data analysis platform. The pharmaceutical industry has used different techniques such as Quality by Design (QbD), Flowsheet modeling, Continuous Manufacturing, and Process Analytical Technology (PAT) implementation to achieve this. However, the overall development and integration of digital twins are still in a nascent stage.
A top digital transformation consultancy and a leading engineering business have worked on a pharma digital twin project. The system they have devised creates a digital representation of a particular process step.
The actual plant is connected to IoT sensors which relay real-time information to the digital twin. With the help of advanced data analytics and artificial intelligence, this solution provides optimized process quality and reliability measures.
Atos has also developed another application of digital twin technology in drug development by using the technology for vaccine development for the SARS-COVID-2 virus. This application has shown how to reduce the time to market for a drug or vaccine with the help of data collection and analysis.
Gramener’s digital twins for pharma manufacturing have enabled the client to predict the quality of manufactured drugs and get a golden batch yield. Our Chemical Synthesis process monitor solution uses machine learning to run a simulation of the drug manufacturing process to change the input variables that can impact quality. It can also provide mobile alerts when there is a drop in quality.
Similarly, it displays the drug production process on a large screen through a floor monitor. This ensures that the operators are always up to date and there are no unaddressed quality issues.
The solution has 4 components:
These four views were built in just 3 days using 8 components of our low code platform Gramex, replacing over 2,500 lines of code.
Digital twins are important for the development of close integration of manufacturing processes and physical resources which can often be a source of worry in the pharma industry.
The faster the industry can adopt this technology across its supply chain, the greater the benefits in terms of scalability and innovation the industry is likely to see. Moreover, in a world full of uncertainties and health scares, it is important that the pharma industry be armed with tools like digital twins, machine learning, artificial intelligence, and predictive analytics.
Contact us for custom-built low-code digital twin solutions for your business challenges, and check out pharma and life sciences AI solutions built for our clients, including Fortune 500 companies. Book a free demo right now.
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