I attended a manufacturing technology conference last month. Every second booth was selling something involving “digital twins.” The term has become so overused it’s almost meaningless.

Which is a shame, because digital twins can be genuinely useful. You just need to separate the hype from reality.

What a digital twin actually is

At its simplest, a digital twin is a virtual representation of a physical thing that updates in real-time based on data from that thing.

The key word is “updates.” A static 3D model of your factory isn’t a digital twin. A CAD drawing isn’t a digital twin. A digital twin is connected to the real world and reflects its current state.

The concept comes from NASA, who used simulation models to monitor and troubleshoot spacecraft. They’d have an identical system on the ground, updated with telemetry from space, so engineers could test scenarios without touching the real equipment.

For manufacturing, digital twins can represent:

• Individual machines (performance, condition, settings)
• Production lines (flow, bottlenecks, status)
• Entire facilities (layout, material flow, logistics)
• Products (tracking configuration, history, quality)

What they’re actually good for

Let me cut through the marketing and explain where digital twins genuinely add value.

Simulation and what-if analysis

Want to know what happens if you change a process parameter, reroute material flow, or add a new machine? A digital twin lets you test in simulation before touching the real system.

A beverage manufacturer I know uses their digital twin to test production schedule changes. “What if we switch the order of products on Line 2?” They can simulate it overnight and see the impact without disrupting actual production.

Remote monitoring and troubleshooting

When you can see a virtual representation of what’s happening in the factory—not just numbers but visualised state—troubleshooting gets easier. Especially useful for multi-site operations or when experts can’t be physically present.

During COVID, this became critical for some manufacturers. Engineers could diagnose and advise on problems without travelling to sites.

Training and onboarding

New operators can learn on a digital twin without risk of breaking expensive equipment or creating quality problems. Some companies have cut training time significantly this way.

Predictive optimisation

Combining digital twins with AI enables predicting future states and optimising proactively. What’s the optimal setting for these conditions? What maintenance should be scheduled? The twin provides context that standalone analytics miss.

What they’re not good for

Simple monitoring needs

If you just need to know whether a machine is running and at what speed, you don’t need a digital twin. A basic dashboard is cheaper and simpler.

I’ve seen companies spend six figures on digital twin platforms when a $20,000 monitoring system would have done what they needed.

One-off analysis

If you’re investigating a specific problem once, building a digital twin for it is overkill. Do the analysis directly. Digital twins pay off when they’re used repeatedly.

Facilities that change constantly

The value of a digital twin depends on keeping it synchronised with reality. If your factory layout changes weekly, maintaining the twin becomes a burden.

The real costs

Let me be frank about what digital twin implementations actually cost.

Basic single-asset twin

Creating a digital twin of one machine—say, a CNC machining centre—with real-time connection and basic visualisation: $30,000-80,000 depending on complexity.

Production line twin

Digital twin of a complete production line with material flow, equipment status, and performance metrics: $150,000-400,000.

Facility-level twin

Full factory digital twin with 3D visualisation, integration across systems, and advanced analytics: $500,000-2,000,000+.

Ongoing costs

Annual maintenance, updates, platform licensing: typically 15-25% of initial cost per year.

These numbers assume you’re working with experienced integrators. DIY attempts tend to cost more in the long run.

When it makes sense

Digital twins justify their cost when:

High-value assets: For expensive equipment where downtime is costly and optimisation is valuable, the investment makes sense. A digital twin of a $10 million machine that improves uptime by 2% is easily justified.

Complex systems: When interactions between components are hard to understand intuitively, simulation helps. Simple systems don’t need it.

Frequent optimisation needs: If you’re constantly testing scenarios, the efficiency gains accumulate. If things are stable, there’s less benefit.

Multi-site operations: When you need to manage and compare multiple facilities remotely, digital twins provide consistency and visibility.

High training throughput: If you’re onboarding many operators regularly, simulation-based training scales better than equipment-based training.

A practical starting point

If you’re curious about digital twins but not ready to commit to a major implementation:

Start with a single asset: Pick one critical piece of equipment. Build a basic twin—data connection, visualisation, maybe some simple analytics. Learn what’s involved.

Use vendor offerings: Major equipment manufacturers (Siemens, Rockwell, etc.) increasingly offer digital twin capabilities for their products. These are often easier to implement than building from scratch.

Focus on specific use cases: Don’t build a twin hoping to find uses later. Start with a specific problem (e.g., “We want to simulate schedule changes before implementing them”) and build what you need for that.

Plan for maintenance: A digital twin that doesn’t reflect reality is worse than useless. Before building, establish how you’ll keep it updated as things change.

The vendor landscape

Several categories of companies offer digital twin solutions:

Automation vendors: Siemens (MindSphere/Insights Hub), Rockwell (Plex), ABB, Schneider. Advantage: integration with their automation products. Disadvantage: less flexible, lock-in.

PLM/CAD companies: PTC (Creo, ThingWorx), Dassault (3DEXPERIENCE). Advantage: strong 3D capabilities. Disadvantage: can be overkill for simpler needs.

Specialised digital twin platforms: Cosmo Tech, Bentley, various startups. Advantage: focused capabilities. Disadvantage: may need more integration work.

Custom development: Build your own using gaming engines (Unity, Unreal) combined with IoT platforms. Advantage: flexibility. Disadvantage: significant development effort.

For Australian manufacturers, local support and integration capability matter as much as the technology itself. Make sure whoever you work with has done this before in industrial contexts, not just demos.

Bottom line

Digital twins are real technology with real applications—not just buzzwords. But they’re not appropriate for every situation, and the cost is significant.

If you have specific problems that simulation, visualisation, or integrated monitoring would solve, digital twins might be worth exploring. If you’re just attracted to the concept without clear use cases, save your money.

As with any technology, start with the problem, not the solution.