Operational Digital Twin Services
Build operational digital twins that create live virtual replicas of your physical assets, processes, and systems. Enable predictive maintenance, optimize operations, and make data-driven decisions with AI-powered simulation.
Digital Twin
Real-Time Simulation
Twin Capabilities
The Digital Twin Advantage
Traditional operations rely on snapshots and assumptions. Digital twins provide continuous, real-time insight into your physical world—enabling prediction, simulation, and optimization that weren't possible before.
You can't see what's happening inside your equipment and processes in real-time. Decisions are based on periodic inspections, historical reports, and operator intuition—not live data.
Equipment fails unexpectedly, causing costly downtime and emergency repairs. Without predictive insights, maintenance is either reactive (too late) or calendar-based (wasteful).
Your processes run well enough, but you know there's room for improvement. Without the ability to simulate changes, optimization is trial and error on live systems.
Sensor data, SCADA systems, ERP, and maintenance records live in separate systems. There's no unified view that connects physical operations with business context.
A digital twin mirrors your physical assets in real-time. See exactly what's happening inside equipment, across production lines, or throughout your facility—updated continuously from live sensor data.
AI models detect anomalies, predict failures before they happen, and recommend optimal maintenance windows. Move from reactive to predictive—fixing problems before they cause downtime.
Test changes in the digital world before implementing them physically. Simulate scenarios, optimize parameters, and validate improvements without risking production.
Connect IoT sensors, PLCs, SCADA, historians, and enterprise systems into a single operational platform. Contextualize data across the asset lifecycle for complete visibility.
Our Services
From strategy to implementation, we provide end-to-end digital twin services that transform how you monitor, predict, and optimize your physical operations.
We assess your assets, processes, and data landscape to identify high-value digital twin opportunities. Our assessment includes ROI modeling, technology recommendations, and a phased implementation roadmap.
Connect your physical assets to the digital world. We integrate sensors, PLCs, SCADA systems, historians, and enterprise applications to create unified, real-time data streams that power your digital twin.
Build accurate virtual representations of your assets using physics-based models, machine learning, or hybrid approaches. Our models capture equipment behavior, process dynamics, and system interactions.
Leverage AI to predict equipment failures, optimize maintenance schedules, and reduce unplanned downtime. Our predictive models learn from historical patterns and real-time data to forecast issues before they occur.
Use digital twins to simulate scenarios, test changes, and optimize operations without disrupting production. Identify bottlenecks, optimize parameters, and validate improvements in the virtual world first.
Make digital twin insights accessible through intuitive interfaces. From real-time dashboards to 3D visualizations and AR/VR experiences, we create interfaces that operators and executives actually use.
Ready to build your digital twin? Let's assess your opportunities.
Get a Free Digital Twin AssessmentOur Process
Building an operational digital twin requires the right combination of domain expertise, data engineering, and modeling skills. Our proven methodology delivers digital twins that provide real business value.
We start by understanding your assets, processes, and objectives. This includes inventorying equipment, mapping data sources, assessing data quality, and identifying the highest-value digital twin use cases.
Key Activities
Deliverables
Assessment report, use case roadmap, business case
Design the data foundation for your digital twin—how data flows from physical assets through edge devices, into the cloud, and powers models and visualizations. This includes integration patterns with existing systems.
Key Activities
Deliverables
Architecture diagrams, integration specs, data models
Build the digital twin models that represent your physical assets and processes. Depending on requirements, this may include physics-based simulation, machine learning models, or hybrid approaches.
Key Activities
Deliverables
Validated digital twin models, model documentation
Deploy the digital twin platform with real-time data ingestion, model execution, and visualization layers. This includes setting up IoT connectivity, data pipelines, and user interfaces.
Key Activities
Deliverables
Production-ready platform, operational dashboards
Validate that the digital twin accurately represents real-world behavior. Compare model outputs against actual measurements, calibrate parameters, and iterate until accuracy meets requirements.
Key Activities
Deliverables
Validated twin, accuracy reports, calibration docs
Roll out the digital twin to production users. This includes operator training, documentation, establishing operational procedures, and ensuring your team can manage and evolve the system.
Key Activities
Deliverables
Live digital twin, trained users, operational docs
Digital twins improve over time with more data and feedback. We help you establish processes for model refinement, adding new use cases, and scaling to additional assets.
Key Activities
Deliverables
Improved models, expanded capabilities, scale roadmap
Our digital twin delivery is powered by SPARK™—our framework that brings predictability, quality gates, and transparent communication to complex engineering projects. Every phase has defined outcomes and success criteria.
