# Digital Twins along the product lifecycle: A systematic literature review of applications in manufacturing

Guillaume Pronost, Frédérique Mayer, Mauricio Camargo, Laurent Dupont

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Guillaume Pronost, Frédérique Mayer, Mauricio Camargo, Laurent Dupont. Digital Twins along the product lifecycle: A systematic literature review of applications in manufacturing. Digital Twin, 2024, 3, pp.3. 10.12688/digitaltwin.17807.2. hal-04715711

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SYSTEMATIC REVIEW

# Digital Twins along the product lifecycle: A systematic literature review of applications in manufacturing [version 1; peer review: awaiting peer review]

Guillaume Pronost ID, Frédérique Mayer, Mauricio Camargo, Laurent Dupont

Équipe de Recherche sur les Processus Innovatifs, Université de Lorraine, Nancy, France

**V1** First published: 21 Mar 2023, 3:3
https://doi.org/10.12688/digitaltwin.17807.1

Latest published: 21 Mar 2023, 3:3
https://doi.org/10.12688/digitaltwin.17807.1

## Abstract

**Background:** The evolution of product expectations in the era of mass customization implies an improvement and a better control of individualized creation and production processes throughout the product lifecycle. The application of the digital twin (DT) seems to be a favoured solution in this context, but its study during the lifecycle of a product has only been partially evoked in the literature.

**Methods:** The purpose of this state-of-the-art article is to identify current trends of applications of the digital twin concept in the literature under two main dimensions: its nature regarding the interaction structure between digital and physical objects (defined as: Pre-Digital Twin, digital model, digital shadow, and digital twin), but also its applications, along the product lifecycle (Design, Production, Operation, Disposal). To achieve this analysis a systematic literature review was carried out. The publications selection was based on the presence in this of a case of application of a digital twin with a focus in the Manufacturing sector. Within this review, 188 scientific papers were compiled and analyzed.

**Results:** Results showed that although the term digital twin is widely used, the deployment of DT technologies in manufacturing is still at an early stage as most of the digital twin applications were in fact prototypes focused on the real-time observability of the physical system, either for optimization or predictive maintenance. Moreover, regarding the product lifecycle, most of the applications have been focused on the production and operational phases whereas those at the design and disposal phases are still limited.

**Conclusions:** This paper presents an original approach to the study of digital twins, not focusing on a single application area or lifecycle phase but which aims to establish future perspectives on the use of digital twins along the lifecycle.

## Keywords

Digital Twin, Digital Shadow, Product Lifecycle, Literature review, Manufacturing

## Open Peer Review

**Approval Status** AWAITING PEER REVIEW

Any reports and responses or comments on the article can be found at the end of the article.
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**Corresponding author:** Guillaume Pronost (guillaume.pronost@univ-lorraine.fr)

**Author roles:** **Pronost G:** Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation; **Mayer F:** Conceptualization, Investigation, Methodology, Writing – Review & Editing; **Camargo M:** Conceptualization, Investigation, Methodology, Writing – Review & Editing; **Dupont L:** Investigation, Writing – Review & Editing

**Competing interests:** No competing interests were disclosed.

**Grant information:** The work reported in this paper has been undertaken and funded as part of the H2020 INEDIT project (Project Grant Agreement N°869952).

*The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.*

**Copyright:** © 2023 Pronost G et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**How to cite this article:** Pronost G, Mayer F, Camargo M and Dupont L. Digital Twins along the product lifecycle: A systematic literature review of applications in manufacturing [version 1; peer review: awaiting peer review] Digital Twin 2023, 3:3 https://doi.org/10.12688/digitaltwin.17807.1

**First published:** 21 Mar 2023, 3:3 https://doi.org/10.12688/digitaltwin.17807.1
## Introduction

The development of digital tools has triggered an evolution of modern production means and methods in recent years. With the development of digital models, the different stakeholders (designers, manufacturers, users, ...) of the product creation process (design, manufacturing) can communicate around a common object, acquire and share information, and manage products more precisely during every phase of their lifecycles. This digitization is thus seen as an opportunity to achieve higher levels of productivity and effectiveness. At the heart of this Industry 4.0 is an emerging technology for the operation of technical systems: digital twins¹. The emergence of digital twins (DT) enriches the application of these models in several areas, and it is beneficial to enhance the traditional product design and development processes². Although the technical and conceptual basis leading to digital twins were established since several decades ago, the concept of DT was first introduced in 2002 by Dr Michael Grieves from the University of Michigan and it was called the “Conceptual Ideal for product lifecycle Management (PLM)” representing the linkage between real space and virtual space. A DT is a digital representation of a physical asset, linked with the physical counterpart by a flow of data enabling the real-time update of the digital model. DTs play a role in monitoring, controlling, and predicting a product’s behavior at all stages of its lifecycle from design through to end-of-life.

The general problem that motivates our work stems from the need to evolve the design-production chain to meet the growing demand for product individualization, which characterizes the specific needs of each customer for one or more products. The convergence between design and production for mass customization leads to a need for an observation and improvement of the complete product lifecycle. In this context, it is relevant to observe the application of digital twins and their nature throughout this life cycle.

