Digital Twins for Real-time Data Analysis in Industrie 4.0:

Pathways to Maturity

Philip Stahmann, Arne Krüger and Bodo Rieger

University of Osnabrueck, Germany

Keywords: Digital Twin, Real-time Data Analytics, Industrie 4.0.

Abstract: Digital twins are virtual copies of production systems’ physical components. In Industrie 4.0, they represent

a promising opportunity for analysing production data in real-time and contribute to improved production

planning and control. However, development of digital twins is challenging for companies due to missing

guidance. In this research paper, we identify four maturity levels for digital twins for real-time data analysis

based on a structured literature review and a market analysis that resulted in a total of 82 analysed

contributions. The results are evaluated through a qualitative interview with four experts from academia and

practice. Manufacturing companies can use the maturity levels for self-assessment and as a guideline. Future

research can use the maturity levels for integration into holistic maturity models.

1 INTRODUCTION

Industrie 4.0 describes the vision of digitalized

production in which decision-makers can be provided

with analysis results in real-time (Kagermann,

Wahlster, & Helbig, 2013). Digital twins represent a

core component for fulfilling this vision (Kuhn,

2017). A digital twin describes the virtual

representation of a physical object. The virtual

representation allows analysis and simulation at any

time, even already before the existence of the real

object. To implement digital twins, all characteristics

of the physical object have to be known (Stark &

Damerau, 2019). This includes current as well as

possible future states.

Using digital twins can have significant

advantages for real-time analysis in production. E.g.,

detailed simulations can predict production

anomalies and instantly model their consequences

(Riedelsheimer, Lünnemann, Wehking, & Dorfhuber,

2020). Current states of production can provide for

better planning of future production orders by means

of the digital twin and thus increase overall

production efficiency.

However, the requirements for a holistic digital

representation of production are high (Riedelsheimer

et al., 2020). Above all, it is demanding when

companies want to digitally map flexibly combinable

production machines including their production

environments. The integration of upstream and

downstream processes, such as marketing and sales,

makes the implementation even more challenging

(Kaufmann & Servatius, 2020). Yet, the integration

and real-time analysis of data from cross-

departmental processes may lead to competitive

advantages by enabling predictive capabilities and

seamless workflows (Liu, Meyendorf, & Mrad,

2018). Difficulties that arise in the implementation of

digital twins essentially relate to the provision of data

in real-time and the competence to analyse this data

in a target-oriented manner (Kunath & Winkler,

2018). These difficulties can lead to inertia in the

adoption and implementation of digital twins for real-

time data analysis. Therefore, companies need

guidance to assess the current state of their digital

twin implementation and to know along which

dimensions they should continue their development

(Riedelsheimer et al., 2020). Maturity levels specific

to digital twins with the purpose of real-time data

analysis may provide manufacturing companies with

this guidance.

On this basis, we formulate the following research

question: How can maturity levels be designed for

digital twins used for real-time data analysis? To

answer this research question, we conducted a

structured literature review based on Vom Brocke et

al. (2015) as well as a market analysis of production

companies.

Stahmann, P., Krüger, A. and Rieger, B.

Digital Twins for Real-time Data Analysis in Industrie 4.0: Pathways to Maturity.

DOI: 10.5220/0010688700003062

In Proceedings of the 2nd Inter national Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL 2021), pages 123-130

ISBN: 978-989-758-535-7

123

The remainder of our research contribution

structures as follows. Section two details the

methodological approach. Section three shows

characteristics of the found contributions. Section

four presents the identified maturity levels.

Methodology and results of the qualitative evaluation

are outlined in section five. Lastly, sections six and

seven include a discussion, starting points for future

research and a conclusion.

2 METHODOLOGY

This chapter aims to present the methodological

procedure of the structured literature review in

scientific data bases and the market analysis based on

information from industrial production companies.

Regarding the structured literature review, we

followed the procedure proposed by Vom Brocke et

al. (2015). Relevant scientific literature was searched

in the databases Google Scholar, IEEE Xplore,

Science Direct, Scopus and Springer Link. Table 1

contains the search string combinations.

Table 1: Alternative search string combinations.

Digitaler Zwilling

ital Twin

AND

Real-time data anal

sis

Streaming data analysis

AND

Industrie 4.0

Industr

4.0

Smart factor

Smart manufacturin

On top are the alternatively used terms of the

digital twin in German and English. The German

variant was included, because all participants in the

research project are native speakers. The search terms

in the middle refer to real-time analytical processes.