Technology Stack
We leverage leading IoT platforms, simulation tools, and analytics technologies to build digital twins that scale. Our technology choices are driven by your requirements—not vendor preferences.
Sensor integration and data collection
Enterprise digital twin solutions
Physics and ML modeling tools
Time-series and analytics platforms
3D rendering and visual interfaces
Cloud platforms and DevOps
Platform-agnostic approach: We help you choose the right technology for your specific needs—whether that's a commercial digital twin platform or a custom-built solution on cloud infrastructure.
Why Digital Twins
Digital twins deliver measurable business value across maintenance, operations, and decision-making. Here's what organizations gain from digital twin implementation.
Predict equipment failures before they happen. Shift from reactive to predictive maintenance and dramatically reduce unplanned downtime.
25-50%
Downtime reduction
Simulate changes and optimize parameters in the digital world. Implement improvements confidently knowing they've been validated virtually.
10-30%
Efficiency improvement
See inside your equipment and processes in real-time. Understand exactly what's happening across your entire operation from a single view.
Real-Time
Asset monitoring
Test new products, processes, and configurations virtually before physical implementation. Reduce development cycles and time-to-market.
40-60%
Faster development
Test scenarios and changes in a safe digital environment. Understand impacts before making changes to physical systems.
Zero Risk
Virtual testing
Optimize maintenance schedules based on actual condition, not arbitrary intervals. Extend equipment life while reducing maintenance costs.
20-40%
Maintenance savings
Give operators and executives the insights they need. Data-driven decisions based on real-time conditions, not assumptions.
Data-Driven
Decision making
Digital twins improve over time. Machine learning models get smarter with more data, delivering increasing value.
Self-Improving
AI models
Use Cases
Digital twins deliver value across industries—from manufacturing floors to smart buildings to supply chains. Here are the scenarios where digital twins have the greatest impact.
Optimize Production Lines & Equipment
Monitor production equipment in real-time, predict failures before they cause downtime, and optimize process parameters. Digital twins help manufacturers improve OEE, reduce scrap, and accelerate changeovers.
Common Applications
Smart Building Operations
Create digital twins of facilities to optimize HVAC, lighting, and space utilization. Reduce energy costs, improve occupant comfort, and enable predictive maintenance of building systems.
Common Applications
Grid & Asset Management
Model power generation, transmission, and distribution assets. Optimize grid operations, predict equipment failures, and improve reliability. Digital twins help utilities balance load and integrate renewables.
Common Applications
End-to-End Supply Chain Visibility
Create digital twins of your supply chain to simulate disruptions, optimize inventory, and improve logistics. Gain visibility across suppliers, warehouses, and transportation in real-time.
Common Applications
Engagement Models
Whether you need a quick assessment, a pilot project, or a long-term partnership — we have an engagement model that fits your needs.
We analyze your codebase, processes, and team dynamics to identify bottlenecks and opportunities. You get a clear roadmap — no commitment required.
Ideal for: Teams wanting an objective assessment before committing
Learn moreStart with a focused pilot project. A small Pod works alongside your team on a real deliverable, so you can evaluate fit and capabilities with minimal risk.
Ideal for: Teams wanting to test the waters before scaling
Learn moreDedicated cross-functional teams that integrate with your organization. Full accountability for delivery with built-in QA, architecture reviews, and the SPARK™ framework.
Ideal for: Teams ready to scale with a trusted partner
Learn moreNeed specific skills? Augment your team with vetted engineers who work under your direction. React, Node, Python, AI engineers, and more.
Ideal for: Teams with clear requirements and strong internal leadership
Learn moreLet's talk about your goals, team structure, and timeline. We'll recommend the best way to start — with no pressure to commit.
Schedule a Free ConsultationAn operational digital twin is a virtual representation of a physical asset, process, or system that mirrors its real-world counterpart in real-time. Unlike static models or CAD drawings, digital twins are dynamic—continuously updated with live data from sensors, control systems, and enterprise applications.
The concept originated in aerospace manufacturing at NASA, where virtual replicas were used to simulate and diagnose issues with spacecraft. Today, digital twins have expanded to manufacturing, energy, buildings, supply chains, and virtually any domain where physical operations meet digital technology.
What makes operational digital twins powerful is the bidirectional relationship between physical and digital. Data flows from the physical world into the twin, enabling monitoring and prediction. Insights from the twin flow back to optimize the physical system. This continuous loop creates a living model that improves operations over time.
It's important to distinguish digital twins from related concepts:
Digital twins may incorporate 3D models for visualization and simulations for prediction, but they're distinguished by their real-time data connection and operational use.
