# A systemic-oriented architecture of a Digital Twin for an industrial production system

Vincent Cheutet, Xeniya Pystina, Aicha Seklouli-Sekhari, Lilia Gzara

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Vincent Cheutet, Xeniya Pystina, Aicha Seklouli-Sekhari, Lilia Gzara. A systemic-oriented architecture of a Digital Twin for an industrial production system. SOHOMA2025 15th International Workshop on Service-Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, Oct 2025, Aix-en-Provence, France. hal-05391703

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# A systemic-oriented architecture of a Digital Twin for an industrial production system

Vincent CHEUTET, Xeniya PYSTINA, Aicha SEKHARI, Lilia GZARA

## Abstract

A Digital Twin (DT) of an Industrial Production System (IPS) can be seen as a digital replica of the industrial system (referred hereafter as the Physical Twin - PT) designed to support the decisions of a stakeholder. The DT concept has gained a strong interest in both the scientific and the industrial communities. Nevertheless, the large diversity of definitions and propositions that can be found in the literature demonstrates the lack of maturity on this subject. In particular, an aspect is sparsely tackled: the information flows existing inside the DT, with the synchronisation issues associated. This paper proposes a conceptual architecture with a strong information system orientation in order to tackle this issue and illustrates the concepts with an implementation on pedagogical production system.

## 1 Introduction

Growing complexity of Industrial Production Systems (IPS) requires more efficient and reactive control for the different stakeholders. In the current "Industry 4.0" dynamic, the approach based on Digital Twins (DT) is known to meet a need for control as close as possible to the system and thanks to the integration of simulation and artificial intelligence, also being able to better predict behaviour. A DT may be seen as an extension of the digital model of a product or a system in the usage phase and had been applied in various domains [13]. Nevertheless, Melesse et al. [10] present challenges that are still open for the design and deployment of DT on production systems, including: difficulties in building, understanding and controlling systems with precise multi-scale models and reliable, the lack of knowledge and methodol-

<footnote>Vincent CHEUTET, Xeniya PYSTINA, Aicha SEKHARI, Lilia GZARA
INSA Lyon, Université Lumière Lyon 2, Université Claude Bernard Lyon 1, Université Jean Monnet Saint-Etienne, DISP UR4570, Villeurbanne, 69621, France, e-mail: vincent.cheutet@insa-lyon.fr</footnote>
ogy on these models, the difficulty of predicting the behaviour of complex systems, the difficulty of accessing the relevant data, the lack of synchronisation between the physical and digital worlds and the difficulty of maintaining the models and check and test their validity.

Among these challenges, the notion of synchronisation between the digital world and the physical one is central, with different objectives: it can be real-time to ensure a control of the physical twin by the DT, or long-term to maintain and update each twin. This opens one main question: what means synchronisation in a DT? And to answer this question, it implies to have a clear definition of what is a DT, what is its architecture and what are the different (mainly data) flows inside. The contribution of this work is **the definition of a conceptual IPS-DT architecture that highlights the internal data flows and the synchronisation process inside the DT consistently with the decisions taken and the physical system.**

The outline is as as follows. Section 2 analyses the state of art on DT, the associated architecture. Section 3 gives details on our proposed architecture of an IPS-DT. An illustration example is detailed in section 4. Section 5 concludes and opens some research perspectives.

# 2 State of the Art on DT architecture and synchronisation

## 2.1 DT definition

The evolution of DT definition provided by Errandonea et al. [4] has gone from asset simulation and virtual replica to an idea of multi-technological environment or platform converging towards physical object and virtual space [14, 6]. Since numerous definitions of DT exist in the literature without a clear consensus, we will use here the following definition: “A set of adaptive models that emulate the behaviour of a physical system in a virtual system getting real time data to update itself along its life cycle. The DT replicates the physical system to predict failures and opportunities for changing, to prescribe real time actions for optimising and/or mitigating unexpected events observing and evaluating the operating profile system”[14]. In a complementary manner, Krintzinger et al. [7] defines a DT as a digital system with an automatic data flow both from and to a PT (Fig. 1). In our view, a digital twin can be seen as the fusion of an information system and a simulation model. This concept makes the information system more dynamic, as it not only captures the past and present states of the physical system, but also predicts its future states through simulation.

Contrarily to Kritzinger’s taxonomy [7], and since we want a DT for operational decisions – with a time range between minutes and weeks – we decide to include the human decision maker in the decision loop. When including a human decision maker inside the decision loop, we aim at not only providing a framework making explicit all information needed by the decision maker to make his/her decision (such
<figure><img src="image_2.png" /><figcaption>Fig. 1: DT taxonomy proposed by [7]</figcaption></figure>

a situation corresponds to a Digital Shadow), but also at capturing the complete decision inside the system PT+DT (see [11] for more details).

