Digital twins have transitioned from being a product engineering tool to a solution to evaluate business factors such as operations and supply chain. In Medical Product Outsourcing’s feature on digital twins in medtech, Identiv’s Deepak Prakash points to that shift as the foundation of a “digital twin value chain” taking shape across regulated healthcare environments.
We are syndicating this article from MPO.
Digital twins (DTs)—virtual duplicates of objects, products, processes, and even entire corporate ecosystems that physically exist and evolve using data from their operational surroundings—continue to transform manufacturing through operational optimization, predictive maintenance, and reduced risk. DTs span an object’s lifecycle, are updated with real-time data, and rely on simulation, machine learning (ML), and reasoning to help make decisions.
A common misconception is that the goal of digital twins is to recreate reality in every detail. “In practice, however, the most effective digital twins are designed with a much more focused objective: to capture the parts of a system that matter for the decision an organization is trying to make,” said David Andersen, regional director for Oakbrook, Ill.-based The AnyLogic Company, a developer of simulation modeling software for business applications. “A model designed to improve manufacturing performance may look very different from one built to evaluate supply chain resilience, production capacity, or product launch strategy.”
At their core, digital twins are not simply digital replicas. They are purpose-built decision-support environments that help organizations better understand complex systems before making changes in the real world. Their real value is in creating a controlled environment where organizations can test ideas, evaluate tradeoffs, and understand the consequences. “Organizations are increasingly building digital twins that evolve alongside the products, manufacturing systems, and business questions they are intended to support,” said Andersen.
For example, in product development, DTs help engineers evaluate design alternatives, predict performance, identify quality issues, and improve manufacturability. In manufacturing, digital twins can optimize production scheduling, capacity planning, equipment utilization, and operational performance while reducing scrap, downtime, and time to market.
“Increasingly, leading manufacturers are integrating digital twins into sales inventory operations planning [SIOP], allowing leadership teams to evaluate demand, capacity, inventory, supplier constraints, and financial tradeoffs before making critical business decisions,” said Lisa Anderson, president of LMA Consulting Group, a Claremont, Calif.-based firm that specializes in manufacturing strategy and end-to-end supply chain solutions. “When combined with artificial intelligence, Internet of Things, and advanced planning systems, DTs are evolving from engineering tools into strategic business capabilities that improve predictability, resiliency, customer service, and overall operational performance.”
Digital Twin Trends
“Digital twins are transforming medical device engineering by shifting development from a reactive build–test–fix model to a more proactive predict–optimize–validate approach,” said Mohan Ramadoss, director of global innovation and development for Phillips Medisize, a Molex company in Hudson, Wis., that provides design, engineering, and manufacturing services to the medical device, pharmaceutical, and in-vitro diagnostics industries. “Rather than a standalone simulation, DTs act as a system-level predictive framework, integrating multi-physics models, system behavior, physical prototyping, and real-world data to evaluate performance under realistic conditions.”
In the early design phase, for example, a digital twin can enable rapid design-space exploration and sensitivity analysis, linking outputs from digital and physical prototypes—such as dose delivery, force, pressure, and flow—to key design variables. This can support structured design of experiments (DOE), allowing engineers to understand patterns, interactions, and dominant influencing variables—driving scientifically guided design decisions rather than trial-and-error approaches, particularly when adapting device platforms for different drug molecules.
“Increasingly, digital twins are extending into the manufacturing sector, linking design intent with process variation—such as molding, assembly tolerances, and material behavior—to predict final device performance,” said Ramadoss. “This can support tolerance allocation, manufacturability assessment, and early failure-risk identification, ensuring designs are robust before major tooling investments.”
The biggest shift that Anderson has seen in medical device manufacturing is that DTs are no longer viewed primarily as engineering or simulation tools. Instead, they are becoming enterprise decision-support platforms that connect engineering, manufacturing, supply chain, and business planning. “They are moving from product twins to enterprise twins,” said Anderson. “Companies are using them to create digital representations of production lines, factories, and even end-to-end supply chains to optimize performance across the business.”
Digital twins, when integrated with artificial intelligence (AI), can identify constraints, quickly evaluate thousands of scenarios, predict outcomes, and recommend actions. “AI and digital twins are often discussed together, but they solve different problems,” said Andersen. “AI is exceptionally good at identifying patterns, forecasting demand, and detecting anomalies. Simulation helps organizations understand how decisions will affect complex systems before those decisions are implemented.”
However, to get the best results, DTs must be applied with discipline. “They are bounded predictive models, not exact replicas of reality,” said Ramadoss.
Their value lies in enabling credibility-driven modeling, where a clearly defined context of use, validation evidence, and quantified uncertainty guide decision-making. In this framework, he noted, digital twins do not replace testing—”they complement physical prototyping by making testing smarter, more focused, and more efficient.”
Humans in the Loop
Digital twins in medical device development are rapidly evolving from open-loop, component-level simulation models into closed-loop, system-level predictive frameworks. Traditionally, digital twins were used to predict outputs—such as dose delivery, pressure, or flow—based on controlled design inputs such as geometry, material properties, and actuation profiles. “However, this approach assumes a fixed environment and does not fully capture real-world variability,” said Vasanthan Mani, senior manager of product development for Phillips Medisize.
A key advancement is the emergence of human-in-the-loop digital twins, particularly for complex drug delivery systems such as on-body injectors and high-viscosity injectors. In these systems, dose delivery performance is no longer governed solely by pump mechanics or fluid dynamics, but by nonlinear interactions between device, fluid rheology, and tissue mechanics. Engineers are now incorporating simplified anatomical models that include tissue compliance, back pressure, and fluid–structure interaction into the simulation loop. “This can enable closed-loop prediction, where device response dynamically adapts to physiological boundary conditions, providing a more realistic estimate of injection time, flow variability, and end-of-dose accuracy,” said Mani.