However, the existing literature is mainly interested in the application of the digital twin to improve a single phase of this life cycle. Different authors tackle the use of digital twins for the design phase, such as Tao et al.³ or Lo et al.⁴, for the production phase, such as Cimino et al.⁵, or Errandonea et al.⁶ for the use phase of the product. These papers focus on a specific phase of the product’s life in their analysis, which allows for an accurate visualization of the state-of-the-art and the use of digital twins during that phase, but makes their study isolated from any visualization of the role of the digital twin throughout the entire life cycle. This hermetic segmentation of the study of digital twins leads to the production of as many studies as there are domains or moments of application, which results in a multiplication of contradictory definitions of the nature of a digital twin.

For this reason, it was decided to carry out a systematic review of the literature, including all cases of applications of the digital twin throughout the product lifecycle. This review will observe, using classifications from the literature and previous works, the nature and functions of digital twins, in the context of the Manufacturing sector. In this context, this work is inspired

by the analysis carried out by Semeraro et al.⁷ observing the distribution of application areas, life cycle phases, and applications within each phase. Following on from the work of Liu et al.⁸ and Semeraro et al.⁷ which also analyzed the distribution of DT-related studies along the product lifecycle to determine the different possible applications in each phase of this cycle, this study will also focus on the nature of the digital twin, i.e. the interaction structure between digital and physical objects.

The remainder of this paper is structured as follows. We begin by reviewing the different uses of digital twins for each phase of a product lifecycle in the literature will be realized, along with a classification of the different existing subcategories of digital twins. Subsequently, we describe the methodology of the literature review, presenting first the research questions and hypotheses, and then the protocol used for the review. Then, a detailed view of the most relevant results is presented followed by a discussion on the implications of the main findings to answer the research questions set in the previous section. Lastly, a conclusion establishes future perspectives on the use of digital twins along the lifecycle.

## Existing theories and previous work

### Definition of digital twins: from aerospace to engineering.

The first formal definition of a digital twin can be attributed to Glaessgen et Stargel⁹, in which the authors define the roadmap of the use of digital twins in aeronautical and aerospace applications for the US Air Force and NASA.

The need to use a digital twin (DT) is justified by the authors because of several issues. Indeed, due to the evolution of technologies and the rapid complexification of systems and failures modes, it has become necessary to go beyond the current empirical and heuristic design rules. In addition, the design of a physical system would require a series of tests in extreme conditions, which would be impossible to reproduce on earth on a physical prototype, because of the cost of recreating such conditions, along with the cost of the destruction of the prototype in each experiment. Due to these practical and economical constraints, it is necessary to realize these series of experiments on a digital counterpart by emulation. Moreover, the vehicles produced are likely, during their phase of use, to encounter unpredictable conditions and situations. Because of that, it was necessary to develop revolutionary approaches for the verification and validation of the models, simulations, and systems used. Finally, the modern high-performance systems used in aeronautics and aerospace require real-time management of its complex materials, structures, and subsystems.

To meet these criteria, they define the nature and composition of a digital twin: "If various best-physics (i.e. the most accurate, physically realistic, and robust) models can be integrated with one another and with on-board sensor suites, they will form a basis for certification of vehicles by simulation and for real-time, continuous health management of those vehicles during their missions. They will form the foundation of a digital twin digital twin"⁹.
Thus, they introduce the widely used definition of a digital twin: "an integrated multi-physics, multiscale, probabilistic simulation of a complex product and which uses the best available physical models, sensor updates, etc... to mirror the life of its corresponding twin." Although this definition is admitted in literature as a basis for many articles on the subject of digital twins, it presents inaccuracies, which results in multiple definitions of the digital twin throughout the literature, creating confusion as to the meaning of this term on this subject in literature.

As a more general definition, a DT can be defined as a virtual representation of an object or system, provided in real time by data coming from its physical counterpart, allowing it to represent it accurately over time. It can be defined as a set of three parts:

1. A model of an object.

2. An evolving set of data relating to the object, also called a digital thread.

3. A means of dynamically updating or adjusting the model in accordance with the data.

Since its first formal definition in 2012 in the context of aeronautical vehicles, the digital twin has become a research topic whose interest grows exponentially in the literature, driven by the development of Industry 4.0, of which it is one of the branches. This growing interest has led to the democratization of the digital twin in many research domains such as medicine¹⁰, smart cities¹¹, manufacturing¹¹,¹², aerospace¹³ or construction¹⁴.

After reviewing publications presenting applications cases of digital twins, and taking into consideration existing application domains classification¹⁵, a classification of the different domains of application of digital twins was established:

1. Manufacturing Industry.

2. Construction / Civil Infrastructure.

3. Healthcare.

4. Energy Systems.

5. Other Application Domain.

This classification will be used later in this paper to describe the current state of these applications domains.

## Digital twins along the product lifecycle

Considering the life of a product, we established, based on the literature⁴,⁷,⁸ that its lifecycle can be divided into four distinct successive phases:

1. System creation/Design phase.

2. Manufacturing/Production phase.

3. Operational/Use phase.

4. Disposal/End-of-life.

According to Lo et al.⁴, although studies presenting applications of DTs exist in each phase of the product lifecycle, a majority (64%) of these studies focus on the production phase of the product lifecycle.