Digital twins exist at different levels of abstraction, from individual components to entire enterprises. Understanding these levels helps identify the right starting point for your digital twin journey.
The most granular level—a digital representation of a single component like a motor, valve, or sensor. Component twins monitor the health and performance of critical parts, enabling condition-based maintenance and failure prediction.
A collection of component twins that together represent a complete asset—a machine, vehicle, or piece of equipment. Asset twins provide a holistic view of equipment health and performance, considering the interaction between components.
Models an entire production process or workflow, connecting multiple asset twins. Process twins optimize throughput, identify bottlenecks, and simulate changes to process parameters. They're particularly valuable in manufacturing and continuous process industries.
Represents an entire system or facility—a factory, building, or power grid. System twins integrate multiple process twins and provide enterprise-level visibility and optimization across the entire operation.
The highest level—a digital representation of the entire enterprise including supply chain, operations, and business processes. Enterprise twins enable strategic decision-making by connecting operational data with business outcomes.
A complete digital twin solution consists of several interconnected layers, each serving a specific purpose in the overall architecture.
The foundation of any digital twin is data. This layer connects to physical assets through IoT sensors, PLCs, SCADA systems, and existing historians. It also integrates enterprise data from ERP, MES, CMMS, and other business systems. Edge computing processes data locally when latency or bandwidth constraints exist.
Raw data must be ingested, stored, processed, and contextualized. Time-series databases store sensor data efficiently. Data pipelines transform and enrich data. Semantic models add context—connecting a temperature reading to a specific pump in a specific location.
The core intelligence of the digital twin. This includes physics-based models that simulate asset behavior based on engineering principles, and machine learning models that learn patterns from historical data. Hybrid approaches combine both for accuracy and adaptability.
Algorithms that turn data into insights: anomaly detection to identify unusual behavior, predictive models to forecast failures, optimization algorithms to improve performance. This layer transforms the digital twin from a monitoring tool to a decision support system.
How users interact with the digital twin. This includes real-time dashboards for operators, 3D visualizations for engineers, mobile apps for field technicians, and AR/VR experiences for immersive interaction. The right interface makes insights actionable.
Digital twins are most valuable when insights drive action. This layer integrates with work order systems to trigger maintenance, control systems to adjust parameters, and business systems to inform planning decisions.
Successful digital twin implementations follow a structured approach that balances quick wins with long-term vision. Here's how we approach digital twin projects at Salt.
Start by identifying the business problems you want to solve. Digital twins can address many use cases—predictive maintenance, process optimization, quality improvement—but trying to do everything at once leads to failure. Prioritize use cases by business value and feasibility.
Assess your data landscape: what sensors exist, what data is already being collected, what systems need integration. Identify gaps that need to be addressed. Define success metrics that will prove value.
Design the technical architecture that will support your digital twin. This includes decisions about IoT connectivity, edge vs. cloud processing, data storage strategy, and platform selection. Build the data pipelines that will feed the digital twin.
Don't underestimate the importance of data quality. Garbage in, garbage out applies to digital twins. Invest in data validation, cleansing, and contextualization.
Build the models that represent your physical systems. Start simple—often a basic model provides significant value. Physics-based models are appropriate when the underlying behavior is well understood. Machine learning works well for complex systems where patterns exist in historical data.
Validate models against real-world data. Calibrate parameters until accuracy meets requirements. Document assumptions and limitations.
Deploy the complete digital twin solution with all layers integrated. This includes the data pipelines, model execution environment, visualization interfaces, and integration with operational systems. Consider scalability— pilot solutions need to expand to full production.
Train operators and stakeholders on using the digital twin. Establish operational procedures. Define governance for model updates and data quality. Monitor system health and model accuracy over time.
Digital twins are never "done." Models improve with more data. New use cases emerge once the foundation is in place. Expand to additional assets and processes. The digital twin evolves as your operations evolve.
The digital twin market offers both commercial platforms and the option to build custom solutions. The right choice depends on your specific requirements, existing technology stack, and in-house capabilities.
Enterprise platforms like Azure Digital Twins, AWS IoT TwinMaker, Siemens MindSphere, and PTC ThingWorx provide pre-built capabilities for common digital twin patterns. They accelerate implementation but may have limitations for highly customized use cases.
For organizations with unique requirements or strong engineering teams, custom digital twin solutions built on cloud infrastructure offer maximum flexibility. Open-source components for IoT, time-series databases, ML frameworks, and visualization can be combined to create tailored solutions.
Digital twin implementations face several common challenges. Understanding these upfront helps you plan effectively and avoid pitfalls.
