(Source: EmmaStock/stock.adobe.com; generated with AI)
In smart manufacturing, the digital twin is a prediction and insight engine for the factory floor. But how accurate does a digital twin need to be to deliver value? The answer isn’t straightforward. Accuracy depends not only on how closely the twin mirrors the real-world system, but also on who is using it, for what purpose, and under what constraints.
This is where most discussions about digital twins fall short. Engineers know that a twin mirroring measured torque and pressure signals while estimating internal states or degradation trends using data-driven models will require a very different fidelity profile than one forecasting long-term asset utilization. Yet conversations about “increasing accuracy” can tend to overlook this context. In practice, accuracy is a relative metric, and not all use cases demand perfection.
This blog discusses what makes a digital twin suited for practical use in industrial settings, arguing that accuracy must be determined by context, user needs, and return on investment (ROI)—not perfection. By reframing accuracy in this way, manufacturers can balance fidelity, cost, and purpose to deploy digital twins that deliver measurable value and improve over time.
The first step in evaluating digital twin fidelity is defining the consumer of its outputs. Are the predictions intended for a human operator, or are they feeding supervisory control layers that inform automated decisions?
For human-in-the-loop systems, data is often visualized in dashboards, reports, or alerts. Latency tolerance is higher, and approximations can be smoothed or contextualized. But when a machine consumes the data directly (e.g., a control system adjusting a robot arm’s trajectory based on feedback from a twin), the expectations for real-time accuracy rise sharply. Minor delays, rounding errors, or missing variables can cause cascading system-level faults.
Even within a smart factory, digital twins may serve radically different roles. A discrete-event simulation modeling material flow through a packaging line doesn’t need millisecond accuracy. A high-fidelity twin of a pick-and-place robot, on the other hand, might require precise motor temperature, torque, and position feedback at high sampling rates to allow the control system to maintain precision and uptime.
Without clearly defining who needs the data and how they will use it, discussions around digital twin accuracy remain vague and difficult to operationalize.
Building a more accurate digital twin is always possible, but it’s rarely free. Increasing fidelity usually demands more sensors, higher-speed data acquisition, more frequent calibration, tighter synchronization, and more computationally intensive models. In some cases, the final few percentage points of accuracy might require disproportionately higher investment.
Seeking higher fidelity creates a cost-benefit tradeoff for manufacturers to consider. Many manufacturers only deploy digital twins once they’re accurate enough to provide actionable insights that justify their usage. Instead of striving for perfection upfront, they focus on refining the twin over time by layering in new data and using machine learning (ML) to improve predictive quality.
This iterative approach also reduces time-to-value. Deploying a 95 percent twin today means collecting real-world feedback sooner. Waiting months to push accuracy from 95 percent to 98 percent may delay production improvements without significantly increasing ROI. In most practical cases, the benefits of continuous learning outweigh the theoretical appeal of near-perfect replication.
To manage accuracy effectively, manufacturers need to understand where errors enter the system. Three primary sources dominate.
Model design forms the foundation of twin accuracy. If the initial model omits key variables, reuses a configuration from a different site, or fails to capture unique plant behaviors, it will never match reality. Digital twins must be optimized for the physical and operational context of each deployment.
Sensor coverage and quality create the data backbone. Missing or inaccurate data are a major contributor to poor fidelity. Insufficient sampling, uncalibrated sensors, or misaligned sensor placement can inject noise and blind spots into the twin.
Latency and synchronization determine real-time relevance. Even accurate data might become useless if they arrive late. For time-sensitive applications, the speed of the data pipeline can determine whether a prediction adds value or misses the moment entirely.
Mitigating these factors requires system-level planning. A robust twin depends as much on the physical architecture of the factory—including sensors, Programmable Logic Controllers (PLCs), computational infrastructure, and communication infrastructure—as it does on the sophistication of the model itself.
Two modeling approaches dominate digital twin construction: discrete-event simulation (DES) and high-fidelity physics-based modeling.
DES models operate at a macro scale. They simulate how parts move through a process, when resources become available, and how system-level delays emerge. This approach is widely used for scheduling, throughput analysis, and layout optimization. Such models are less concerned with physics and more focused on flow and timing.
High-fidelity models, by contrast, zoom in on specific equipment or processes. These may simulate the internal temperature of a welding head, the angular velocity of a spindle, or the pressure inside a hydraulic cylinder. These twins rely on granular sensor data and are usually tied directly to control loops.
Many modern digital twin platforms support both types of modeling. The trick is to match the technique to the use case. Simulating a supply chain bottleneck with a high-fidelity robot model wastes compute. At the same time, using a DES model to diagnose bearing degradation on a lathe is equally ill-suited.
Even the best-designed twins will eventually encounter events they weren’t built to predict. In a manufacturing context, these could include unexpected equipment failures, sudden environmental changes, or unpredictable interruptions like supply chain disruptions.
Manufacturers can prepare for these events over time by designing twins to learn. When failures do occur, the control system should replay historical data, comparing what the twin predicted against what actually happened. This forensic loop allows teams to identify gaps, retrain models, and improve future accuracy.
Moreover, shared learning across assets is becoming more common. If one wind turbine in a fleet fails due to a rare vibration signature, operators can inject that failure signature into every other twin to prevent repeat failures. Some OEMs now offer shared digital twin frameworks where field data is exchanged bidirectionally between customers and vendors to enrich fault prediction models.
Validating accuracy is an ongoing process that requires multiple complementary approaches. Historical data replay allows teams to test prediction accuracy with known outcomes, creating a baseline understanding of model performance. Statistical error analysis helps quantify false positives and negatives, revealing patterns in where the twin succeeds or fails. Root cause mapping proves equally valuable, tracking model deviations back to their source to identify whether issues stem from sensor placement, model assumptions, or data processing gaps.
Similarly, ROI evaluation becomes meaningful when assessed across interconnected dimensions. Financial returns typically emerge through reduced waste, energy savings, and improved yield—direct bottom-line impacts that justify continued investment. Operational improvements manifest with better overall equipment effectiveness, higher throughput, and shorter changeovers, creating compound benefits across production lines. Strategic advantages encompass enhanced safety, sustainability metrics, and operator satisfaction, delivering longer-term competitive positioning that extends beyond immediate manufacturing metrics.
The most accurate digital twin is not the one with the most inputs or the lowest error margin. It's the one that delivers measurable improvements aligned with the factory's business goals.
As manufacturers move toward smarter, more connected operations, they will look less at building the perfect digital twin and more at building one that is fit for purpose. A “good enough” twin delivers meaningful insights, adapts with new data, and evolves alongside the plant itself. By aligning reliability with user needs, understanding the true sources of error, and focusing on iterative improvement rather than theoretical precision, manufacturers can unlock ROI without unnecessary complexity. In the era of digital transformation, the most valuable digital twins will be those that scale, learn, and drive measurable outcomes.
Hector Barresi is an award-winning Industrial Technology Advisor, Consultant, and Public Speaker specialized in Industrial Automation, Smart Manufacturing, and Digitalization. He has held executive positions at Honeywell, Danaher, IDEX and General Electric, and he is renowned for shaping top-tier Product Innovation organizations globally. Notably, he pioneered the Honeywell XYR5000, the first industrial wireless sensor family on the market, and the groundbreaking Tintelligence smart tinting platform, revolutionizing the paint industry.