In order to further classify the uses of the digital twin during each of these phases, an exhaustive list of these applications is proposed for each phase. This list relies on the work of Liu et al.⁸, who proposed a classification of applications based in a systematic review of the industrial applications of digital twins along the product lifecycle, as presented in Table 1. In the context of this study, Liu's classification will be used as a first step, to identify the proportion of each application of digital twins in the current literature. Afterwards, the aim is to observe for each of these applications, the typology of digital twins that has been used.

Moreover, the digital twin can, depending on the application, be either the twin of the product being created or the twin of the production system creating a product, thus giving different information to the user. As this review focusses on the study of the product lifecycle, we consider the use of the digital twin in the context of the product. Thus, for example the use of a digital twin of a production system for real-time monitoring would fall under the manufacturing/production phase of the product lifecycle if the studied product is manufactured using this production system, but would fall under the operational/use phase of the product lifecycle if the studied product is the production system itself.

As presented here, no applications were identified for the digital twin in the Disposal Phase. This was due to the fact that, this lifecycle phase would require deeper research, as we only identified one paper through our screening mentioning this at the time of the study¹⁶. Due to the appearance of emerging applications in this lifecycle phase, we consider it necessary to include this phase in our review. Another role of this paper was to identify missing applications and, following on from the work of Liu et al, to produce a set of applications across the product lifecycle. These results will be presented in the Discussion section.

## Classification of digital twins regarding their structural functions

The recent development of DT applications and application domains has led to a multiplication of definitions, resulting in difficulties of organization and understanding of the literature, some authors describing any digital model (CAD, etc...) as a digital twin¹, while other authors specify the need for the presence of an associated physical object⁹, or a data flow between these two objects¹⁷.

Thus, before carrying out a systematic review of the literature on digital twins, and in particular their use in design and production, a first exploratory review has been realized to identify the set of technical criteria on the main functions of the digital twin, to propose a systematic classification of digital twins according to these criteria. The realization of this classification framework, based on various approaches to classifying DTs in the
<table><caption>**Table 1. Applications of digital twins along the different phases of the product lifecycle, from Liu et al.⁸.**</caption><thead><tr><th colspan="2">Applications in the design phase</th></tr></thead><tbody><tr><td>Iterative optimization</td><td>The use of digital twins allows for iterative design optimization to improve accuracy and efficiency, product material selection, and reflect changing dynamic parameters. The digital twin is able to predict product or system performance and identify potential problems to indicate the direction of optimization, which traditional simulation may overlook.</td></tr><tr><td>Provide data integrity</td><td>The digital twin collects and analyzes data from the physical space to provide a sufficient basis for decision making in the design phase to each stakeholder, while ensuring that no proprietary information is disclosed.</td></tr><tr><td>Virtual evaluation and verification</td><td>The digital twin consists of a high-fidelity multi-physics digital model of the product and its environment in which it operates, which allows us to verify its behavior in real situations through simulation. The high fidelity of the model within the digital twin also makes it possible to observe unforeseen behaviors of the product, as close to reality as possible.</td></tr></tbody><thead><tr><th colspan="2">Applications in the manufacturing phase</th></tr></thead><tbody><tr><td>Real-time monitoring</td><td>The digital twin can be used as a tool for state monitoring of a product being produced, by comparing the received data with the expected model of the product and updating it.</td></tr><tr><td>Production control</td><td>The digital twin can link the physical system to its virtual counterpart for intelligent real-time control to perform preset operations and react to disturbances.</td></tr><tr><td>Workpiece performance prediction</td><td>The digital twin can predict the performance of a part before it is manufactured, linked to the degradation of machines and tools, or the choice of material, for example. To do this, it uses updated models of physical phenomena related to the manufacturing process, driven by an empirical database.</td></tr><tr><td>Human-robot collaboration and interaction</td><td>By working in a closed-loop manner, the digital twin of a human-robot collaborative work environment supports human-robot task allocation, workstation layout optimization, human ergonomic analysis, and robot program test.</td></tr><tr><td>Process evaluation and optimization</td><td>The quality of products depends on the conditions of the manufacturing process. To gain a better understanding of the working conditions of the machine, a digital twin collects and interprets data from the machine, the process planning, and the models. It then interacts with the manufacturing environment and helps the machine to improve the process performance.</td></tr><tr><td>Asset management</td><td>The digital twin contributes to the visibility and traceability of manufacturing assets, which plays a critical role in improving the shop-floor performance and contribute to better control, maintenance, and planning decisions.</td></tr><tr><td>Production planning</td><td>Due to the increasing disturbances in the manufacturing phase, the need for intelligent dynamic planning has become more prevalent. Through the use of a digital twin, it is possible to provide globally-optimized production planning based on real-time status changes.</td></tr></tbody><thead><tr><th colspan="2">Applications in the operational phase</th></tr></thead><tbody><tr><td>Predictive maintenance</td><td>The digital twin enables a real-time representation of a physical object. This monitoring allows for predictive maintenance. The interventions for repair and maintenance operations can be carried out before these problems become critical, which can represent a considerable saving in time and money.</td></tr><tr><td>Fault detection and diagnosis</td><td>Digital twin fault diagnosis combines both data-based fault diagnosis (which consists of identifying patterns in the data received from the physical system corresponding to a defect, a technical problem, or wear and tear of parts, by comparing it to saved data associated with a similar issue) along with physics-based fault diagnosis to model the behavior of a physical product, to predict both pre-known anomalies and unpredicted anomalies.</td></tr><tr><td>State monitoring</td><td>The digital twin can be used as a tool for state monitoring of system behavior, by comparing the received data with the expected model of the system and updating it. This allows the real system to be observed at any time without the need to measure the information to be acquired directly on the real system. This enables a better observability of the real system.</td></tr><tr><td>Performance prediction</td><td>By having access to the full lifecycle data of a product, along with its model, the digital twin can predict the system's performance and help to optimize it.</td></tr><tr><td>Virtual test</td><td>The digital twin can simulate the behavior of its physical counterpart with great accuracy, enabling virtual testing in a simulated environment.</td></tr></tbody></table>
literature, has been carried out in a previous study¹⁸, to serve as
the basis for the literature review carried out in this paper.