Below, the alternative equivalents for the German

"Industrie 4.0" are shown. The equivalents were

iteratively determined while going through literature

and included as search strings to expand the search

space. Initially, only two terms were combined.

Subsequently, terms from all three sections shown in

Table 1 were used.

We first used a keyword search, then forward and

backward search. The search was restricted to

publications from after 2015 as a Google Trends

analysis shows a tendency of increased attention in

literature on digital twins from 2015 onwards. Only

literature written in German or English was

considered. The literature identified in the search was

selected by two inclusion and two exclusion criteria.

Inclusion criteria were, first, that the search terms

"Digitaler Zwilling" or "Digital Twin" were

mentioned in title, introduction or keywords.

Secondly, publications were included in which the

concept of the digital twin was clarified by

background information or clear descriptions even

without mentioning the terms explicitly. Publications

whose information was insufficient to contribute to

answering the research question were excluded. In

addition, publications that were purely theoretical and

did not show any reference to an entrepreneurial

context were excluded. The validity of one criterion

was decisive for inclusion or rejection. If publications

fulfilled at least one inclusion criterion and at least

one exclusion criterion, an individual decision was

made in each case. After application of the criteria

and exclusion of duplicates, 44 results were obtained.

Additionally, we performed a market research.

We searched the keywords from Table 1 using

Google. The goal was to include current practical

applications of the digital twin in industrial practice.

To this end, we focused on press releases of industrial

manufacturing companies covering descriptions of

implementations, implementation recommendations

and experience with the topic. Again, we restricted

our search to publications from 2015 or later. The

inclusion and exclusion criteria were the same as in

the structured literature review, except for the second

exclusion criterion as purely theoretical results were

not expectable. This search resulted in 38 practical

contributions.

This research paper does not aim to present a

ready-made maturity model, but to identify stages of

such a model in order to show pathways to maturity.

However, we still adhere to the eight requirements for

the development of a maturity model given by Becker

et al. (2009).

3 FINDINGS

This chapter aims to present further information on

the references found by structured literature review

and market analysis. Table 4 shows the kinds of

contributions in the scientific literature. Most of the

publications make a practical contribution. In fact,

only two publications target a purely academic

audience. 15 publications contribute to both academia

and practice. Case study creation or analysis is the

most frequently found methodology in the scientific

references.

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Table 2: Kinds of contributions of scientific references.

Contribution Type Methodology No.

Academic

Book Literature review 1

Journal Literature review 1

Practical

Book

Case study 6

Literature review 1

No specific 3

Review 4

Conference

Case study 6

Literature review 1

Review 2

Qualitative survey 1

Journal

Case study 1

Literature review 1

Review 1

Both

Conference

Design science 1

Literature review 4

Review 1

Journal

Literature review 2

No specific 4

Review 3

Table 3: Distribution of scientific references by industry

and purpose.

Industry Purpose No.

Engineering

Analyse characteristics 2

Analyse different applications 1

Logistics

Analyse characteristics 1

Analyse different applications 2

Creation of digital twins 3

No specific

Analyse characteristics 10

Analyse different applications 7

Explore potentials 3

Table 3 presents the industries and purposes the

scientific publications refer to. Only engineering and

logistics are explicitly mentioned, most publications

do not name a specific industry. These publications

are grouped as “no specific” in the first column. The

analysis of characteristics of digital twins is the most

frequent purpose among the identified scientific

references. The second most frequent purpose is the

analysis of applications. These scientific references

refer to real or conceptual implementations of the

digital twin and outline its disadvantages and

advantages. Ten scientific publications have other

purposes. Examples are the creation of frameworks or

generic requirements for the implementation of

digital twins (Azarian, Yu, Solvang, & Shu, 2020).

Ten scientific publications have other purposes.

Examples are the creation of frameworks or generic

requirements for the implementation of digital twins

(ibid.). Table 4 presents the practical contributions’

industries and purposes. Most of these refer to quality

and efficiency assessment as well as advancements in

digitalization when implementing the digital twin or

specific features to obtain advantages of real-time

data analysis.

Table 4: Distribution of practical references by industry and

purpose.

Industry Purpose No.

Automotive

Digitalization 9

Planning 9

Quality and efficiency assessment 6

Sales 4

Training 2

Industrial

engineering

Quality and efficiency assessment 6

Other

Digitalization 1

Sales 1

4 PATHWAYS TO MATURITY

Based on our findings in scientific literature and

practical contributions, we identified three

dimensions that enable the differentiation of four

maturity levels. The levels including their dimensions

reoccurred in the referenced publications and were

identified in an iterative process (Becker et al., 2009).

