Sensors, MES, asset monitoring: many Italian factories already have the data needed to build a digital twin. The problem isn’t technology — it’s knowing where to start. In this article, we explain what a digital twin in manufacturing really is and how to build it step by step, starting from the infrastructure you already have.
If you’ve already heard about the digital twin, you’ve probably seen it presented as a cutting-edge concept: research labs, multimillion-euro investments, large automotive corporations.
Yet in the everyday reality of those working in Operations within an Italian factory, the digital twin is far more accessible than it may seem.
In this article, we explore what it really means to build a digital twin in a manufacturing context, starting from what companies already have: connected machines, MES, IoT sensors, and production data. Because the goal isn’t a perfect simulation — it’s making the best possible decisions.
What is a digital twin?
A digital twin is the dynamic digital representation of a physical entity (a machine, a production line, or an entire plant) continuously updated with the real-time data that the entity generates over time.
It’s not a static 3D model. It’s not a dashboard. It’s not a PowerPoint diagram. It’s a living system that reflects, in real time—or nearly so—the operational state of its physical counterpart.
Three fundamental elements make up a mature digital twin:
- The model: the logical structure of the entity (parameters, relationships, expected behaviors)
- The data: the continuous flow coming from sensors, machines, and management systems
- The simulation capability: the ability to ask “what if…” questions without affecting the physical reality
Many manufacturing companies — including SMEs — already have the second element (the data exists) and can progressively build the first (the model). The leap to simulation comes later, once the digital twin is mature.
Why the digital twin has returned to the center of the debate in 2026
The concept of the digital twin isn’t new. What has changed in recent years—and accelerated in 2026—is the availability of concrete tools to implement it without having to reinvent the infrastructure from scratch.
Three factors are making the digital twin increasingly relevant for Italian manufacturing:
The maturity of machine interconnectivity
More and more factories today have connected machines that transmit data in real time. Interconnectivity—a fundamental prerequisite for any digital twin project—is no longer an exception, but a practical and achievable option, even for older machinery, thanks to IoT gateways and standard protocols like OPC-UA and MQTT.
Competitive pressure on response times
In a context where lead times, demand variability, and product complexity are constantly increasing, the ability to simulate production scenarios in advance has become a measurable competitive advantage. Those with a digital twin can respond to an urgent customer request with data. Those without it rely on the experience of the department manager.
The connection with Transition 5.0
The Italian regulatory framework has placed energy efficiency at the center of incentives. A digital twin that integrates energy consumption data per unit produced is not just an operations tool: it’s also a lever to access the benefits of the Transition 5.0 plan, which rewards documented and measurable improvements in energy performance.
The building blocks of the digital twin: what you already have and what you’re missing
One of the most common mistakes is thinking of the digital twin as something you buy. In reality, it is built—often starting from existing infrastructure. Let’s look at the fundamental building blocks:
MES and production progress
If you have an MES software—or even just a digital production tracking system—you already have the informational core of your digital twin. Data on orders, cycle times, process completions, and machine stoppages are the raw materials that feed the digital twin.
Machine interconnectivity and IoT
The data generated directly by machines—cycles, temperatures, pressures, vibrations, consumption—represents the physical layer of the digital twin. Without it, you can have a management model, but not a true twin of the factory. This is why machine interconnectivity is considered a fundamental prerequisite for any advanced digitalization project.
Enterprise Asset Management (EAM)
The lifecycle of assets (machines, molds, equipment) is an integral part of a factory digital twin. Knowing how many cycles a mold has completed, when the last maintenance was performed, and the efficiency curve over time—that is, having an Asset Management platform—helps build a digital twin that reflects not only what the factory produces but also the conditions under which it operates.
Advanced Planning (APS)
A digital twin without planning is like having a dashboard without a steering wheel. The ability to simulate the impact of a change in the production schedule before executing it is one of the most immediate and high-value use cases of the digital twin. APS software thus becomes not just a scheduling tool, but a factory simulation engine.
Three concrete use cases in the factory
Let’s move from theory to practice. Here are three scenarios where the digital twin generates measurable value in a real manufacturing context.
Case 1 — Simulating the bottleneck before it happens
A production manager receives an urgent order that requires doubling the output on a specific line. With a digital twin, they can simulate the impact on the entire production chain: which machine will reach capacity first? Which stage will create queues? Where will stoppages concentrate?
Without a digital twin, this analysis requires hours of meetings, Excel sheets, and the historical knowledge of department managers. With a digital twin, it’s a simulation that takes just minutes.
Case 2 — Predictive maintenance driven by usage data
By monitoring a machine’s operational parameters in real time—such as vibrations, temperature, and cycle count—the digital twin can identify abnormal patterns that signal a potential failure. The difference between reactive maintenance (after a failure) and predictive maintenance (before it happens) translates directly into increased production uptime and reduced extraordinary repair costs.
This is one of the most immediate use cases: the data already exists (coming from sensors), the model is relatively simple (a baseline of normal operation), and the value is immediate and measurable.
Case 3 — Energy optimization per unit produced
With a digital twin that integrates production and energy consumption data, it becomes possible to answer a simple but powerful question: how much energy do I consume to produce a single unit on this machine, at this rate, with this operator?
This analysis, impossible without a digital twin, allows you to identify the most efficient production configurations, reduce energy waste, and, where applicable, document the improvements required by the Transition 5.0 incentives.
How to build it in practice: a step-by-step approach
There isn’t a single path to the digital twin. But there is a pragmatic approach that works in Italian manufacturing environments, often characterized by heterogeneous machinery, established processes, and resistance to change.
Step 1 — Digital Assessment
Before building any model, it’s necessary to take an honest inventory of what exists: which machines generate data, in what format, and at what frequency. Which systems are already in place (ERP, MES, CMMS)? Where are the most critical gaps? The digital assessment is the foundation on which everything else rests.
Step 2 — Connect and Integrate
The second step is to build the data flow: connect machines that aren’t yet linked, make existing systems communicate, and create a unified data layer that feeds the model. It doesn’t need to be done all at once—starting with a single department or a pilot line is a valid starting point.
Step 3 — Model and Visualize
Using the available data, the digital model is built: operational dashboards, real-time KPIs, and representations of the plant’s status. At this stage, the digital twin begins to be visible and usable by operators and production managers.
Step 4 — Simulate and Decide
This is the most mature level. The digital twin doesn’t just reflect reality; it interrogates it. “What happens if I move this order? If this machine stops? If I add a shift?” The ability to answer these questions with data, rather than intuition, is the ultimate value of the digital twin.
Where to start?
If you’re reading this article and think that the digital twin is only for large multinationals, you’re mistaken. The right question isn’t whether your company is big enough—it’s whether you already have production data that you’re not fully leveraging.
If your machines generate data, if you have an MES or a production tracking system, and if you monitor your assets—you already have the building blocks of your digital twin. The next step is to build the model that brings them together.
Do you want to understand where to start in your specific context? Let’s begin with an assessment together: we’ll analyze your machinery, existing systems, and information gaps. Then we’ll create a tailored roadmap.