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Are we far from this? ……not with the progress being made in Digital Twin Tech.
As industries aim to achieve efficiency in their processes, innovation, and a competitive edge, Digital Twins have emerged as a game-changer. They are transforming the world of manufacturing, providing real-time data that has eliminated redundancies, improved decision-making, and optimised supply chain processes. As a technophile overseeing the Digital Transformation of industry verticals at Cisco, I’ve had the chance to explore digital twins, how they are different from simulations, and the benefits they offer to industries, and these are my key insights. Before I get to that, let's get the definition right and differentiate it from simulators and modelling.
Defining a Digital Twin
A digital twin is a digital replica of a physical product, system, or process that serves as an indistinguishable virtual counterpart for practical purposes. Additionally, digital twins can be virtual replicas of physical devices such as assets, systems, or processes.
This description might sound like a simulation. Simulations and digital twins both utilise digital models to replicate a process. While simulations are model-based representations of technical processes and unit operations in software, a digital twin is a completely virtual environment that generates multitudes of data for engineering teams to analyse. The difference in scale between digital twins and simulations, along with the use of cutting-edge technologies such as AI and ML, provide a goldmine of data and information. This data is often used to fine-tune end products and optimise supply chain processes. Digital twins can run multiple simulations simultaneously and transmit data in real time.
Let’s now get to the insights:- Providing Real-world Real-time Scenarios in Manufacturing
While digital twins are being widely used in everything from healthcare to urban planning to power generation to real estate, they have found the fastest adoption rates in the manufacturing industry.They can be built for assets, production lines, raw materials, and pretty much the entire operational chain, to recreate real-world real-time scenarios. The inherent capability of a digital twin to automate data pulling, cleansing, structuring, and transforming, helps put valuable data into the hands of engineers. This eliminates the need for manual data manipulation and frees resources for innovation and market readiness.
Digital twins can be leveraged by any spoke of the manufacturing wheel, the early adopter industries are automotive, agriculture, aviation, consumer goods, healthcare and medical devices, and smart cities. Across these industries, digital twins are truly able to represent the product and production systems, leading to reduced time and cost associated with assembling, installing, and validating factory production systems.
A Vehicle for Massive ROI
Digital twins are sure-shot vehicles for organisational savings. A recent survey by market advisory firm ABI Research affirmed that cost benefits from the twins are upwards of $280 billion by 2030 in the implementation of smart cities, driven primarily by the design, plan, and implementation of connected infrastructure and assets. However, within manufacturing, car manufacturing businesses are the frontrunners in leveraging digital twins to reduce redundancies in design, leading up to cost savings to the tune of 41–54% globally. Moreover, industries that use metals for production are already saving half of their costs compared to legacy simulation methods. In short, every industry is either realising better profits or is en route to long-term cost savings by leveraging digital twins.
Three Steps to Initializing Digital Twins
A typical question that arises in the minds of CXOs and decision-makers of manufacturing businesses is how to kickstart a Digital Twin journey that is streamlined to their unique business needs. While each business needs to first identify the most critical aspect in the manufacturing life cycle that is most repetitive, time-consuming, and resource-reliant, a lot of businesses choose the McKinsey model.
This model looks at digital twins as a virtual representation of a physical asset, person or process, in order to achieve agile, flexible and resilient operations. McKinsey has essentially created a generic model for enterprises of all sizes, across business domains, and today it acts as the basic framework for any business that believes every process related to business can be replicated and connected virtually. McKinsey looks at digital twins as an integral part of the larger “enterprise metaverse”. In simpler words, the model helps businesses envision digital twins as a vehicle to maximize value and reusability while managing ownership and governance structures.
The model simplifies the process into three generic steps: creating a blueprint, building the base digital twin, and boosting capabilities. While the first step involves ideating the governance, ownership, and operational aspects of the twin, the second step mostly revolves around a platform that leverages future-ready technologies to manage both structured and unstructured data. Finally, one can add a data analytics layer that can help unlock insights that are relevant to the future of business.
Constructing a Digital Twin
This is getting a bit lengthy…but bear with me.
Beginning with defining the scope, you must first determine the physical system or object that you want to replicate and the level of detail that you need to include in the digital twin. Then, gather data from sensors, control systems, and other sources to create a digital representation of the physical system. This may involve using techniques such as computer-aided design (CAD), 3D scanning, or machine learning algorithms to capture and process the data.
Software tools such as simulation programs or specialised platforms for digital twins are then used to develop a virtual model of the physical system using the data collected. This digital twin then needs to be validated to verify that it accurately represents the physical system by comparing the behaviour of the virtual model to the actual system under different operating conditions.
This new digital twin can then be connected to other systems, such as control systems or data analytics platforms, to enable real-time monitoring and analysis. It is vital that the digital twin is continuously improved and updated as new or live data becomes available or the physical system changes to ensure it accurately represents the current state of the system.
Overall, building a digital twin requires a multidisciplinary approach that combines expertise in engineering, data analytics, software development, and system integration.
Building Infrastructure to Support Digital Twins
Digital twins have revolutionised the manufacturing industry, enabling businesses to leverage cutting-edge technologies and drive digital transformation. At the core of all simulation activity lies the IT network that powers it. By integrating the technological ethos of digital twins into the network, businesses can automate more processes and transform their operations.
But as digital twins evolve, they become increasingly essential in automating a growing number of processes. As such, they can bring about digital transformation to the core network infrastructure of the business, enabling businesses to leverage cutting-edge technologies to drive into the future. In order to achieve digital transformation through digital twins, two backend aspects of IT need to be watertight – Security and BI.
Strengthening Security and BI: Key to Digital Transformation
In a digital twin environment, huge amounts of data travel bilaterally between simulators and engineering teams. It is essential to ensure that the right people have access to the right data to prevent vulnerabilities that could prove catastrophic for businesses. Security in a digital twin environment is closely linked to applications and processes that drive the business intelligence aspects of the supply chain, making it vital to strengthen them.
Besides security, the digital twin technology stack primarily includes a data management and analytics platform, self-learning AI, robotics, and AR/VR, depending on the business need. BI flows across the enterprise to ensure maximum and relevant output, cost, and workforce optimization across the board.
Unlocking Data-Driven Insights for the Future of Manufacturing
Digital twins are the future of manufacturing. They amalgamate a wide range of future-tech technologies while unlocking data-driven insights. With the right security defence systems in place, digital twins can truly take businesses into the future. Digital twins enable businesses to leverage cutting-edge technologies and drive digital transformation. By integrating digital twins into the IT network, businesses can automate more processes and transform their operations to be the perfect fit for an actively evolving business future.
About the author:
Vinod Karumampoyil
Vinod Karumampoyil leads the Digital Transformation Office (DTO) for Cisco India & SAARC. He's also the Program Execution Leader for Cisco's flagship Country Digital Acceleration (CDA) program. Vinod's charter includes but is not limited to, accelerating the national digitization agenda by building long-term relationships with leaders at the national level as well as in the industry and academia, strengthening mindshare, and increasing our footprint in the market.
Vinod and his team have been pivotal in providing solutions and driving our success across several verticals, especially Smart Cities, Agriculture, Transportation, e-Gov, and Utilities.
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