Why Leaders Should Stop Comparing Digital Twins and Simulations

Why Forward-Looking Leaders Use Both to Power Real-Time, Spatially-Aware Operations

For years, digital twins have been misunderstood—often seen as just advanced simulations. This false equivalence leads to poor tech bets and missed opportunities. While both simulations and digital twins model real-world systems, they serve fundamentally different purposes. One plans, and the other performs.

Leaders today must stop asking which one to choose. The real advantage lies in using both—together—to drive faster, smarter decisions across the entire operational lifecycle.

Simulations: Planning in the Digital Realm

Simulation has long been essential to engineering and design teams. From finite element analysis to computational fluid dynamics, these tools reduce risk and costs by testing scenarios before committing resources.

But simulations are inherently limited. They use static inputs. They generate one-off forecasts. And they operate independently of real-world feedback. A simulation tells you what might happen—but only under preset assumptions. When the world changes, the model doesn’t.

Digital Twins: Operating in Real Time

A true digital twin is a living, evolving mirror of a physical system. It ingests real-time data from sensors, spatial mapping, and business systems—continuously updating to reflect current conditions. It enables physical-to-digital and digital-to-physical feedback, creating a persistent, bidirectional loop.

This is where many implementations fall short. The term “digital twin” has been watered down, often applied to static dashboards or disconnected 3D models. But without real-time data, persistent state, and spatial anchoring, it’s not a digital twin—it’s just a visualization.

The Phygital Shift: Why Spatial Computing Is the Missing Link

Simulations live in the digital world. Real operations happen in the physical world. Spatial computing is the bridge.

By anchoring digital data to precise physical coordinates, spatial computing enables digital twins to understand where assets are, how they’re behaving, and what needs to happen next. Whether it's a forklift repositioning inventory or a city tracking urban infrastructure, the digital twin adjusts in real time—no manual updates required.

From “Or” to “And”

Many leaders frame the narrative as adopting simulations vs digital twins. The bidirectional nature of digital twins (physical → digital → physical) is opposite to the unidirectional approach of simulations (digital → prediction). Where simulations create isolated forecasts based on predetermined parameters, digital twins establish persistent connections that enable continuous data exchange between physical assets and their digital counterparts. This means that changes in the physical world automatically update the digital representation, and actions in the digital environment can trigger responses in physical systems.

Rather than viewing digital twins as replacements for simulation technology, forward-thinking leaders recognize that they incorporate simulations as components within a broader spatial computing framework. A digital twin may run multiple simulations concurrently, each addressing different operational aspects while maintaining alignment with the current state of physical systems.

The distinction becomes particularly clear when comparing scenario testing (simulation) versus continuous optimization (digital twins). A warehouse simulation might predict how a proposed layout change would affect pick efficiency under various demand scenarios. In contrast, a warehouse digital twin continuously monitors actual operations, identifies emerging inefficiencies, and recommends real-time adjustments to workflows, inventory positions, and resource allocations.

When a forklift operator repositions inventory, traditional systems require manual updates to reflect these changes. In a digital twin implementation, the movement is automatically detected through spatial awareness technology, the digital representation updates in real time, and downstream systems reroute picking assignments or update fulfillment timelines based on the new configuration.

What Most Digital Twin Strategies Miss

Many so-called digital twin deployments fall short of delivering real business value. Why? Because they miss five core components:

Real-Time Data Integration

Without constant data flow from physical systems, digital twins become static replicas—no better than scheduled reports.

Persistent State Modeling

A digital twin retains memory. It builds a timeline of behavior, enabling predictive insight, anomaly detection, and pattern recognition.

Multi-System Interactions

Real-world assets don’t operate in silos. Effective twins capture the interconnected nature of complex systems—whether in factories, cities, or natural ecosystems.

Bidirectional Feedback Loops

Twins should not only observe but act. They must trigger adjustments, reallocations, and interventions automatically or semi-autonomously.

Spatial Anchoring

Location context is critical. Knowing where something is happening drives far better decisions than just knowing what is happening.

Five Questions Every Leader Should Ask

To shift the conversation from features to outcomes, executives evaluating simulation and digital twin investments should ask:

1. Does this integrate with our existing data infrastructure? Choose solutions that connect seamlessly with your existing OT (Operational Technology) and IT (Information Technology) systems—no data silos, no duplication.

2. Can we establish feedback loops between the digital and physical world? Look for real-time, bidirectional updates—where digital insights trigger action and physical changes are instantly reflected.

3. Will this improve our real-time decision-making? Prioritize systems that shrink the gap between data, insight, and action—so teams can respond to changing conditions as they happen, not after the fact.

4. How does this enhance team collaboration? Look for solutions that create shared spatial context—giving distributed teams a common operational picture to align faster, make decisions quicker, and act with confidence. See how Sphere and Niantic Spatial are enabling real-time collaboration across enterprise teams in this case study.

5. Is this solution scalable across the enterprise? Don’t invest in narrowly focused tools (“point solutions”) that solve only one specific problem or use case. Instead, look for platforms that can scale across departments, workflows, and business units—and evolve with your organization's needs over time.

The Bottom Line

Framing digital twins and simulations as either/or limits your organization’s potential. Leading companies don’t choose between them—they integrate both as part of a spatial computing strategy that connects the digital and physical worlds in real time.

To unlock the next wave of operational intelligence, it’s time to stop comparing and start combining.