The first dimension refers to real-time data continuity

and consistency. The second dimension relates to

companies’ analytical capabilities for real-time

validation and quality assessment in production. The

third dimension considers the integration of

stakeholders, which include machines and humans

directly involved in production steps or upstream or

downstream processes. Figure 1 relates the

dimensions and shows the four distinguishable

maturity levels. These are sequentially dependent, so

that later levels include all characteristics of previous

levels.

In the first maturity level, data transparency in a

consistent real-time data base is initially created in

isolated production aspects. Continuous real-time

data collection during the execution of single work

steps gradually increases data transparency. Step by

step, a uniform data basis is created that is suitable for

real-time data analysis. This requires consistent data

models before analysis, since real-time analysis is not

suitable for separate data organization after or parallel

to analysis (Stahmann & Rieger, 2021). Regarding

the third dimension, the focus restricts to company

Digital Twins for Real-time Data Analysis in Industrie 4.0: Pathways to Maturity

125

Figure 1: Maturity levels identified through structured literature review and market analysis.

internal processes. Adamenko et al. (2020) explain the

trade-off between a more and more detailed virtual

representation of a physical object and the increasing

complexity in data organization. The authors examine

the concept of a data-based digital twin, which uses

sensor data of a physical object and enables specific

analyses.

Ait-Alla et al. (2019) intend to observe the effects

of deploying different numbers of sensors to

machines to create digital twins in different detail

levels. The authors can identify differences in the

speed of data communication and productivity

depending on the progress of virtualization of isolated

production steps. In line with our first maturity level,

the degree of data acquisition from a physical source

thus progresses step by step. The major benefit of this

maturity level is the continuous creation of a real-time

data base, which enables the virtual simulation of

physical processes. On the other hand, this may

require to fundamentally change previous data

structures.

Redelinghuys et al. (2020) create a reference

architecture for digital twins including physical

objects and their holistic virtualizations. Using a case

study, the authors demonstrate how the reference

architecture is suitable for real-time simulation and

anomaly detection based on the virtualization of

individual production steps.

The second maturity level in the dimension real-

time data continuity and consistency comprises

transparency over multiple production steps. This

transparency requires a consistent real-time data base

covering data from dependent production steps. The

focus of data management thus shifts from continuous

database creation of isolated aspects to the integration

of related steps in the production process. In the third

dimension, the second maturity level refers to internal

company processes like the first maturity level.

Adamenko et al. (2020) analyse and compare

instantiations of the digital twins to understand

application potentials and design alternatives. As in

our second maturity level, the authors find that

models of several interrelated aspects of production

must be combined virtually to provide a proper

representation of the physical equivalent.

Also, the digital recording and transparency

creation of products across production steps enables

creation of key figures in real-time (Daimler AG,

2020). Aggregated key figures may provide a view of

several products in different steps, more detailed key

figures can address individual products and their

production status.

RFID chips may simplify the real-time

localization of individual products and their

components across production steps (BMW Group,

2020). This means that information on the progress of

all products, involved machine components or

employees is available at all times. By evaluating

virtual production processes, error-prone and

inefficient processes can be identified (Audi, 2019).

Solutions for these processes can then be developed

and tested directly in the virtual production

environment. This means that the entire production

process can be optimized before cost-intensive

changes (Adler & Masik, 2020).

This maturity level’s benefit is the high level of

detail of such an implementation of a digital twin,

which can e.g. be used for comprehensive real-time

simulations of physical processes. A major hurdle in

achieving this maturity level resides in the continuous

integration of the production steps real-time data

(Adamenko et al., 2020).

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In the first dimension, the third maturity level

covers real-time data transparency overall production

aspects of a physical object. The virtualization is

holistic and does not only include isolated aspects.

Accordingly, the second dimension also includes

validation and quality assessment of all production

components involved. The availability of integrated

production data enables leveraging more profound

data analysis and usage (Tao et al., 2019). As

production may include components delivered by

suppliers or the provision of own products for

subsequent manufacturing, the third dimension also

includes other companies (Reisewitz, 2018). Digital

twins that are separated in partial models may

constitute the data basis for cross-company

cooperation (Tao, Qi, Liu, & Kusiak, 2018). E.g., the

supplier of semi-manufactured production

components may deliver specific characteristics

virtually such as weight or quality to facilitate

subsequent production (Reisewitz, 2018). Real-time

production data used for simulation in alternative

production process orders and facility layouts may

reduce costs and improve production efficiency. The

subdivision of digital twins does not only hold

advantages for cross-company cooperation, but also

internally. Depending on the intention, digital twins

that depict all details of a physical object can be

combined. The creation of a large, non-subdivisible

digital twin would be costly, the use e.g. for

simulations might result in overhead calculations.