The result of this work is a separation of the digital twin in
four subcategories, presented in Figure 1, based on three
differentiation criteria:

1. Presence of a physical system: the digital model studied may or may not have a physical counterpart of which it serves as a representation.

2. Automatic model update: data collected on the physical system can automatically feed the digital model. Otherwise, the model will only be updated with information from the physical system manually, by user decision.

3. Control of the physical system: information from the execution of the digital model can be automatically fed into the physical system as a command from the physical system. Otherwise, there will be no automated communication from the digital model to its physical counterpart.

Based on these classification criteria, a decision tree, presented in Figure 1, has been created, to be able to determine, when reading a scientific document dealing with “Digital Twins”, the subcategory to which the DT mentioned belongs.

## Methods

### Research positioning

To define the objectives and research questions (RQs) of this paper, an exploratory literature overview was conducted, whose results are presented in the previous section. One of the conclusions of this exploration, as can be seen in the previous section and from which stems the need for a classification of digital twins, the term digital twins can characterize very different virtual objects, as has also been presented by Madni et al.¹⁷ or Kritzinger et al.¹². A first objective of this paper is therefore to study, with the help of the classification presented above, the presence of these different virtual objects grouped under the term “Digital Twin” in the literature, in a continuation of the work of Kritzinger et al.¹².

Another point that could be highlighted is the diversity of application areas of digital twins within the literature. To identify all the domains of application of digital twins in the literature and their distribution, one of the objectives of this paper will be to identify the domain and the type of application.

In addition to a diversity of application domains, in the literature, as presented in the previous section, we find digital twins throughout each of the different phases of the lifecycle of a product, from its ideation and design phase to its use and end-of-life. Nevertheless, few studies deal in a general way with

<figure><img src="image_6.png" /><figcaption>**Figure 1.** Decision tree of the digital twins subcategories, from 1.</figcaption></figure>
the review of the literature of the digital twins all along this lifecycle. An objective of this paper will therefore be to observe the product lifecycle phases where DT technology is applied in the papers from the selected literature. This will allow us to have a representation of the importance of each of these phases in the literature. This paper will focus its attention on studies which include use cases (i.e. the realization of an application of a digital twin technology) displaying the use of DT technology, in particular to observe the proportional occurrence of each of the subcategories of digital twins defined in the previous section in each of the phases of a product's lifecycle.

Given the above considerations and objectives, the following research questions (RQs) are formulated:

* **RQ1:** What are the application areas of DTs in the literature?

* **RQ2:** On which applications of the digital twin is the literature focused?

* **RQ3:** Which subcategory of digital twins are being realized in each product lifecycle phase?

## Applied research methods

A review protocol, based on the work of Siddaway *et al.*¹⁹ and adapted from Cruz Sanchez *et al.*²⁰, is applied to carry out the selection of studies for a systematic literature review about the use of digital twins along the product lifecycle.

The review protocol, presented in Table 2, consists of five steps:

1. **Search strategy:** It defines the type of studies, keywords, search equations, and the database to be considered in the review.

2. **Study selection criteria:** It describes the inclusion and exclusion principles that are useful to subset the retrieved documents.

3. **Study selection procedure:** It describes how the selection criteria is performed. The steps and the features to analyze in order to accept or reject a particular study are defined.

4. **Data extraction strategy:** Defines the information that is extracted from the studies.