With the chosen definition of DT, it is so important to better understand what is an IS in this context. Research works in the domain of system engineering has helped to identify the model that forms the basis of most current information system approaches [8]. This model distinguishes, in one organisation, three sub-systems (Fig. 2):

* the **operative system** is composed of all resources relative to the organisation activity,

* the **decision system** encompasses all elements responsible of the management and the control of the organisation and its resources,

* the **information system** is seen as the communication medium between the operative system and the decision one.

<figure><img src="image_3.png" /><figcaption>Fig. 2: The information system in between the operative one and the decision one (adapted from [8]).</figcaption></figure>

The main purpose of the information system, in this context, is to provide each actor of the organisation with all the information about its current or past situation.
It aims to provide a coherent view of the information representing the state of the
operative system. A system is then defined as the set of entities (**topological struc-
ture**) in dynamic interactions (**behaviours**) organised according to a goal.

## 2.2 DT architecture

Among the literature, one can find numerous DT architectures, with some papers trying to review them [14, 13]. When performing a deep analysis, one can verify that all the DT architectures referenced in these two papers are fully compliant with the Kritzinger's taxonomy and so defined automatic flows of data between DT and PT. This positioning is not consistent with our point of view on an IPS, with a clear integration of Human in the decision loop.

In a domain close to DT paradigm, Cyber Physical Production Systems (CPPS) are systems of systems of autonomous and cooperative elements connecting with each other in situation dependent ways, on and across all levels of production, from processes through machines up to production and logistics networks, enhancing decision making processes in real time, response to unforeseen conditions and evolution along time [2]. From all existing CPPS architectures, the one from Cardin et al. [3] illustrates the positioning between the physical part and the cyber (i.e. digital) part, with a clear integration of the human in the decision loop (Fig. 3).

<figure><img src="image_4.png" /><figcaption>Fig. 3: CPPS architecture proposed by Cardin et al. [3]</figcaption></figure>
## 2.3 DT flow synchronisation

Tao et al. [15] highlights that a crucial component of applying DT is its capacity for co-evolution and synchronisation between the physical and virtual realms. In the literature, DT synchronisation covers two different needs:

1. the co-evolution of the physical and digital twins to ensure the global consistency between the two faces of the mirror (see [16] for instance)

2. the “real-time” synchronised exchange of data between the two twins

This research work focuses on the second point.

Most of the literature do not precise the different protocols to ensure these synchronised data exchanges. Zipper et al. [17] suggests a method that applies optimisation techniques to synchronise the states of the digital twin with the physical asset during production time and at the same time supports detecting changes and identifying their source. Gehrmann et al. [5] discusses how a digital twin replication model and corresponding security architecture can be used to allow data sharing and control of security-critical processes. Liang et al. [9] discusses the development of a system to bridge robot simulations and physical robots in construction and digital fabrication. Abdoune et al. [1] gives details on the synchronisation mechanism between the physical system and the DT emulation model.

All these research works consider the DT without a Human in the loop and so are closer to the control theory. It implies that the everything should be real-time and with the same internal dynamics, in order to have the best feedback. This position is not consistent with our needs and this is why we need to explore other directions.

# 3 A systemic-oriented DT architecture

Based on the analysis made in the previous section (and in particular the definition of DT according to IS), we propose the conceptual architecture of the an IPS with a DT (Fig. 5), with - for comparison - the conceptual architecture of an IPS without a DT (Fig. 4). This architecture relies on the three layers described in Fig. 2.

## 3.1 Operative layer

With the given definition of the operative layer, we decide to define one operative block for the physical asset and as many operative blocks as different digital behavioural models we want to implement. In fact, since the controls (and so decisions) can be multiple, the behavioural blocks (that can be either simulation model, artificial intelligence algorithms, etc.) can be multiple, possibly launched in parallel and/or in sequence. For all synchronisation and consistency issues that have been
<figure><img src="image_5.png" /><figcaption>Fig. 4: Physical asset architecture decomposition on the three layers</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5: IPS-DT detailed architecture</figcaption></figure>

raised in the previous section, these operative blocks are not directly connected to-
gether but they are all linked to the information layer through specific connection
interfaces.
## 3.2 Information layer

DT paradigm enhances the IPS control but this control should continue to exist without the DT or at least with not the same synchronicity (with for instance a speed up of the digital system for prediction). In the proposed IPS-DT architecture, the information layer is so doubled on the physical system and the digital one, with a strong synchronisation required between them. This synchronisation (type and frequency) is controlled accordingly to the decision layer (see next section for more details).

