340
T. Riedelsheimer et al.
are not yet considered (e.g. machine to machine, machine to human), complexity
of LCSA results is challenging to communicate
Additionally, the analysis shows that most applications in case studies are not
covering the whole lifecycle (cradle to grave), but only parts of the lifecycle and
therefore may disregard important impacts or aspects.
21.5 New Concept Digital Lifecycle Twin
A new concept for a Digital Lifecycle Twin (DLT) that is able to address these
open challenges is presented subsequently. As a specific manifestation of the DT, the
concept of a DLT is described by Riedelsheimer et al. (2018). Real-time lifecycle
assessment is defined as a central use case of the DLT. Related work discusses the
DT as an enabler for LCA (Barni et al. 2018) and specifically in the context of IoT
(Raihanian Mashhadi and Behdad 2018). In addition, the research findings from the
IoT cluster (Raihanian Mashhadi and Behdad 2018; Tu, et al. 2017; Garcia-Muiña
et al. 2018; Brundage 2018; Kim et al. 2017; Tao 2014,2016) are seen as important
input for the DLT concept.
The DLT addresses the following identified gaps (Chap 4.3) along the LCSAphases with its key features:
LCI and data collection: Lack of product specific data, no automated data
collection.
Analysis and impact assessment: Complex indicator dependencies of the
different indicators representing LCA, LCC and S-LCA.
Derive measures and interpretation of results: Complexity of identifying design
dependencies between product design decisions and measured sustainability impact,
lack of real-time assessment and real-time support, challenge of automated and
consistent deduction of measurements and feedback.
Communication of results: Lack of providing individual and selected results
tailored to specific groups of users.
A DLT for LCSA is a specific form of a DT. A DLT for LCSA is the digital
representation of a system, which collects and analyzes the sustainability information
of the individual product’s lifecycle from cradle to grave, specifically entailing a realtime, dynamic and product-specific LCSA. Figure 21.4 clarifies the relation of a DLT
to different DT types as well as its context and use cases.
The specific vision for the DLT for LCSA is indicated by the location within
the taxonomy (see Fig. 21.5). The DLT for LCSA must be able to automatically
make decisions, integrates all aspects of sustainability (LCSA) and provides decision
support during the BoL-phase.
A DT, according to Stark et al., consists of six different design elements (Stark
et al. 2019), hardware, software, data repository, digital models and digital shadow
data as well as intelligence. Accordingly, the necessary design elements of a DLT
for LCSA are defined as follows.
T. Riedelsheimer et al.
are not yet considered (e.g. machine to machine, machine to human), complexity
of LCSA results is challenging to communicate
Additionally, the analysis shows that most applications in case studies are not
covering the whole lifecycle (cradle to grave), but only parts of the lifecycle and
therefore may disregard important impacts or aspects.
21.5 New Concept Digital Lifecycle Twin
A new concept for a Digital Lifecycle Twin (DLT) that is able to address these
open challenges is presented subsequently. As a specific manifestation of the DT, the
concept of a DLT is described by Riedelsheimer et al. (2018). Real-time lifecycle
assessment is defined as a central use case of the DLT. Related work discusses the
DT as an enabler for LCA (Barni et al. 2018) and specifically in the context of IoT
(Raihanian Mashhadi and Behdad 2018). In addition, the research findings from the
IoT cluster (Raihanian Mashhadi and Behdad 2018; Tu, et al. 2017; Garcia-Muiña
et al. 2018; Brundage 2018; Kim et al. 2017; Tao 2014,2016) are seen as important
input for the DLT concept.
The DLT addresses the following identified gaps (Chap 4.3) along the LCSAphases with its key features:
LCI and data collection: Lack of product specific data, no automated data
collection.
Analysis and impact assessment: Complex indicator dependencies of the
different indicators representing LCA, LCC and S-LCA.
Derive measures and interpretation of results: Complexity of identifying design
dependencies between product design decisions and measured sustainability impact,
lack of real-time assessment and real-time support, challenge of automated and
consistent deduction of measurements and feedback.
Communication of results: Lack of providing individual and selected results
tailored to specific groups of users.
A DLT for LCSA is a specific form of a DT. A DLT for LCSA is the digital
representation of a system, which collects and analyzes the sustainability information
of the individual product’s lifecycle from cradle to grave, specifically entailing a realtime, dynamic and product-specific LCSA. Figure 21.4 clarifies the relation of a DLT
to different DT types as well as its context and use cases.
The specific vision for the DLT for LCSA is indicated by the location within
the taxonomy (see Fig. 21.5). The DLT for LCSA must be able to automatically
make decisions, integrates all aspects of sustainability (LCSA) and provides decision
support during the BoL-phase.
A DT, according to Stark et al., consists of six different design elements (Stark
et al. 2019), hardware, software, data repository, digital models and digital shadow
data as well as intelligence. Accordingly, the necessary design elements of a DLT
for LCSA are defined as follows.