By Manas Sarkar
In recent times, digitization across industries has led to a significant impact across industries through a myriad of business, leadership, and technology transformations. These have also led to the accelerated adoption of digital twins – a virtual replica of an enterprise setup, which might be a product, an operational process, or even an entire supply chain, that simulates real-world conditions.
A digital twin uses real-time data and snapshots to provide a detailed simulation model and greater visibility of a product or process which can help to optimize and improve the same as well as uncover any susceptibilities. These digital models can simulate all the characteristics of their physical counterparts allowing stakeholders to interact with or modify a system in a virtual space which can be quicker, easier, and safer than doing so in the real world as well as to monitor performance and derive insights.
Despite being around for decades, digital twin technology is now increasingly being harnessed across industries and geographies to unlock operational excellence. Gartner estimates that the aggregate market size for digital twins will cross $183 billion in revenue by 2026.
An efficient value chain
By integrating artificial intelligence and building process twins, businesses can mirror real-world operations, analyse vast datasets in real-time, identify bottlenecks and vulnerabilities, and streamline processes and workflows. These digital twins can be created at various levels – an equipment twin, an assembly and plant level twin, or an ecosystem twin and leverage a combination of physics-based models and data-driven analytics to provide real-time insights of any physical entity, subsystem, system, process, or an ecosystem, allowing for a transparent validation basis the strategic business indicator.
Essentially, the infusion of AI helps build and execute richer models with prediction at its core. The AI-powered process twins empower an organization to enhance overall operational agility and make data-driven decisions from product development to manufacturing to end user experience to maintenance and to sustainability.
By embracing the power of AI-powered digital twins, organizations can be at the forefront of innovation, driving sustainable growth, and maximizing value creation across the entire value chain, encompassing engineering, manufacturing, operations, and maintenance.
Additionally, digital twins also enable businesses to rethink their supply chains and implement changes to make them more efficient, effective, and resilient. These technologies enable companies to transition from a steady-state supply chain to an agile one that evolves and adapts to business and market changes at a rapid pace.
State of the industry
The combination of powerful computing capabilities, accelerated growth of IoT, and ubiquitousness of 5G at significantly lower costs in recent times have propelled the growth of digital twins. The blend of AI and machine learning can further unlock operational ingenuity and enable holistic predictability.
A key factor driving the growth of digital twins is Industry 4.0 – the foundation of modern industry – that requires embracing automation, real-time data exchange, and smart manufacturing processes. AI-powered process twins leverage data digitization and analytics to improve automation, optimize operations, and build productivity in increasingly digitized industrial setups. These will play a crucial role in the journey towards Industry 4.0 by driving efficiency, innovation, and sustainability across various aspects of industrial operations.
AI brings massive opportunity for enterprises to embark on implementing process twin by combining real-time context fabric and enterprise ecosystem knowledge. This will bring considerable benefits in terms of modelling digital ecosystem, optimizing business and IT processes, enterprise threat, and opportunity identification and governance.
The adoption of digital twins in enterprises are not without some challenges. Poor data integrity or inadequate infrastructure allocation can be barriers to implementation or the effectiveness of diverse digital twin solutions. Organizations should, therefore, treat digital twins as critical systems with vital focus on availability, integrity, and confidentiality of data and resources.
Summary
Digital twins have been around for some time, but the maturity in these solutions along with recent technological innovations and advancements in computing, analytics, and connectivity have now led to broader adoption across enterprises with predictive, high-fidelity models instead of siloed or proof-of-concept implementations.
Now, with AI and generative AI, digital twins could transform into autonomous virtual systems with an ability to design, simulate, validate, and optimize products, processes, and plants. Their benefits are being widely realized by organizations across industry sectors to not just enhance organizational outcomes but also to add business value and drive competitiveness.
They can impact the entire value chain as well as help in creating next-generation products and unlock additional revenue opportunities. AI-powered digital twins enable effective monitoring, diagnostic, and prescriptive analytic capabilities to help optimize operations and effect more efficient, flexible, and sustainable processes.
(The author is Vice President and Global Head API Economy, Microservices, Cloud Native Development and Cloud First Integration, Infosys, and the views expressed in this article are his own)