The first stage of this layer is the model definition phase, knowing that not one model is sufficient due to the diversity of operative behaviours and the multiplicity of decisions. The second stage of this layer is the data management. By abuse of language, we will use here the terms data and information as synonyms, since all data in the system are given with context. Two types of data in this layer exist:

* **"static" data** (grey block in Fig. 5), representing the knowledge on the IPS (Bill-Of-Material, etc.) and coming from Enterprise Information Systems (EIS) like ERP;

* **"dynamic" data** (light blue block in Fig. 5), representing the dynamics of the system (manufacturing order, data from sensors, etc.) and coming either from EIS or directly from the operative layer.

Derived from these models, data lifecycle is important: capture (with sensors), storage, update, treatment. And this for all the different operative blocks.

## 3.3 Decision layer

We consider here a twinning between the physical asset and some digital models, it so implies to have a unique decision-support interface for the decision-taker. As already stated, we define a DT as decision-support, it implies and requires a visualisation module and a comparison module between the different behavioural blocks. Decisions taken have two directions:

* towards the physical asset, e.g. reconfiguration

* towards the digital asset, e.g. Design of Experiment definition

Three modes exist to support the decision, that have impact on the synchronisation (see [11] for more details):

* **Digital Shadow**: In this mode, only transitions triggered by external events are activated in the digital system. Therefore, it can be seen as only a digital dashboard as it only displays the current state of the physical system to the human decision maker.

* **Exploratory DT**: This mode only enables transitions triggered by internal events. This mode can be seen as a pure simulation-based dashboard with an
initialisation driven by the current state of the physical: the internal transitions simulate the future of the physical system in accelerated mode in a full de-synchronisation with the physical system. This allows the decision maker to explore the variability of possible futures with the help of simulations.

* **Synchronised DT:** Transitions triggered by both transition types are enabled in this mode of the digital system. Consequently, the internal events cause it to simulate in accelerated a possible future of the physical system, until an external event re-synchronises the digital system on the present state of the physical system, then the internal events start again to simulate the future in accelerated starting from the new present state of the physical system until the arrival of the next external event and so on. In summary, a large number of internal events simulate the future between two external events and these external events re-synchronise the digital system on the physical.

In synthesis, the “Digital Shadow” mode mainly corresponds to a **digital dashboard automatically fed with data** from PT. The “Exploratory DT” mode is used to perform “What-If” simulations offline and proposes decision support to decision makers; it is thus a **classical decision-support tool based on simulations automatically initialised with “real data”**. With this synchronisation, The “Synchronised DT” mode can be seen as a synthesis of the two other modes, i.e., a **digital dashboard fed with both real-time data and results of simulations**, allowing the decision maker to have a better insight into the potential futures.

# 4 Illustration on a use case

This section gives details on how we deploy the proposed architecture on a pedagogical automated assembly line, hosted by the S.Mart RAO platform (Fig. 6).

## 4.1 Assembly line presentation

The overall objective of the line is to assemble tokens on a pallet according to a shape chosen by the operator from a predefined selection. The assembly line consists of a central conveyor delivering pallet carriers to six separate stations, each with its own specific purpose.

More specifically, the operator interacts with the interface at Station 7 to configure the product type and define the required quantity. Once the configuration is validated, the corresponding command is transmitted to Station 2, where the product type and the associated sequence of operations are encoded onto the pallet's track tag via RFID reader/writer 1. If no prior markings are detected on the tag, the pallet proceeds from Station 2 to Station 6. Throughout its circulation, RFID sensors (RFID 2) continuously read the information stored on the track tag and communicate it to the control system, which dynamically adjusts conveyor routing based on the
<figure><img src="image_7.png" /><figcaption>Fig. 6: S.Mart RAO Academic Technological Platform Industry 4.0</figcaption></figure>

encoded instructions. After the completion of each operation at a station, the con-
trol program increments the operation index to identify the next processing station,
updating the tag accordingly through RFID 1. At Station 6, the pallet is loaded with
tokens according to the combination specified on its tag. The product type, however,
cannot be modified directly at any station once the process has started. After token
insertion, the pallet moves to Station 1, where a color camera verifies the assembly
against the production order. If the image is validated by the control program, the
pallet advances to Station 4, where the robot removes the tokens. In case of valida-
tion failure, the pallet is redirected to Station 3 for manual inspection. At Station 5, a
monochrome camera captures an image to confirm whether the pallet is empty after
the robotic operation at Station 4. If the verification is successful, the pallet returns
to Station 2, where it is temporarily stored until conveyor space becomes available
or a new production cycle is initiated. Otherwise, the pallet continues circulating in
the central loop until validation conditions are met.