According to Adamenko et al. (2020), the level of

detail encompassed by subdivided digital twins leads

to better simulations for checking machine and

product conditions in real-time. The detailed

simulation can make the implementation of real

prototypes unnecessary. Slot et al. (2020) propose a

framework to systematize and simplify the

integration of different digital twins. The framework

provides guidelines for the integration of digital twins

to networks, e.g. for real-time analysis.

Furthermore, another potential of the digital twin

in maturity level three is employee training on the

basis of real-time production data (Audi, 2020;

Porsche, 2018). Simulation of different production

situations may increase efficiency. Also, employees’

safety may be increased due to virtual training as

potentially dangerous situations may be trained

during simulation.

The fourth maturity level refers to real-time data

transparency over product lifecycles in the first

dimension. This extends the third maturity level by

covering all aspects of physical objects including

changes over time. Accordingly, the second

dimension also includes upstream and downstream

production processes. The fourth maturity level

therefore integrates internal and cross-company

processes as well as customers. A major benefit of

achieving this maturity level is that customers can be

integrated both before and after production to meet

individualized demand. Before production, the virtual

twin of the product can be used to present production

alternatives to the customer, e.g. using virtual reality

(Porsche, 2018). After sale of a product, customer

usage data can be transmitted to the manufacturer for

real-time analysis. User behavior can form the basis

for follow-up offers or changes to the future product

(Schleich, Anwer, Mathieu, & Wartzack). Negative

effects of strong customer integration may be the

dependence on individual customer behavior and the

loss of know-how regarding own innovative ideas for

product and service development (Gassmann, Kausch

& Enkel, 2010).

Covering entire lifecycles, the digital twin can

also generate benefits in the area of sustainability

(Riedelsheimer et al., 2020). It can form the basis for

defining ecological indicators, such as overall energy

demand or resource efficiency through virtualized

planning and control. E.g., environmentally harmful

emissions of each production component can also be

recorded and analysed.

5 EVALUATION

This chapter outlines methodology and results of the

qualitative interviews conducted with experts from

industry and academia.

5.1 Methodology

To evaluate and broaden our findings, interviews with

four experts were conducted. From industry, an

expert from an internationally operating supplier in

pneumatic automation and an expert from an

international automobile manufacturer were

interviewed. The scientific perspective on the digital

twin was covered by an expert from an institute for

applied sciences and an expert from a business-

related research institution. The interviews were

electronically recorded with the consent of the

interviewees. The interviews lasted about 41 minutes

on average. They were conducted in German, as all

participants were native speakers.

The basis for the expert interviews was a semi-

structured questionnaire that built on the steps of the

three dimensions shown in Figure 1. We did not

explicitly present the identified maturity levels, so

that answers were not restricted to our findings.

Digital Twins for Real-time Data Analysis in Industrie 4.0: Pathways to Maturity

127

Rather, we took an explorative perspective during

analysis of the answers following Bell et al. (2019).

5.2 Results

Table 5 has the purpose to summarize the experts’

main statements on the dimensions identified in

chapter 4. The items listed in Table 5 group into

confirmations or emphasis of the findings from

literature and market research or the addition of

further factors that impact the identified maturity

levels.

Table 5: Results from expert interviews in relation to

dimensions.

Results from expert interviews

First dimension:

 Uniform database across companies

 Virtual mapping of aspects of physical objects

+ Use of digital twin for employee knowledge

preservation

+ Insufficient employee know-how

+ Lack of standardization in creating real-time

data continuity and consistency

! Data granularity needs to fit the digital twin’s

purpose

Second dimension:

 Digital twin used as basis for validation

 Production feasibility assessment of

individual customer demand by simulation

 Comparison of as-is and to-be production

characteristics to detect production anomalies

 Potential for predictive maintenance

+ Lacking trust in simulation results

+ Difficulty identifying cost-benefit ratio

! Possibility of simulation of various aspects

Third

dimension:

 Cross-company cooperation to enhance

efficiency

 Integration of customers’ individual demand

+ Data protection issues

+ General trust issued regarding cooperation

+ added,  confirmed, ! stressed

Relating the experts’ answers to the dimensions

from chapter 4, a connection between the first and

third dimension can be supported. Cross-company

cooperation can be made more efficient through

digital twins.