The most common challenge. Many organizations discover that the data they thought they had is incomplete, inaccurate, or siloed. Legacy equipment may lack sensors. Data historians may not retain sufficient history. Addressing data gaps often requires investment in sensors and infrastructure.
Bridging operational technology (OT) and information technology (IT) worlds is challenging. OT systems have different security requirements, communication protocols, and organizational ownership. Digital twin projects often become exercises in organizational alignment as much as technical implementation.
Building models that accurately represent physical systems is hard. Equipment behavior changes over time. Operating conditions vary. Models must be continuously validated and calibrated. Setting realistic accuracy expectations and budgeting for model maintenance is essential.
Pilot projects often succeed but struggle to scale. Solutions that work for one asset may not work for thousands. Architecture decisions made early have long-term implications. Design for scale from the beginning.
Digital twins change how people work. Operators need to trust and use the insights. Maintenance teams need to act on predictions. Leadership needs to make decisions based on data. Technical implementation is often easier than driving adoption.
Digital twin benefits can be hard to quantify upfront. Avoided downtime, improved efficiency, and better decisions are real but challenging to measure. Build the business case carefully, start with high-value use cases, and establish metrics to prove value.
Based on successful digital twin implementations, these best practices improve outcomes:
Digital twins are means to an end—don't build a twin because it's cool technology. Define the business problems you're solving. Predictive maintenance? Process optimization? Quality improvement? Clear objectives guide every decision.
Don't try to build an enterprise twin on day one. Start with a single asset or process where you can prove value quickly. Learn what works, refine your approach, then scale. Pilots should be meaningful enough to demonstrate value but small enough to execute quickly.
Your digital twin is only as good as its data. Invest in sensors, connectivity, and data quality upfront. Build robust data pipelines that can handle scale. Establish data governance. The data foundation serves all future digital twin use cases.
Basic models often provide significant value. A simple threshold-based anomaly detection may catch 80% of issues. Complex ML models can come later. Resist the urge to over-engineer. Get something working, prove value, then iterate.
The best digital twin is useless if nobody uses it. Design interfaces for your actual users. Involve operators early. Make insights actionable. Integrate with existing workflows rather than creating parallel processes.
Digital twins require ongoing care. Models drift as equipment ages. Data pipelines break. New use cases emerge. Budget for ongoing model calibration, system maintenance, and continuous improvement.
Salt brings a differentiated approach to digital twin implementation. Here's what sets us apart:
Engineering Depth: Digital twins require expertise across IoT, data engineering, machine learning, and visualization. Our teams bring deep technical skills across all these domains—we're not just connecting platforms, we're building intelligent systems.
Industry Experience: We've built digital twins for manufacturing, energy, and facilities. We understand the domain challenges— OT/IT integration, industrial protocols, and operational workflows—not just the technology.
Platform-Agnostic: We help you choose the right technology for your needs—commercial platforms, custom solutions, or hybrid approaches. No vendor lock-in, just practical recommendations based on your requirements.
End-to-End Capability: From strategy to implementation to operations, our managed pods handle the complete digital twin lifecycle. No handoffs between consulting teams and implementation teams.
SPARK™ Delivery Framework: Our SPARK™ framework brings structure to complex digital twin projects. Clear phases, quality gates, and success metrics ensure predictable delivery.
AI-Native Approach: We use AI throughout our engineering process—and we build AI into digital twins. Predictive models, anomaly detection, and optimization algorithms are core capabilities, not afterthoughts.
Ready to explore how digital twins can transform your operations? Schedule a free assessment with our team to discuss your digital twin goals and how Salt can help you build operational intelligence.
Industries
We've built software for companies across industries. Our teams understand your domain's unique challenges, compliance requirements, and success metrics.
HIPAA-compliant digital health solutions. Patient portals, telehealth platforms, and healthcare data systems built right.
Scale your product fast without compromising on code quality. We help SaaS companies ship features quickly and build for growth.
Build secure, compliant financial software. From payment systems to trading platforms, we understand fintech complexity.
Platforms that convert and scale. Custom storefronts, inventory systems, and omnichannel experiences that drive revenue.
Optimize operations end-to-end. Route optimization, warehouse management, and real-time tracking systems.
Hire dedicated developers to extend your team
Whether you need to build a new product, modernize a legacy system, or add AI capabilities, our managed pods are ready to ship value from day one.
100+
Engineering Experts
800+
Projects Delivered
14+
Years in Business
4.9★
Clutch Rating
FAQs
Common questions about operational digital twins, from implementation to ROI and technology choices.
