Digital twins in manufacturing

This article is written by Shreya Desai. She is experienced in Process improvement.

What If Your Assembly Line Had a Mind of Its Own? A system on your shop floor that not only sees—but senses. One that learns your bottlenecks before your planners do. That catches torque drift before QA flags it. That knows what’s about to go wrong—not based on guesswork or KPIs from last month—but because it’s reading your factory in real time. Yes, I am talking about digital twins in manufacturing.

Too good to be true?

Welcome to the world of Digital Twins: where your machines no longer just work.
They talk, they think, and in some ways, they whisper truths you didn’t know you were ignoring.

Let’s start with an example of use of digital twin in Manufacturing

Study the below picture very well. This picture explains how digital twin works and how it can be utilized. This picture explains how a digital twin of a electric motor is used for its predictive maintenance in manufacturing.

Let’s dive deep into the illustration of the picture.

You can see a physical motor and sensors are attached to it to sense its speed of rotation, sound, temperature and vibration.

Now this sensed data is fed to the virtual simulation model. These can be a virtusl working 3d model with displays data sensed from the sensors or it can be graphs or other visualisation. This is called digital twin.

Now the most import part, which is shown at the right side of the image, AI/analytics model which predicts what happen over time or when will a failure occur to this motor and for what reason.

Using this data the maintenance people will be able to do maintenance activity at right place, right time and at the required frequency.

You can also see an alarm in the picture which notifies the maintenance team if anything goes wrong.

This is the basics on how digital twins in manufacturing are revolutionizing productivity, enabling predictive maintenance, reducing downtime, and optimizing factory operations through real-time virtual simulations.

Here we have explained only about predictive maintenance, but the basics for all applications are same.

Hope you got an idea about digital twin.

Let’s continue…

Why Are We Still Reacting?

Every manufacturing professional talks about quality, uptime, OEE. But here’s a harder question:

Why do we still rely on symptoms- downtime reports, rework rates, customer complaints to act?
Why do we wait until a line crashes to call maintenance?
Why do we test improvements on the live line, risking output?

The problem isn’t that we lack data. The problem is that we don’t listen to it as a system…

This is where the Digital Twin revolution begins. Not as a fancy tech showcase, but as a shift in mindset.

What Is a Digital Twin, Really?

A Digital Twin is your process, reflected in data, alive in logic, and unrestricted by physical limits.

It’s not just a model. It’s a thinking double of your operation that lets you-

• Observe without disrupting
• Predict without guessing
• Improve without experimenting blindly

It merges live sensor data, machine learning, process logic, and real-time simulation into one coherent story- a story that reveals not just what is happening, but why, and what to do next.

Here’s an uncomfortable truth: most issues on assembly line aren’t discovered in time—they’re tolerated.

Loose torques here, panel fitment gaps there, a robot skipping a beat once every few cycles. You can feel it, the team talks about it… but no one has the full picture.

Now imagine this:

• Every workstation, robot, human, and part is mirrored in a virtual space.
• Every sensor feeds into a logic-driven simulation.
• Every decision you make can be tested before it affects production.

You Don’t Need a Smart Factory. You Need a Smarter Lens–

Everyone wants “smart factories.” But here’s the catch:

You don’t need to overhaul your factory. You need to see it differently.

Digital Twins challenge the way we think about process improvement. Instead of:

• Fixing what’s broken,
  We ask: What is silently drifting?

Instead of:

• Chasing KPIs,
  We ask: Are we measuring what really matters?

You could have world-class automation, but still run blind.
You could have manual stations, but with the right digital insight, be 10x smarter.

At its heart, a Digital Twin isn’t a tool.
It’s a different way of thinking about what is happening in the real physical world.

It says:

• Reality is complex, but not unknowable.
• Data is chaotic, but patterned.
• Change is risky, but controllable—if you simulate first.

And maybe most radically: every factory has an untapped mind. A logic waiting to be mapped. A set of insights waiting to be read. But only if we build its twin.

So, Where Should You Start?

Not with software.

Start with a question you’ve never fully answered:

• “Why does Station X always seem behind when humidity goes up?”
• “What’s the real reason for this repeated rework spike every Friday?”
• “What’s the hidden cost of our current takt time setup?”

Then:

• Mirror one workstation.
• Feed in real data.
• Simulate.
• Compare.
• Learn.
• Expand.

It’s not about digital transformation. It’s about Digital Translation– giving your physical world a voice.

Think of it as the neural layer of your factory—built with a real-time feedback loop between physical sensors, PLCs, and cloud platforms. It pulls live data from tools, assembly robots, conveyors—digitizes their behavior—and simulates thousands of future scenarios before you even hit a red light.

It’s not just a replica—it’s a living system trained to detect the micro-shifts you can’t. A deviation in spindle vibration. A latency in arm retraction. A thermal drift no eye can catch.

If your Digital Twin is set up right—with edge computing, IIoT connectivity, CAD/CAE data fusion, and AI-driven analytics—you’re not just monitoring. You’re predicting. Preventing. Evolving.

So maybe the better question isn’t what can a Digital Twin do for you?
Maybe it’s this: Can you afford not to know what your process is silently trying to tell you?

About the Author

Shreya Desai

Results-driven Industrial Engineer with experience in manufacturing process optimization, continuous improvement, and supply chain management. Strong background in lean manufacturing principles, process automation, and digital transformation in automotive production.

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