To this end, the line is constituted by two industrial robots (station 6 for the
assembly and station 4 for the disassembly) and a set of conveyors, all equipped with
PLC systems connected together within the OPC server. Moreover, the production
line is equipped with position detecting sensors that send information to the server
detecting the presence of pallet during its movement.

The use case described hereafter focuses on station 6, which is composed of:

* an industrial robot that takes tokens from a stock and put them on the pallet at the right place (according to the sequence chosen by the operator);

* a conveyor local to the station;

* several actuators, with one that derives the pallet from the principal conveyor to the station one and an other one that blocks the pallet at the centre of the station;
* several sensors: a RFID sensor at the station entry that check if the pallet has to go on the station, a RFID sensor that check the track tag, a sensor to check the stock level of tokens

More specifically, several scenarios are defined where the line responsible can choose between the rescheduling of tasks at this station and/or the replenishment of equipment stocks in the event of a shortage of tokens, in order to avoid blocking the station while waiting for stocks to be replenished or manufacturing a faulty product.

## 4.2 DT architecture instantiation

Fig. 7 illustrates the chosen instantiation of a DT on the station 6 precisely<fnref n="1" />.

<figure><img src="image_8.png" /><figcaption>Fig. 7: Instantiating the proposed conceptual architecture of the platform.</figcaption></figure>

On the line, all sensors and actuators described in the previous section are connected through OPC-UA protocol to the central digital station, in which we implement two Asset Administration Shells (AAS) with almost the same models inside: one for the Physical Twin (PT-AAS) and the other for the Digital Twin (DT-AAS). The DT-AAS stores more data coming from the DT operative layer, i.e. the results

<footnote n="1">¹ A demo video of the DT is available here: https://www.intranet.disp-lab.fr/s/WwF2if7BigaNkad</footnote>
of the behavioural model (see hereafter for more details). A synchronisation algo-
rithm has been implemented to push all data from the PT-AAS to the DT-AAS when
requested.

On the DT operative layer, we develop a simple material consumption algorithm,
allowing to anticipate the risk of material lack on the station. It requires from the
DT-AAS data concerning the levels of raw material stocks and from the ERP (or
from the DT-AAS) the production planning and the Bill-Of-Materials (BOM) for
each product. This algorithm will calculate the new level of token stocks and the
optimised production scheduling with the associated level of performance, data that
will be stored in the DT-AAS.

At the interactive display, an algorithm as been developed to show the initial
production planning and the optimised one, with all performance level associated.
Based on these information and his/her knowledge, the line responsible can decide
to re-route the pallet (i.e. to postpone the operation), to handle tokens for stock
re-completion or to do nothing. The line responsible select the decision on the inter-
active display, so that the actuator that derives the pallet can be activated or not in
case of he/she has decided to postpone the operation for instance. He/she can also
decide to launch other scenarios simulation by modifying some parameters, like the
initial production planning.

5 Conclusion

Digital twins for an industrial production system are seen as a promising approach to control as close as possible to the system and also to better anticipate behaviour. Nevertheless, if the literature is important both in the scientific and industrial communities, several issues are still open and in particular the synchronisation between the physical asset and its digital twin. In this work, we propose a conceptual systemic-oriented architecture of DT allowing to distinguish the core components attached to the most important layers according to system modelling: the operative layer, the information one and the decision one. This architecture allows us to define recommendations on the synchronisation needs according to the internal DT dynamics. This proposition is illustrated on a pedagogical automated assembly line, allowing us to validate its completeness and its consistency.

Nevertheless, the architecture remains conceptual and some research works are required to precisely define implementation guidelines towards a logical architecture and the physical one. Ongoing works are focusing on this aspect [12]. Moreover, this example is purely didactic and an application on a more complex system is required to fully test and validate this proposition. Finally, this work only focuses on internal dynamics as synchronisation and a very important aspect to increase trust in this DT is to ensure consistency checks and validation in between the two twins along time.
## Acknowledgments

This research was funded by l'Agence Nationale de la Recherche (ANR) for the Digital Twins for Cyber-Physical Systems project (ANR-23-CE10-0010-01).

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