<table><caption>**Table 2.** Systematic review protocol for the literature review.</caption><thead><tr><th>Stage</th><th>Principle</th><th>Description</th></tr></thead><tbody><tr><td rowspan="6">Search strategy</td><td>Type of Studies</td><td>Journal papers and conference proceedings</td></tr><tr><td>Keywords</td><td>"Digital Twin", "manufacturing", "design", "PLM"</td></tr><tr><td>Search equation</td><td>(TI=(Digital Twin)) AND ((TS=(lifecycle OR life cycle \ OR life-cycle OR monitor* OR predict* OR maintenance) \ AND TS=(manufactur* OR production OR design)) \ OR TI=(manufactur* OR production OR design)) Field tags: TS=Topic; TI=Title</td></tr><tr><td>Period of time</td><td>2000 - 2021</td></tr><tr><td>Databases</td><td>Web of Science</td></tr><tr><td>Criteria</td><td>1) Articles related to the use of digital twin technology for the creation of a product<br>2) Studies should focus on the realization of an application of the digital twin along the lifecycle of a product</td></tr><tr><td rowspan="4">Study selection</td><td rowspan="4">Procedure</td><td>1) Title, abstract, and keywords are screened<br>2) Introduction section and conclusions were read<br>3) Full article was reviewed<br>4) Selection is made on the quality assessment</td></tr><tr><td>Application area of the digital twin</td></tr><tr><td>Application setting</td></tr><tr><td>Physical counterpart represented by the digital twin</td></tr><tr><td rowspan="2">Data extraction</td><td>Application of the digital twin</td><td>Phases of the product lifecycle considered</td></tr><tr><td>Categorization of the digital twin</td><td>Subcategory of digital twin used</td></tr><tr><td>Quality assessment</td><td>QA</td><td>Does the study present an application of a digital twin in a use case?</td></tr></tbody></table>
5. **Study quality assessment:** Evaluates the pertinence of a study regarding the research question. Quality checklists are used to guide the evaluation of the study.

Data was extracted manually from Web of science database based on the research equation presented Table 2, and was manually analyzed to identify relevant information.

Figure 2 illustrates the research methodology.

## Findings

In this section, the results of the literature review carried out will be presented, before interpretations are made based on these results in the next section.

A total of 188 papers on the subject of digital twins, and which present a practical case study, were selected after multiple screenings. The results are presented in an external repository²¹.

## Application domains of digital twins

As presented in Figure 3, a vast majority of the publications selected for this review were published after 2018. The fact that the emergence of case studies and applications of digital twins is very recent and plentiful enables us to explain the fact that many different definitions and methods co-exist in the scientific community. Moreover, a vast majority of studies (160 papers out of 188) were published in the context of the Manufacturing industry, with the other domains of applications (Construction, Healthcare, Energy systems) being represented marginally.

There is a focus of these studies on the manufacturing domain, as presented in Figure 3. Other sectors seem to present an increase in the research on digital twins, although not as exponentially as the manufacturing sector. Figure 4 displays the distributions of these domains of application for each application defined in Liu et al.⁸'s nomenclature.

We can see that, compared to the manufacturing Industry for which many different applications exist, the other application

<figure><img src="image_7.png" /><figcaption>Figure 2. PRISMA 2020 flow diagram of the systematic review protocol.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 3. Number of digital twin articles in each application domain per year of publication.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 4. Distribution of the domains of use of digital twins for each of the digital twin applications.</figcaption></figure>
areas have a much smaller number of different applications. One can observe in the Figure 4 that the selected papers from the Construction domain are focused on a few specific applications (asset management, production planning, predictive maintenance, and state monitoring) which are based essentially on monitoring and anticipating. In the context of the construction domain, this mainly concerns monitoring and anticipating the building mechanical state and its behavior for maintenance optimization<sup>22,23</sup> on the one hand, and building logistics management<sup>24,25</sup> on the other hand. Papers presenting twins applications in the Energy Systems domain were focused on using the digital twin as a tool for state monitoring energy in grids<sup>26-28</sup>. Twins in Healthcare were used for the prediction and optimization of human performances<sup>29</sup>.

Four articles did not fit into the defined domains of applica-
tion. Two articles focused on agriculture, and in particular
to the management of a greenhouse<sup>30</sup>, and of the production
planning of an aquaponic farm<sup>31</sup>. Another article focused on the
optimization of an oil and gas pumping process<sup>32</sup>. Finally, an
article focused on the ergonomic aspect of dynamic lighting in a
museum<sup>33</sup>. These results illustrate the increasing development
of digital twins use cases in new sectors.

Distribution of digital twins use cases along the Lifecycle

As presented in Section 2, the lifecycle of a product consists of four phases: Design, Manufacturing, Operational phase, and Disposal. Figure 5 presents the distribution of the publications reviewed among these four phases.

As depicted, the two main phases where digital twins applica-
tions are presented are the Manufacturing (116 in total) and
Operational (56 in total) phases, whereas there are only a
small number of publications focusing on Design (14 in total)
and Disposal (1 in total).

After having observed the different lifecycle phases of the
application of digital twins in the literature, this paper will try
to define, within these lifecycle phases, what are the specific
applications of the digital twin. In order to establish this, the
framework proposed by Liu et al. was used to classify the set of
selected papers. If the application found in one of the reviewed
articles is not included in the applications presented before
then it will be classified in the category named “other”. After
study of the items in this category this may allow us to identify

<figure><img src="image_10.png" /><figcaption>Figure 5. Number of digital twin articles in each lifecycle phase per year of publication.</figcaption></figure>
new applications not yet included in the current classification
as defined by Liu *et al.*⁸.

