Digital Twin Software: What it is and Why it Matters to Retailers

Digital twin software allows organizations to create a digital, virtual model of a workflow, process, physical asset, or other business component. This replica allows organizations to build, predict and manage operations in a digital environment so that challenges and opportunities can be identified in real-life applications.

If a digital twin is a digital replication of a real-life business system, then digital twin software is the platform used to build, manage and extract insights from those models. In other words, it’s the technological foundation of the digital twin ecosystem.

At the core of digital twin software are four integrated components:

• The physical asset, which generates real-time data
• Live data streams from sensors, machines and systems
• A simulation and modeling engine to process that data
• An analytics layer that provides actionable feedback

These elements, when combined, create a continuous feedback loop, so that insights from the digital ecosystem can be leveraged for real-world decision-making. From there, those physical changes can trigger a whole new set of data responses, setting the feedback loop back into motion.

It’s an adaptive cycle that powers smarter operations, faster innovation and predictive decision-making.

Though digital twin software platforms vary in scope, each has a core set of capabilities and functions that make them valuable to complex organizations. Here’s a breakdown of some of the most common features to look for.

Real-time monitoring and data aggregation

Effective digital twin software collects data, to be sure, but it also orchestrates it. The platform integrates streams of information from IoT sensors, programmable logic controllers (PLCs), building information modeling (BIM) systems and even product lifecycle management (PLM) platforms into a unified view of asset performance.

This capability transforms raw sensor readings into contextual insights. When a temperature sensor detects a 2-degree variance in a manufacturing process, for example, the digital twin software correlates this with historical patterns, current production schedules and environmental conditions to determine whether it’s a normal fluctuation or an early warning sign.

Simulation and scenario modeling

Modern digital twin platforms go beyond visualization by integrating simulation capabilities powered by physics-based modeling, machine learning, or hybrid approaches. These tools allow teams to test “what-if” scenarios, but without interrupting operations or incurring the costs of physical trials.

In manufacturing, simulation enables teams to fine-tune production schedules, explore process adjustments and assess the downstream impact of proposed changes before they go live. For product development teams, digital twins can simulate thousands of real-world usage conditions to uncover potential failure points early in the design cycle.

In turn, this can significantly reduce reliance on costly physical prototypes and accelerate a product’s time to market.

Predictive maintenance

Of all the capabilities of digital twin technology, this is where predictive software demonstrates clear ROI. By analyzing patterns in vibration, temperature, pressure and other operational data, AI-driven insights can predict equipment failures weeks or months before they occur. This transforms maintenance from a reactive cost center into a strategic advantage.

The sophistication here goes beyond simple threshold alerts. Modern platforms recognize complex failure signatures, typically as combinations of factors that indicate impending issues even when individual metrics remain within normal ranges.

Lifecycle management

Digital twin software supports every stage of a product’s lifecycle, starting with initial design and extending through operation, maintenance and eventual decommissioning. By creating a continuous feedback loop between the physical and digital worlds, teams can capture real-world performance data and feed those insights directly into future design cycles.

This can be especially valuable for complex, long-life assets where field data helps drive iterative improvements and smarter next-generation products over time. In tech-heavy products, this ability is a true competitive advantage.

Remote access and control

Cloud-enabled digital twin platforms offer secure, real-time access to physical assets from anywhere in the world. Teams can remotely monitor system performance, detect anomalies and in some cases, adjust parameters or trigger interventions without being on-site.

This capability proved essential during the COVID-19 pandemic, when organizations relied on digital twins to maintain operational continuity with limited in-person staffing, or, without sacrificing oversight or responsiveness.

Because of the complexity of digital twin technology, there’s no simple checklist to mark off simple features. Instead, organizations will want to evaluate digital twin platforms based on how they can integrate into their current systems and infrastructure. Here’s a high-level look at what to consider.

Integration potential

Ensure the platform integrates seamlessly with current systems, including PLM, ERP and CAD. Look for solutions that support open APIs and real-time data synchronization. Platforms that require replacing core systems often introduce more complexity than value.

Industry alignment

Organizations can select software built for an industry’s unique demands. Manufacturing platforms may include production planning and quality control tools, while architecture and engineering-focused systems emphasize building performance and structural analysis. General-purpose platforms can fall short in delivering specialized capabilities.

Simulation capabilities

Not all simulations are equal. Some platforms rely on physics-based models for engineering accuracy; others use machine learning to analyze large data sets. Hybrid approaches offer flexibility but may require more advanced implementation. Ask whether the software can realistically simulate common operational use cases.

