Digital Transformation of the Pharma Industry: Role of the Digital Twin
Published on : Friday 15-12-2023
Simulation using digital twin technology plays a significant role in designing and visualising the system, says Deepika S.
In the rapidly evolving field of healthcare, the pharmaceutical industry is one of the fastest-growing economic sectors. From optimising drug discovery to enhancing manufacturing processes and patient care, the technological revolution promises innovation in every aspect of pharmaceutical operations.
With the growing demand for pharmaceuticals, it is important to understand and predict consumer demand to increase manufacturing efficiency among other things. This can be achieved with Digital Transformation to enhance operational efficiency, drive innovation, provide better customer experiences, and improve the quality of products and services.
For effective business transformation, decision-making should be a combination of the company's goals and visions backed by the real data collected through various channels. One such technology that plays a crucial role in digital transformation is Digital Twin.
Digital twin refers to a virtual representation or model of a physical system, process, or product. It is a detailed and dynamic simulation that looks, behaves, and interacts just like the real thing or real system. In this article, our focus is on highlighting the advantages of building a digital twin in the field of manufacturing.
Digital twin framework
In general, the following framework is used in building digital twins in manufacturing.
1. Define goals
2. As the first step, we need to define the goals or required output from the system.
3. Inputs to the system:
a. Based on the available information, we define the process as either a Greenfield or Brownfield project. A Greenfield project refers to designing optimal processes for new solutions where historical or real-time data is not available. A brownfield refers to optimising the existing process through a digital twin.
b. Inputs to the Greenfield Project
i. Assumptions and programmatically generated data. For example, feed rate assumed, MTBF & MTTR, maximum resource utilisation, etc.
c. Inputs to the Brownfield Project
i. Existing historical data: It is the data that is collected over time from the real system and saved in the form of Excel etc.
ii. Real-time data is gathered from the physical system. For example, factory input data, sensor data, and output data will help in defining the existing plant realistically. This data can be directly fed to the digital twin.
a. Step 1: Concept development
b. Step 2: Virtual modeling of machines involves defining the exact dimensions, and working parameters with process times and behaviors to get the exact analytics while replicating the plant.
c. Step 3: Defining products, process flow where the product undergoes a change and flow which shows the movement of the material between the processes using different resources.
5. Simulation
a. Apply the inputs defined in Step 2 as per the project type.
6. Analytics
a. Generate the required outputs for the stakeholders involved in the decision-making.
b. These can be videos, charts and graphs, and CAD data.
7. Decision Making and Iteration
a. Based on the data collected, the decision-making team will decide further actions.
b. These actions can either be to iterate further or go for the deployment.
Building a successful digital twin
In this article, we take an example of building a small packaging plant, in Visual Components simulation software, that can fill 2 types of pills into 120000 bottles approximately that are further packed into cartons to produce 175 batches of pallets approximately in a shift of 8 hours.
To accommodate all equipment in limited space, we built the system on two floors. The ground and the first floor contain primary bottle filling and packaging into the cartons. Palletising and storage are defined on the ground floor. The area for truck loading is also defined in the given space.
The required machines such as bottle-filling units, capping units, and box-sealing stations are added and cycle times are set. After defining all parameters, simulation flow is added to show the SKUs’ movements between different stations. Through iterations, the process is simulated, and KPIs are plotted and analysed, to check if the requirements are met.
After getting the required output, the simulation is exported to various formats such as spreadsheets, videos, and 2D/3D CAD.
This data can be used by various stakeholders to collaboratively decide upon the final solution.
Further Advantages of Digital Twin
Apart from building efficient systems, Digital Twin has further advantages.
Training and Education
Using VR technology, digital twins can be used as a training ground for new employees where they can get a basic understanding of the systems and lines before entering the actual shop floor.
Advantage compared to traditional SCADA
Although traditional SCADA fulfils the need for the monitoring of the system in real time, it is limited only to representation and reactive maintenance. With the help of a 3D digital twin, one can simulate and optimise the existing system as a part of continuous improvement.
Conclusion
To conclude, the simulation of systems using digital twin technology plays a significant role in designing and visualising the system and helps make it efficient by analysing the performance parameters and applying the improvements.
Visual Components Simulation Software offers cutting-edge tools to build such systems and collaborate for effective decision-making.
Steps we follow in Visual Components:
1. Digital Twin Development using a predefined library of 3000+ models. These intelligent models contain the parameters for simulating and interfaces for connecting with other models within the simulation and in the real world. Having a predefined and continually updating library helps users focus on the process rather than modeling.
2. Visual Components have an easy-to-use interface to define the processes. Processes are defined as process nodes (stations), flows (the path), and the resources that execute the operations.
3. Inputs can be defined within the simulation or imported historical data as Excel sheets, or connected to the real-time system for monitoring and control using OPC UA.
4. Outputs are generated as per the requirements of the stakeholders. These can be 2D/3D CAD data, Excel sheets with data such as utilisation graphs, throughput and, cycle time charts, HD videos, and 3D PDFs.
Deepika S is working as a Simulation Engineer, at APEXIZ. She has 3+ years of experience in 3D plant simulation. During her career, Deepika worked on various projects involving complete plant design. She has also trained many companies in starting their journey into the simulation. Connect with her through LinkedIn at: www.linkedin.com/in/deepikasuggu
APEXIZ is an engineering company offering products and services in 3D plant simulation and offline programming. APEXIZ is the partner of Visual Components, a Finland-based company, and is the authorised distributor of their products. APEXIZ has customers in China, South Africa, Israel, Singapore, and many European countries.