The result of this review is presented in Figure 6.

Regarding the product lifecycle, as shown in Figure 6, the
applications of DT are mostly centered on the Manufacturing
and Operational phases. The applications of biggest interest
are the ones focused on monitoring and control (Real-time moni-
toring - 26, Production control - 21, State monitoring - 15),
on optimization (Production planning - 34, Process evalua-
tion and optimization - 20), and on predictive maintenance (23).
These groups of applications appear to be the main focus of
research on digital twins.

Figure 6 also pinpoints an emerging field of application of DT not considered within the categories proposed by Liu *et al.*<sup>8</sup>; the Disposal phase. The digital twin is used within the identified paper<sup>16</sup> as a database containing all the product information, from its manufacturing information (CAD, CAM, ...) to its updates, repairs, change of parts, and so on. This data model then supports end-of-life decisions (Repair, Recondition, Remanufacture, Recycle, ...), here in the context of Waste from Electrical and Electronic Equipment (WEEE). This digital twin application for the end-of-life phase of a product lifecycle justifies the need for a new category, which we decided to name “Product End-of-life management” in the end-of-life phase to complete Liu *et al.*<sup>8</sup>'s classification.

In addition to this new application in the Disposal phase, another application, in the Operational Phase, did not fit any of the applications established in Liu *et al.*<sup>8</sup>'s classification. This application focusses on the ergonomic aspect of dynamic lighting in a museum<sup>33</sup>. This digital twin application for the operational phase of a product lifecycle justifies the need for a new application centered around interaction with humans and ergonomics, which we decided to name “Interactivity and Ergonomic Optimization” to complete Liu *et al.*<sup>8</sup>'s classification.

The classification of Liu et al. is centered around the Manu-
facturing Industry. As a result, the different applications
proposed in their classification are particularly adapted to
this Industry, and less to other fields of use of the digital
twin. The emergence of the use of the digital twin in these
other domains has pushed this study to complete this clas-
sification to make it more comprehensive, as the application
domains of the digital twin widen.

Digital twin subcategories along the product lifecycle

As presented in Figure 1, four subcategories of digital twins exist: Pre-Digital Twin, digital model, digital shadow, and digital twin. Figure 7 presents the distribution of the publications reviewed among these subcategories.

As presented in the figure, a majority (56%) of digital twins
in the reviewed literature falls under the Digital Shadow
subcategory, while only 18% are actual digital twins.

Regarding the interaction structure between the digital and
physical objects, a majority (58%) of digital twins in the
reviewed literature actually falls under the digital shadow
subcategory, as presented in Figure 7, underlining the fact that
most digital twin applications only consist of an acquisition
chain from the physical to the digital realm, and not a command
chain from the digital to the physical system.

It is also interesting to look at this distribution in perspective
with Kritzinger et al.<sup>123</sup>'s work. In their research back in 2018,
nearly the same analysis was performed on all papers about
digital twins, at a time when only a few papers contain-
ing actual use cases were available. The other noticeable
difference was the absence of the “Pre-Digital Twin” sub-
category in their classification, where it was included in the
“Digital Model” subcategory.

The result of this comparison is presented Figure 8.

<figure><img src="image_11.png" /><figcaption>Figure 6. digital twin applications in the literature, along the product lifecycle.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Figure 7.** Proportion of each subcategory of digital twin in the reviewed literature.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 8.** Comparison between our review and another review on digital twins subcategory by Kritzinger *et al.*¹².</figcaption></figure>

The result of their study is that an important proportion of artifacts presented under the name of digital twins were in fact digital shadows, digital models, or undefined, whereas only 18% of these articles contained actual digital twins. Our results show that the proportion of DT under its strict definition remain today at the level of 17% of digital twins, with

digital shadows, digital models and pre-digital twins being more prevalent. This shows that the development of the twins applications is mainly concentrated on the digital shadows, as we could also see in Figure 7, since it is the only category that presents a clear increase of its share, since Kritzinger *et al.*'s study.
Figure 9 shows the same analysis from the perspective of the lifecycle.

One result from this figure is that almost all the digital twins used in the design phase are pre-digital twin or digital models. This may seem intuitive, as the physical object is not yet in the manufacturing phase, it is complex to acquire information using sensors from it. We can therefore ask ourselves if and how the use of digital twins could be developed in this design phase. Nevertheless, two articles present the use of a digital shadow in the design phase. Indeed, in these two articles, a physical prototype is created during the design process, in an iterative optimization process. Tests are carried out on these prototypes, equipped with sensors, which allow a real-time visualization of the performance of the prototypes, and thus allow a faster iteration in the design phase. This leads to a possible use of digital shadows and digital twins in the design phase, as the use of these tools in this phase will allow optimization and time saving during the design iterations.

In addition, it is observed that most digital twins are used in the production phase, while still not being commonly used in the other phases of the product lifecycle.

To go into deeper detail as to the link between the different digital twin applications and the digital twin subcategories, this study is carried out separately for each application along the product lifecycle. The results are presented in Figure 10.

