XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1109
cloud computing are suggested to facilitate the integration,
sharing, and management of the related data and informa-
tion (Ghobakhloo, 2020).
For successful utilization of Industry 4.0, three pil-
lars are identified by Müller et al. (2018), namely: digi-
talization of processes, self-controlled production systems
(also known as smart manufacturing), and inter-company
connectivity. Considering LCA relies on data from many
different sources in the production process where several
different companies can offer solutions for digitalization
within the production process, this study focuses on the
inter-company connectivity aspect of Industry 4.0.
Compatibility across systems has also been raised as a
challenge for utilizing Industry 4.0 for sustainability gains
by Ghobakhloo (2020). Proper data management is seen
as part of tackling challenges with compatibility across sys-
tems. Another enabler is seen through increasing utilization
of data already being collected and reducing data inefficien-
cies (Janík et al., 2022). Achieving good data management
has been suggested through using the FAIR principles:
namely that data is Findable, Accessible, Interoperable, and
Reusable (Wilkinson et al., 2016). Interoperability is also
associated with increasing data utilization and is, therefore,
the focus of this study.
Interoperability is referenced as early as the 1950s
when discussing merging data across systems (Luebbert,
1959). Several definitions of interoperability in relation to
Industry 4.0 have been published in the last decade which
vary somewhat in scope and detail (Global Partnership for
Sustainable Development Data, 2017 GODAN Action,
2019 Wilkinson et al., 2016). They include:
• “the ability of a data set to work with other systems
or datasets without special effort on the part of the
user.” GODAN Action network
• “the ability to access and process data from multiple
sources without losing meaning and then integrate
that data for mapping, visualization, and other forms
of representation and analysis” Global Partnership
for Sustainable Development Data
• “the ability of data or tools from non-cooperating
resources to integrate or work together with minimal
effort” Wilkinson et al., 2016
This emphasizes the point already lifted in the literature
that interoperability means different things to different
people (Morales &Orrell, 2018). This is likely to hold true
in the multidisciplinary arena that is the extraction indus-
try which can be a challenge to enabling communication
between systems and people in order to realize sustainabil-
ity benefits from Industry 4.0.
Therefore, this study looks at how data interoper-
ability has been seen and addressed by different partners
in the DEQ project. The focus has been on data needed
for environmental assessment through LCA in the project.
Insights have been collected through in-depth interviews
with different partners who are collecting, producing, and
consuming data related to the environmental assessment.
The aim is to determine any differences in understanding
of interoperability between actors, challenges, and possible
solutions for achieving higher levels of data interoperability
for environmental assessment. The context of the study is
the aggregates industry, however, insights on how hurdles
can be overcome for other extraction industries is sought.
The research questions considered are:
• Do different partners have different understandings
of data interoperability?
• What challenges exist for achieving data interoper-
ability for the aggregates industry?
• Which tools are helpful in achieving data
interoperability?
BACKGROUND
The DEQ project started in 2021 and is due to finish in
2025. The project is comprised of 25 partner organizations
spanning a wide variety of backgrounds making it a mul-
tidisciplinary arena. The main objective of the project is
to design, develop, and validate an intelligent quarry sys-
tem moving the industry towards achieving benefits from
Industry 4.0 implementations. To work towards this, the
project is split into ten work packages where Work Package
(WP) 4 addresses the IT infrastructure for the system. As
the project is now in its final half, the initial design and
development of the IT infrastructure is completed. The
IQS consists of a Data Lake platform, IoT infrastructure,
and individual services.
WP 5 within the project aims at utilizing this infra-
structure to further develop tools for environmental assess-
ment and management. Part of achieving this relies on
utilizing production and consumption data produced by
other systems. Key factors for the use of such tools by pro-
ducers in the future is a high level of automation to avoid
as much manual input as possible, along with appropri-
ate analysis of such data to provide relevant environmental
information. As this focuses on the information and data
itself, the Data Lake platform is the main interest for WP 5
within the IT infrastructure. The initial architecture for the
Data Lake is given in Figure 1.
