Operational technology (OT) environments combine legacy programmable logic controllers (PLCs), cloud analytics, edge devices and artificial intelligence (AI)-driven controllers, which all have to operate together in real time. However, this mix raises risk. Faults can cascade across systems and trigger costly, unplanned downtime.

Operational digital twins prevent these failures. This technology creates physics-based models of assets in real time, enabling you to simulate scenarios and predict downtime. Integrated into your reliability program, they can cut outages and extend asset life.

The New Paradigm of OT Reliability

Reliability used to mean one thing — keep systems running. Today, it involves more than that. OT teams must guard uptime and prevent performance degradation, improve efficiency and protect systems from cyber risk. When a motor runs slower or a process drifts out of specification, that drop in performance can be just as costly as an outage.

This transition requires you to shift from reactive fixes to proactive operations. Rather than repairing after failure, you need tools that identify problems early and allow you to test fixes safely.

Operational digital twins are specifically designed for this purpose. They let you reproduce field behavior, run “what if” scenarios and validate mitigations without interrupting production. Integrated into maintenance workflows with strong OT security, you prevent issues from occurring in the first place.

The Anatomy of an Operational Digital Twin

Most teams recognize that digital twins can enhance uptime. In fact, one report discovered that 86% of industrial senior executives acknowledge the applicability of the technology. However, many have yet to start because the work looks complex. Still, if you break the technology into clear pieces, the project stops feeling like a huge leap.

• Physical assets and process data: The twins’ foundation is the real equipment and the data it creates. This information includes machine states, setpoints, cycle counts, alarms and time series. Accurate source data is nonnegotiable.
• Internet of Things (IoT) sensors and edge devices: Sensors and edge computing hardware capture and pre-process real-time signals, such as temperature, vibration, pressure, flow and more. Edge devices reduce latency and filter noise before data reaches the technology.
• Data integration: Reliable, secure pipelines move data from the plan to the model. That includes buffering, normalization, timestamp alignment and handling dropped packets. The use of authenticated connections and role-based access is key to preventing your twin from becoming a new attack surface.
• Virtual model and analytics engine: The digital twin itself is made up of physics-aware models, statistical models and machine learning or AI. Together, they simulate behavior, run scenarios and produce predictions. How you design the models is critical so you can validate them against field data.
• Visualization: Dashboards, alerts and integrations with systems let engineers act on model output. The best interfaces translate model output into clear, prioritized tasks for operators and maintenance teams, enabling them to perform their duties efficiently and effectively.
• Security integration: The twin should include cybersecurity controls, change management and clear ownership. It should exist in workflows so its outputs trigger predictable, auditable actions.

Map which of these pieces you already have and which need investment. Building a minimal, validated twin for one critical asset is a good first step before expanding from there.

A Framework for Digital Twin Integration

For a digital twin to become a reality, you must start with clear outcomes. Use the four phases below to go from goals to live operations.

Phase 1 — Define Reliability Goals and KPIs

Begin with specific problems you need to solve, such as reducing unplanned downtime or cutting mean time to repair (MTTR). Write two to three measurable key performance indicators (KPIs) tied to those problems.

Align stakeholders early and agree on how to measure your success. Doing so prevents scope creep and keeps the twin focused on delivering value.

Phase 2 — Data Acquisition and Virtual Modeling

Map the physical sources you need, including PLCs, Supervisory Control and Data Acquisition (SCADA) systems, machine sensors and your IoT edge devices. Inventory what data each source provides and close the most critical gaps first.

With that data in hand, build a minimal virtual model. It can be a physics-aware or hybrid model that mimics the key behaviors of a single asset. Validate the model against historical failures and live telemetry to ensure predictions are grounded in reality before expansion.

Phase 3 — Simulation for Predictive Reliability

Use the model to run targeted scenarios for load spikes, degraded seals, sensor drift or operator mistakes. Digital twins let you simulate, test and optimize manufacturing processes in software so you can see failure modes and fix controls without stopping the line.

Prioritize simulations that map directly to your KPIs. Capture results as ranked actions and confidence scores, allowing maintenance and engineering to act on testable recommendations.

Phase 4 — Real-Time Monitoring and Control

Turn the validated twin into an operational tool. Stream live telemetry to the model, compute anomaly scores or remaining useful life estimates and feed those outputs into your operator dashboards. Set thresholds that generate prioritized work orders rather than noisy alerts.

Ensure you govern the live twin as you would any other OT asset. Apply role-based access, change control and periodic model re-validation. When the technology is integrated into daily workflows, it becomes a predictable, auditable capability.

Bolstering OT Security with Digital Twins

Operational technology systems are always under attack. An OT security report found that about 70% of industrial firms reported an operational technology cyber incident from the year prior. Cybersecurity threats are a major cause for concern because attacks often cause downtime, equipment damage and prolonged performance loss.

Fortunately, a digital twin gives you a safe, production-free sandbox to test patches, configuration changes and control-logic updates before pushing them into the plant. Use it to validate firmware and software updates, exercise incident playbooks and measure how a fix changes key metrics without risking operations.

Make sure the security gains are repeatable. Feed the data to the technology, run regular patch-validation jobs and gate any change with an automated test in the model before rollout. Pair this with strict access controls and logging so the twin remains secure. Then use the results to create documentation that maintenance and security can act on.

The Future of Resilient Operations

Digital twins stop being experiments when you look at them as projects with measurable outcomes. Govern it like any OT asset and ensure it stays reliable and safe. When a validated one runs in production, you detect problems earlier, prevent outages and improve asset life.

Share what’s worked for you on digital twins and learn from others’ experiences in ISA Connect Forums: Digital Twin.