As seen in this figure, applications focused on monitoring and control present a large majority of the use of digital shadows (real-time monitoring 93%, production control 47%, and state monitoring 80%), due to the lack of necessity for a command loop in these applications, whereas a larger proportion of digital twins are used in applications focused on optimization (production planning 29%, process evaluation, and optimization 35%).

## Discussion

The results of our review of the literature on the applications of digital twins allow us to draw several observations on their uses in Research and Industry. In this section, analysis of the results of the literature review will aim to provide responses to the research questions raised in the methodology, as well as making observations on the current state of research on digital twins, and thus to identify the different research gaps in this domain.

First, some limitations have been identified towards the classification established by Liu *et al.* and used in this review. On the one hand, these are linked to the emergence of the disposal phase, which can be explained by the absence of applications in this phase when this classification was created. Therefore, this classification was updated within this paper in the context of the digital twin for Industry to integrate the emerging new applications in this field. On the other hand, new domains of applications have appeared for digital twins, whose applications may not always fit in Liu's classification which was designed for manufacturing industry-oriented applications.

As highlighted in Figure 6, despite a large proportion of applications in the Manufacturing phase, recently more and more applications are appearing in the design phase. We can see that the development of the digital twins is starting to be extended beyond these few traditional applications, but it remains an emergent development. These emerging works show a lot of potentialities linked to the efficiency of the design process thanks to iterations in “real system” conditions with users, as well as the possibility to enhance “collaborative” design between communities of designers-users for example³⁴.

The findings indicate a lack of dedicated applications for Digital Twins in the disposal phase, emphasizing the need for increased community effort in developing Digital twin research and applications for managing resources throughout their entire

<figure><img src="image_14.png" /><figcaption>Figure 9. Proportion of each subcategory of digital twin, along the product lifecycle.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Figure 10. Proportion of each subcategory of digital twin for each application along the product lifecycle.</figcaption></figure>

lifecycle, particularly during the end-of-life stage, in light of current energy and raw material scarcity challenges³⁵.

This comprehensive review confirms the analysis of Rasheed et al.³⁶ in the sense that there are still barriers to digital twins from a technological standpoint. In the context of industrial applications, the results confirm that the deployment of digital twins is still in its early stages as the realization of a true digital twin relies on the two-way connection between the physical object and its digital equivalent. This data flow, especially from the physical object to the digital object, must provide relevant and timely data in order to be a reliable representation of the physical system. Various technical difficulties are highlighted by this requirement.

First of all, acquiring the relevant data on the physical object can be an issue. Indeed, some information coming from the product to feed the twin can be difficult to obtain in real time. In the case of the manufacturing sector in particular, it can be difficult to acquire data directly on the manufactured part during its production by means of sensors. Thus, it is preferred in most of the studied cases of applications found in this review to realize a digital twin not of the manufactured product, but of the production machine carrying out this process. This approach allows to more simply collect information on the state of the machine, which allows to deduce the state of the produced object by exploiting models of the machine. A limitation of this approach is related to the reliability of the information received on the product by this method, because the accuracy of this information will depend on the accuracy of the underlying model. This notion is related to the study of social barriers and trust in the data and the model, discussed later.

The management of large volumes of data can also pose a problem for the feasibility of a digital twin, because for a twin to be functional, it must be able to update its model based on the data received in a short enough time to remain a relevant representation of the system studied, which implies the availability of a computer equipment capable of managing large amounts of data within a complex model which is not always the case in manufacturing environments. This may explain why, often in the literature twins are not used in a very advanced form or for an entire system. As Figure 7 and Figure 9 demonstrate, most digital twin applications are still at the digital model or digital shadow stage. digital twins are therefore yet rarely used to their full potential.

Results of this research also show that the twins are mostly used at a local scale, for a specific function defined by an immediate implementation need. Currently, digital twins are still thought of as add-on solutions, which are attached to a system to improve a particular task, a specific action. This way of thinking, by first building the physical system and then adding the twin, reduces the possibilities of using digital twins and sometimes complicates their implementation (sensors, design, ...)³⁷. This allows us to infer that the industrial actors still need a better understanding of the concept itself and of the implementation process to get the maximum benefit from the implementation of the digital twins. It is necessary to change the way systems and their digital twins are built, by thinking about their design together to maximize their potential.

From an operational point of view, this study has shown a real separation of the uses of digital twins according to application sectors, as can be seen in Figure 4. The manufacturing
industry, leading the applications of digital twins, presents applications in most of the defined categories in the classification made by Liu, and in his updated version proposed in this paper. Indeed, in the civil engineering domain, we can see that the applications are almost exclusively focused on the monitoring and prediction of the state of structures over time for maintenance applications.

Moreover, these different domains use different tools/ denominations for the realization of digital twin models. For example, the civil engineering domain mainly uses BIM (Building Information Modeling), whereas CAD (Computer-Aided Design) or physical models are more often used in industry. This results in discrepancies between the projects and applications in the different domains of applications of digital twins or even within the same domains, and contributes to slowing the progress of the digital twin technology³⁸. There is a need for a generic model for digital twins, and in a larger sense a set of standardizations for digital twins³⁶.