Both the reduction of manual data input and the
smoother analysis of data for environmental assessment are
cloud computing are suggested to facilitate the integration,
sharing, and management of the related data and informa-
tion (Ghobakhloo, 2020).
For successful utilization of Industry 4.0, three pil-
lars are identified by Müller et al. (2018), namely: digi-
talization of processes, self-controlled production systems
(also known as smart manufacturing), and inter-company
connectivity. Considering LCA relies on data from many
different sources in the production process where several
different companies can offer solutions for digitalization
within the production process, this study focuses on the
inter-company connectivity aspect of Industry 4.0.
Compatibility across systems has also been raised as a
challenge for utilizing Industry 4.0 for sustainability gains
by Ghobakhloo (2020). Proper data management is seen
as part of tackling challenges with compatibility across sys-
tems. Another enabler is seen through increasing utilization
of data already being collected and reducing data inefficien-
cies (Janík et al., 2022). Achieving good data management
has been suggested through using the FAIR principles:
namely that data is Findable, Accessible, Interoperable, and
Reusable (Wilkinson et al., 2016). Interoperability is also
associated with increasing data utilization and is, therefore,
the focus of this study.
Interoperability is referenced as early as the 1950s
when discussing merging data across systems (Luebbert,
1959). Several definitions of interoperability in relation to
Industry 4.0 have been published in the last decade which
vary somewhat in scope and detail (Global Partnership for
Sustainable Development Data, 2017 GODAN Action,
2019 Wilkinson et al., 2016). They include:
• “the ability of a data set to work with other systems
or datasets without special effort on the part of the
user.” GODAN Action network
• “the ability to access and process data from multiple
sources without losing meaning and then integrate
that data for mapping, visualization, and other forms
of representation and analysis” Global Partnership
for Sustainable Development Data
• “the ability of data or tools from non-cooperating
resources to integrate or work together with minimal
effort” Wilkinson et al., 2016
This emphasizes the point already lifted in the literature
that interoperability means different things to different
people (Morales &Orrell, 2018). This is likely to hold true
in the multidisciplinary arena that is the extraction indus-
try which can be a challenge to enabling communication
between systems and people in order to realize sustainabil-
ity benefits from Industry 4.0.
Therefore, this study looks at how data interoper-
ability has been seen and addressed by different partners
in the DEQ project. The focus has been on data needed
for environmental assessment through LCA in the project.
Insights have been collected through in-depth interviews
with different partners who are collecting, producing, and
consuming data related to the environmental assessment.
The aim is to determine any differences in understanding
of interoperability between actors, challenges, and possible
solutions for achieving higher levels of data interoperability
for environmental assessment. The context of the study is
the aggregates industry, however, insights on how hurdles
can be overcome for other extraction industries is sought.
The research questions considered are:
• Do different partners have different understandings
of data interoperability?
• What challenges exist for achieving data interoper-
ability for the aggregates industry?
• Which tools are helpful in achieving data
interoperability?
BACKGROUND
The DEQ project started in 2021 and is due to finish in
2025. The project is comprised of 25 partner organizations
spanning a wide variety of backgrounds making it a mul-
tidisciplinary arena. The main objective of the project is
to design, develop, and validate an intelligent quarry sys-
tem moving the industry towards achieving benefits from
Industry 4.0 implementations. To work towards this, the
project is split into ten work packages where Work Package
(WP) 4 addresses the IT infrastructure for the system. As
the project is now in its final half, the initial design and
development of the IT infrastructure is completed. The
IQS consists of a Data Lake platform, IoT infrastructure,
and individual services.
WP 5 within the project aims at utilizing this infra-
structure to further develop tools for environmental assess-
ment and management. Part of achieving this relies on
utilizing production and consumption data produced by
other systems. Key factors for the use of such tools by pro-
ducers in the future is a high level of automation to avoid
as much manual input as possible, along with appropri-
ate analysis of such data to provide relevant environmental
information. As this focuses on the information and data
itself, the Data Lake platform is the main interest for WP 5
within the IT infrastructure. The initial architecture for the
Data Lake is given in Figure 1.
Both the reduction of manual data input and the
smoother analysis of data for environmental assessment are