Scalability and ease of use

Adoption hinges on usability. If the platform demands extensive training or complex workflows, teams may struggle to adopt it fully. Look for intuitive tools that can scale in complexity as users gain confidence.

Data security

Digital twins often contain sensitive operational data. Enterprise-grade platforms should include role-based access controls, encryption and audit trails that meet an industry’s compliance requirements.

Vendor stability

Consider the long-term outlook. Established vendors with dedicated R&D resources are more likely to evolve with an organization’s needs and provide ongoing support. Be cautious of providers for whom digital twin functionality is an add-on rather than a core focus.

What does the use of digital twin technology look like in the real world? Here’s a look at common scenarios and models where digital replicas can help organizations predict and avoid costly delays and production disruptions.

Manufacturing optimization

A consumer electronics manufacturer used digital twins to replicate production equipment and uncover hidden bottlenecks. By simulating scheduling changes virtually, they reduced cycle times by 15% without altering physical infrastructure. Predictive modeling also improved process change accuracy by 94%.

Product development acceleration

An automotive parts supplier replaced costly prototypes with digital simulations. Using digital twins to run thousands of virtual stress tests and usage scenarios, they cut development costs by 60% and reduced time-to-market by eight weeks. Products validated with digital twins showed 40% fewer field failures in year one.

Supply chain resilience

A logistics company built digital replicas of their distribution network to anticipate and resolve disruptions. By integrating real-time inputs, such as vehicle data, weather, traffic and demand, they maintained 99.2% on-time delivery during peak season and cut fuel costs by 12%.

Smart building management

A commercial real estate firm implemented digital twins across multiple properties to reduce energy consumption and improve tenant comfort. Real-time data from HVAC systems, occupancy sensors and utility rates enabled dynamic adjustments, which resulted in a 25% drop in energy costs, fewer maintenance requests and higher tenant satisfaction.

Digital twin technology is rapidly evolving, but so are the misunderstandings around it. Here are some of the most common myths that can lead to misaligned expectations or stalled initiatives.

1. Digital twins are just 3D models

While 3D visualization can be part of a digital twin, it’s only one layer. A true digital twin processes live data, adapts to real-world changes and provides predictive insights. Static models can’t simulate future scenarios or inform real-time decisions.

2. Tech stacks will need to be replaced

The most effective digital twin platforms integrate with existing PLM, ERP and CAD systems. Replacing foundational systems adds risk and complexity, while integration allows faster adoption and greater return on investment.

3. Digital twins are only for large enterprises

Thanks to cloud-based platforms and modular implementations, digital twin technology is now accessible to mid-sized companies. Many organizations start small by applying digital twins to a single product line or process, then scale as value is proven.

4. Data simulation alone is a digital twin

Simulation is just one element. A digital twin continuously syncs with its physical counterpart through live data streams. Offline models may help with analysis, but they can’t support real-time monitoring or predictive feedback loops.

5. One-size-fits-all digital twins work for every business

Digital twin needs vary widely by industry and use case. A manufacturer may focus on process optimization and predictive maintenance, while a commercial real estate firm prioritizes energy efficiency and occupant comfort. The right platform should align with sector-specific goals.

Digital twin software delivers the most impact when it connects to a broader technology ecosystem, especially product lifecycle management (PLM) platforms. PLM data such as design specs, materials and performance requirements form the foundation of accurate digital models.

In return, real-world performance data captured by digital twins flows back into PLM, closing the loop and informing smarter, next-gen product design. This interoperability extends across CAD, BIM, ERP and MES systems. The key is frictionless data exchange without manual uploads or complex customizations. Digital twins aren’t meant to stand alone; they work best as part of an integrated, insight-driven environment.

What does integration look like in real-world organizations? That can include:

• real-time sensor integration with current IoT infrastructures
• advanced simulation and predictive analytics
• bidirectional compatibility with PLM and CAD systems
• cloud-based architecture with robust security and access controls

The main takeaway: digital twin software is powerful, but it can only be leveraged to maximum effect when it works seamlessly with existing business environments.

Digital twin software is shifting organizations from reactive problem-solving to proactive, predictive operations. By leveraging real-time data and simulation, brands in the most complex industries can reduce costs, improve efficiency and accelerate innovation while preventing issues before they occur.

Want to see how digital twins can transform the product development process? Request a demo of Centric PLM™ today and explore how our platform helps brands streamline operations, gain predictive insights and stay ahead of the competition.