An operational digital twin is a virtual representation of a physical asset, process, or system that mirrors its real-world counterpart in real-time. Unlike static models, digital twins are continuously updated with live data from sensors and systems, enabling real-time monitoring, predictive analytics, simulation, and optimization. They bridge the gap between physical operations and digital insights.
A 3D model is a static visual representation used for design and documentation—it has no real-time data connection. A simulation is a mathematical model that predicts behavior under different conditions, typically run offline. A digital twin combines aspects of both but adds real-time data connectivity, continuously synchronizing with physical systems. This live connection enables operational use cases like monitoring, prediction, and optimization that static models can't support.
Digital twins deliver value in any industry with physical assets or processes to optimize. Manufacturing uses digital twins for equipment monitoring and production optimization. Energy and utilities model power grids and generation assets. Buildings and facilities optimize HVAC and space utilization. Supply chain and logistics simulate distribution networks. Healthcare creates twins of medical equipment. The common thread is physical operations that benefit from real-time visibility and prediction.
Timeline varies based on scope and complexity. A focused pilot for a single asset or process typically takes 3-6 months from assessment to production. Enterprise-scale digital twins that cover entire facilities or supply chains may take 12-18 months. We recommend starting with a focused pilot that proves value quickly, then scaling to additional assets and use cases. This phased approach delivers value faster and reduces risk.
Digital twins require real-time operational data from sensors, PLCs, SCADA systems, or IoT devices—things like temperature, pressure, vibration, flow rates, and equipment status. They also benefit from contextual data: asset metadata, maintenance history, process parameters, and business data. Many organizations discover data gaps during assessment. Part of our process is identifying what data exists, what's missing, and how to address gaps.
It depends on your current instrumentation. Many organizations have more data available than they realize—existing PLCs, SCADA systems, and historians may already collect useful data. Our assessment identifies what's available and what gaps need to be filled. Sometimes additional sensors are needed, but we prioritize leveraging existing infrastructure. Modern IoT sensors are relatively inexpensive and can be retrofitted to legacy equipment.
Accuracy depends on model quality, data quality, and the specific use case. Well-calibrated models for predictive maintenance typically achieve 80-90% accuracy in predicting failures days or weeks in advance. Process optimization models can accurately simulate the impact of parameter changes. We validate all models against real-world data and calibrate until accuracy meets your requirements. Models also improve over time as they learn from more data.
ROI varies by use case. Predictive maintenance typically delivers 25-50% reduction in unplanned downtime and 10-40% reduction in maintenance costs. Process optimization often yields 10-30% improvement in efficiency or throughput. Energy optimization can reduce consumption 15-25%. We help quantify expected ROI during assessment by analyzing your specific operations, historical costs, and improvement opportunities. Most digital twin projects achieve positive ROI within 12-18 months.
It depends on your requirements and capabilities. Commercial platforms like Azure Digital Twins, AWS IoT TwinMaker, or Siemens MindSphere accelerate implementation with pre-built capabilities. They're good choices when your use cases align with platform strengths. Custom solutions built on cloud infrastructure offer maximum flexibility for unique requirements. Many implementations are hybrid—commercial platforms for standard capabilities, custom development for differentiated features. We help you evaluate options based on your specific needs.
Digital twins integrate at multiple levels. At the OT layer, they connect to PLCs, SCADA, historians, and IoT devices using industrial protocols (OPC-UA, MQTT, Modbus). At the IT layer, they integrate with ERP, MES, CMMS, and other business systems via APIs. Digital twin insights can trigger work orders in maintenance systems, update parameters in control systems, or inform planning in business systems. We design integration architecture that works with your existing technology landscape.
Security is critical when connecting operational technology to digital systems. We implement defense-in-depth: network segmentation between IT and OT, secure edge gateways, encrypted data transmission, authentication and authorization controls, and comprehensive monitoring. Our architectures follow frameworks like IEC 62443 for industrial cybersecurity. Data can flow from OT to digital twins without exposing control systems to internet-connected environments.
Digital twins are designed to be used by your existing teams—operators, maintenance technicians, and engineers. The interface layer presents insights in accessible formats: dashboards, alerts, and recommendations that don't require data science expertise. However, maintaining and improving the underlying models does require specialized skills. We provide training and can support ongoing model maintenance as part of our engagement, or help build internal capabilities over time.
Yes, digital twins work with legacy equipment. Older machines may not have built-in connectivity, but retrofit sensors and edge devices can collect the data needed. Vibration sensors, temperature probes, current monitors, and other non-invasive sensors can be added without modifying equipment. The key is identifying what parameters matter for your use cases and finding practical ways to measure them. We've successfully built digital twins for equipment spanning decades.