Digital Twin and Digital Factory Compared: Definitions and Applications in Production Systems

If the digital twin is a modelling and simulation tool, the digital factory represents a systemic concept.

From a technical perspective, it can be interpreted as the digital architecture of the factory, integrating design, simulation, operational data and lifecycle management of the production system.

In some authoritative interpretations, the digital factory is described as a shared digital model of the entire factory, capable of integrating data related to structure, assets, processes and performance throughout the lifecycle of the plant.

In this lifecycle-oriented perspective, the digital factory architecture develops across the phases of planning, design, construction and operation (Plan, Design, Build, Operate), a framework also adopted in recent industrial initiatives dedicated to the digital transformation of manufacturing.

The digital factory therefore does not correspond to a single technology, but to an integrated architecture in which:

• machines, people and information systems are interconnected
• data are collected, orchestrated and distributed throughout the production flow
• planning, execution and control operate in digital continuity
• plant design and management rely on a shared data environment

This architecture may include:

• ERP and MES systems
• industrial automation
• IIoT connectivity
• structured production data management
• simulation tools and digital twins

In summary:

The digital twin is an advanced model.
The digital factory is an integrated organizational and technological strategy.

Smart factory: cyber-physical system and operational adaptability

The term smart factory emerged in the early 2010s with the rise of the Industry 4.0 paradigm, to describe a cyber-physical production system characterized by:

• extensive interconnection between machines, sensors and information systems
• real-time data exchange
• vertical and horizontal integration of information flows
• adaptive capabilities based on analytics and advanced algorithms

From a functional perspective, the smart factory prioritizes real-time operational optimization: it monitors the current state of the production system and adjusts operating parameters based on the available data.

It does not necessarily imply a structured predictive simulation component. When present, this capability is typically implemented through a digital twin, which can be integrated into the overall architecture to extend analysis from managing the present state to evaluating future scenarios.

In this interpretation, the smart factory represents the adaptive operational layer of the digitalized production system.

(Example sources: Hermann, Pentek, Otto, 2016; NIST – Smart Manufacturing definitions)

It is therefore useful to distinguish the three concepts on a functional and structural basis, highlighting their different areas of application and objectives.

Summary framework – conceptual level

Concept	Main Function	Focus	Objective
Digital Twin	Predictive modelling and simulation	Specific asset, production line or process	Validate future scenarios and optimize before real execution
Smart Factory	Real-time operational adaptability	Interconnected production system	Optimize current operations based on available data
Digital Factory	Lifecycle integration and governance	Overall architecture (Plan–Design–Build–Operate)	Orchestrate operational and modelling components coherently

---

Strategic implications for manufacturing companies

A conceptual overlap between digital twin and digital factory can affect design and investment decisions. For example:

• considering a 3D CAD layout model, even if animated, as a digital twin in the absence of process simulation
• investing in interconnected digital infrastructures without including tools for modelling and preventive validation
• developing advanced simulation models only during the design phase, without connecting them to real data and operational systems, making continuous improvement and progressive integration across the factory lifecycle less effective

An example of an evolutionary roadmap

A coherent transformation can follow a structured path:

Step	Objective	Enabling Tool	Result
1	Model the system	3D simulation	Validation of layout and flows
2	Test control logic	Virtual commissioning	Reduced start-up risks
3	Integrate real data	IIoT connectivity	Transition toward digital twin
4	Continuous improvement	Predictive simulation	KPI improvement
5	Integrate into the enterprise system	Digital factory architecture	Full governance

The role of simulation

Within the evolutionary path described above, industrial simulation of production processes represents the first structured level of digital maturity: it is the starting point that allows the production system to be modeled even before it is interconnected or made operationally adaptive.

Through simulation, it becomes possible to validate configurations, test alternative scenarios and verify operational logic without impacting the real system. It is in this phase that the foundations are built for a truly predictive digital twin and for its coherent integration within the architecture of the digital factory.

---

FAQ

Frequently asked questions about digital twin and digital factory

Is the digital twin part of the digital factory?
In many advanced architectures, the digital twin represents a modelling component that can be integrated within the digital factory. However, it remains distinct in scale, scope and operational purpose.

What is the difference between smart factory and digital factory?
A smart factory describes an interconnected production system capable of adapting operations based on data collected in real time. A digital factory, on the other hand, represents a broader architectural model that integrates design, data, systems and processes throughout the entire lifecycle of the plant.

Does a digital factory necessarily require a digital twin?
A digital factory can be implemented as an integrated architecture of data, systems and processes even without a structured predictive modelling component. However, integrating a digital twin extends its capabilities toward preventive validation and the analysis of future scenarios.

---