From a social acceptance point of view, several issues are also raised by the appearance of digital twins. An initial problem is that of data security and ownership. For their operation, digital twins require a large amount of data to feed their models. This raises questions about the notion of privacy and the protection of personal data³⁹,⁴⁰. Indeed, as the twin is the result of data captured by sensors and feeding a model, which represents a collaborative work, it can be difficult to define who the owner of the data is.

Another problem is the question of the reliability of the digital twin, especially when its work must replace a human or help a human decision. This comes down to the question of whether the digital twin is trustworthy⁴¹. This means that it must work as expected at all times.

This can be separated into two parts: on the one hand, making sure that the data captured by the digital twin reliably represents the physical system being matched, and on the other hand

that the virtual model of this system is sufficiently accurate and reliable. It is therefore necessary to put in place regulations and control procedures for the digital twins, as well as to leave the human in the decision loop, especially when these decisions may involve significant risks, for instance in the case of controlling heavy machinery in contact with human workers. On this aspect, among the applications observed on this review, it appeared that a lack of studies related to the human-digital twin interaction. This aspect will be essential in the coming years to improve the understanding and acceptance of digital twins by operators and process analysts, and guaranty the safety and reliance of digital twins.

To finish, Table 3 summarizes the most relevant dimensions that should be tackled by the scientific and industrial communities to catalyze the deployment of digital twins in the manufacturing sector in the years to come.

## Conclusion and future work

Since its first appearance twenty years ago, the digital twin concept has received increasing attention in the literature. Its rapid development, mainly in the last decade, has been fostered by the advent of technologies such as the Internet of Things (IoT), Artificial Intelligence, and advances in sensors and in data processing within Industry 4.0.

A limitation of this study is that it focuses only on the artefacts grouped under the name of digital twin. Indeed, before these artefacts were given this name, previous research on these objects had begun in product lifecycle management (PLM) as an emulation of the operating part⁴².

This research study aimed to answer three research questions around the concept of digital twins today: What are its application areas? What are its applications along the product lifecycle? What subcategories of digital twins are used for these applications in the literature?

In the case of the applications areas, the research is still mainly focused on the domain of the manufacturing industry, but its

<table><caption>**Table 3.** Ideal digital twin Summary in the Manufacturing Sector.</caption><thead><tr><td></td><td>Current Status of DT</td><td>Requirements to further deployment of DT in manufacturing applications</td></tr></thead><tbody><tr><td>Technological dimension</td><td>Complex and sometimes indirect data acquisition<br/>Choice between accurate and reliable model and computation cost and time.<br/>Digital twins used as add-on to an existing system.</td><td>Reliable and precise model designed in parallel with the physical system, allowing to design the physical object thinking about the presence of a Twin, and allowing a direct acquisition of its data.<br/>Designing the digital twin alongside its physical counterpart.</td></tr><tr><td>Operational dimension</td><td>Separation of the applications according to the fields of use of the digital twin.<br/>Use of separate tools and methods for different applications and domains.</td><td>Creation of a standardization of the methods of realization of the digital twins, allowing a better sharing and a better development of the digital twins collaboratively.</td></tr><tr><td>Social dimension</td><td>No clear rules regarding data ownership.<br/>Trust issues with the data and models used.</td><td>Establishment of regulations on data ownership, as well as on the reliability of models and data.<br/>Collaborative Human-Twin interaction.</td></tr></tbody></table>
research development is beginning in an increasing number of sectors, including healthcare, smart cities, or construction.

Although the research on digital twins concerns an increas-
ing number of applications along the product lifecycle,
from its design to end-of-life, our results show that most
publications are focusing on a small number of applications,
mainly monitoring, optimization, and predictive maintenance.
The predominance of these functions explains the fact that
the majority of the applications of the digital twin in the
literature do not present a chain of control from the digital
organ to its physical counterpart, but only aim to acquire data
from the physical part to feed a model.

Finally, this work has led to a discussion on the design of digital twins, pointing out the disparity of the implementation of the digital twins along the lifecycle of the product. Indeed, most of the digital twins implementations were realized after the system was already produced or in use, and the digital twin was implemented afterwards as an add-on, resulting in implementation operations.

This original quantitative analysis allows us to draw a portrait
of the issues at stake in this hot topic, which is widely
mobilized at least in terms of publication, but which sometimes
suffers from imprecision. This work reveals the research

tracks that should be investigated around the digital twin. An
agenda for researchers is thus established.

To reduce the possible costs of implementation, and to allow for the use of digital twins for more applications along the product lifecycle, further work will, in the context of the European project INEDIT, study the role of a digital twin design methodology for designing the digital twin of a product alongside its physical counterpart, in a process of “Design for digital twinning”, to observe if it impacts on the use of the digital twin along the lifecycle.

Data availability

Figshare. Digital twins along the product lifecycle product lifecycle: a systematic literature review of applications in manufacturing, 10.6084/m9 figshare.21941033²¹

This project contains the following underlying data:

Data file 1. (Analysis table for this review, presenting the screened articles and their analysis based on the classifications presented in this paper)

Data are available under the terms of the Creative Commons
Four “No rights reserved” data waiver (CC BY 4.0).

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