SANTA CLARA, June 7, 2023 — Vatsal Shah, CEO & Co-Founder – Manufacturers face the challenge of managing all sorts of machinery and assets to keep their operations running at the highest capacity possible. With modern Industry 4.0-enabled facilities, one of the biggest challenges in achieving this goal for manufacturing operators is ensuring that all of their machinery is functioning at its peak potential.
As new innovations have been made in manufacturing technology, data-driven Digital Twins have become one of the premier solutions to visualizing and optimizing manufacturers’ digital ecosystems. Today’s development teams leverage AI-enabled systems, operational technology (OT) data collected from machines, or assembly line or supply chain simulations to get an overview of performance from the organization as a whole. With these types of digital twins, manufacturers have the ability to test, scale and improve processes and save them time, resources and costs.
In this piece, we will explore what digital twins are, how they augment manufacturing processes to better execute tasks and improve system functionality, and their current state of use within Industry 4.0 and beyond.
In a nutshell, a digital twin is a visual representation of an asset. In other words, it is a digital model of a process, system, machine or object. Sometimes called “what-if” scenarios, digital twins give operators a way to conduct hypothetical experiments of their systems, or test out various operations to ultimately simplify the creation and maintenance of purpose-driven data models.
Manufacturers, businesses, and healthcare entities are leveraging digital twins today to experiment with the many options associated with their product development process. When used properly, digital twins can help streamline the way these operations collect, contextualize, and analyze data with visual representations of their systems. Among the many benefits of experimenting with digital twins are:
Improved product design and development
Ability to identify bottlenecks within systems
Better predictive maintenance and reduced downtime
Increased efficiency in production and supply chain management
Enhanced quality control and product testing
Greater visibility and transparency of system KPIs across the entire process
Businesses and manufacturers are at various levels of maturity for deployment of digital twins depending on the infrastructure and use-cases. But when a digital twin model is fully matured, it can be used to monitor assets and predict maintenance issues in real-time. For example in manufacturing, a digital twin is a visual representation of anything considered to be an industrial asset, which could be anything from a programmable logic controller (PLC), to an industrial machine, to an assembly line, or even a complete site.
Here is an example of how a digital twin model looks in action. Say a manufacturing facility wants to use digital twins to optimize the way their assembly line functions. The same assembly line could have three different data models – one for energy monitoring, another for predictive maintenance, and yet another for production optimization. Each of these models use both static and dynamic data from the assembly line, has contextual information from Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES) relevant to the specific application, and produces outputs that measure efficiency, alerts or other KPIs. These outputs are valuable in their own right to speak to the overall functionality of the system, but they can also serve as an input to a larger data model. For example, a smaller assembly line digital twin could be an input to the digital twin of the entire manufacturing facility.
As a data model, digital twins can serve endless use cases, one of them being product development. A digital twin can serve as a functional prototype that models a new product’s strengths and weaknesses without the time or monetary investment of creating the product from the ground up. By using machine learning and artificial intelligence, the digital twin model can be enhanced to replicate real world scenarios.
Product development and engineering teams can observe product and user behaviors in a digital twin to guide their product development efforts and at times, their overall product strategy. For product development teams, this what-if simulation identifies breaks or challenges with the product prior to production and saves cycles in the overall development effort.
For manufacturers, the benefits of leveraging digital twins far outweigh the costs of implementing them. Not only do digital twins provide better visibility and understanding of large systems of complex machinery, but they also improve the way products are designed and iterated. Moreover, digital twins give manufacturing operators insights into when their systems will require maintenance and downtime, so they can plan for these cycles as opposed to shutting down due to an unforeseen issue. Ultimately this results in increased efficiency in production, better overall supply chain management, and growth in net gains.
The future of Industry 4.0 will depend on how manufacturers leverage the solutions available to them, and digital twins are among the most powerful and practical of those tools.
About Litmus Litmus is an Industrial Data Ops company that enables Industrial companies to unlock and activate their operations data at scale. Rapid-to-deploy, easy-to-use and built-to scale, Litmus is a fast way to connect to all operational technology (OT) assets and put data to work at the edge and across the enterprise. Litmus technology is trusted by Google Cloud, Parker Hannifin, Dell Technologies, HPE Mitsubishi, and other global Fortune 500 companies. For more information visit https://litmus.io.