From a scientific perspective in particular, the

digital mapping of all relevant areas of an object in

the real world for real-time data analysis could be

supported.

Attempts are made to map the entire lifecycle of a

product or machine using digital twins, which

supports the connection of all three dimensions. In

practice, the digital twin allows the consumer to be

integrated more closely into product development. By

collecting real-time consumer data, market research

can be conducted in a more targeted manner.

Besides support of our findings, challenges of

using the digital twin in Industrie 4.0 were addressed

during the expert interviews. Regarding the third

dimension, experts added data protection and lack of

mutual trust between cooperating companies as

particular challenges for the establishment of digital

twins. Moreover, the acceptance of digital twins in

business practice is a major hurdle. Companies often

lack the necessary trust in the simulation results of the

digital twins to follow the proposed recommendations

for action. The lack of trust in the digital twin also

leads to problems when determining its cost-benefit

ratio, which may prevent their development.

Furthermore, there is often a lack of functional

examples to illustrate the added value of improving

or expanding existing applications. Therefore,

empirical values on the use of digital twins for real-

time analysis would be beneficial for determining the

cost-benefit ratio.

According to expert interviews, small and

medium sized companies in particular encounter

obstacles when introducing digital twins. Insufficient

knowledge about their use and creation of real-time

data continuity and consistency were identified as

challenges. For large companies, the challenge lies

more in identifying meaningful areas of application.

From both a business and a scientific perspective, a

lack of standardization in relation to the digital twin

can be identified in conjunction with insufficient

know-how. The expert interviews reveal that the

standardization challenges are primarily taking place

in the first dimension as there is no comprehensive

standardization covering all aspects of a continuous,

consistent real-time data base for digital twins.

6 DISCUSSION

This chapter aims to critically reflect our study and

outline starting points for future research.

Despite adherence to methodological guidelines,

procedure and results of this research paper are not

free from limitations. Regarding the systematic

literature review, there is no guarantee that relevant

research publications were not included due to the

selection of search strings, databases, and exclusion

of publications prior to 2015. In addition, the

application of exclusion criteria is subject to the

judgment of the researcher. 38 sources from industry

that served as basis for deriving the maturity levels

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128

are not from scientific databases. The sources are not

free of economic advertising, which had to be reduced

to objective facts. Moreover, several publications

identified with the help of scientific databases come

from conferences, only 13 were published in journals.

This may indicate that the topic has a low level of

scientific maturity. Future research might increase the

topic’s scientific maturity by investigating long-term

effects and strategies for implementing results

regarding digital twins in data analysis from scientific

reviews (Lim, Zheng, & Chen, 2020; Tao et al.; Tao

et al., 2019; Tao & Zhang, 2017).

The derived maturity levels are generic. In

individual cases, this can make the concrete use of the

maturity levels more difficult. Also, it seems

recommendable to start implementing the levels

subsequently in an iterative process. This is due to the

effort required to change existing data organization

and analysis practices in companies.

In addition, the expert interviews are not

generalizable, as only four experts were interviewed.

Instead, the expert interviews are intended to serve as

an initial critical reflection on the literature review's

findings. Future research may discuss these findings

in more depth in a qualitative study with more

participants from different perspectives.

7 CONCLUSION

The digital twin is a promising concept for

implementing real-time data analytics in Industrie 4.0

production environments. Creating transparency

about current aspects of production holds potential for

controlling production as well as integrating it with

related processes and stakeholders. However,

implementing digital twins for real-time data analysis

requires consistent, continuous data collection and

preparation as well as analytical and integrative

capabilities for processing permanent data streams in

production as well as upstream and downstream

processes. The joint development and overarching

integration of these capabilities is complex and

requires guidance.

A structured review of scientific literature and

practical contributions resulted in 44 scientific

sources and 38 references from industry practice. On

this basis, this paper contributes with the

identification of four maturity levels for digital twins

for real-time data analysis. The result was evaluated

and extended with four qualitative interviews with

experts from industry and academia based on a semi-

structured questionnaire. The evaluation mainly

substantiated our findings and added practical

challenges regarding the implementation of digital

twins.

Our paper contributes to research and practice.

Other scholars can e.g. contribute on the basis of

quantitative surveys of the states of digital twins for

real-time analysis in manufacturing companies. A

further differentiation of the identified maturity levels

is also possible through a broader literature research,

which may include factors that are relevant for

production planning and control, such as reporting

with the help of digital twins. In addition, future

research can integrate the maturity levels into a

broader maturity model for the digitalization of

production. Practitioners can apply the results and

also extend them on the basis of concrete use cases.

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