21 Progress for Life Cycle Sustainability Assessment by Means …
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LCA. They enable modelling of product lifecycles (Wellsandt 2017), collection of
product-specific primary data via sensors and IoT-capabilities (Barni, et al. 2018),
online-assessment (Rückert et al. 2018) as well as domain- and user-specific decision
support (Barni et al. 2018; Riedelsheimer et al. 2018). A DT has the capabilities for
automatic and autonomous decision-making up to action taking (Riedelsheimer et al.
2018), which can be used for decision support systems.
Internet of Things (IoT)
The search for IoT in the context of LCSA resulted in 18 papers with a main emphasis
on LCA. After filtering, ten papers are considered as highly relevant, as they propose
new technological solutions for the execution of LCSA (1), LCA (9), LCC (2) and/or
SLCA (1). In the analyzed research, sensors are named as enablers for the collection of accurate, product-individual and actual data. Most of the papers focus on
energy consumption during Begin of Life (BoL). A highly relevant approach for this
research, is presented under the term ubiquitous Life Cycle Assessment by Raihanian Mashhadi and Behdad. The authors propose automated data collection during
the manufacturing phase by using sensors and IoT-products on the example of a
whole series of hard disc drives (Raihanian Mashhadi and Behdad 2018). Tu et al.
propose an approach for a dynamic carbon footprint (CF) based on IoT-technology
(Tu, et al. 2017). Garcia-Muiña et al. also use sensors and meters from a digitized
production environment (Industry 4.0) as well as the input from manufacturing ITSystems (MES) for a detailed impact analysis (Garcia-Muiña et al. 2018) in LCA,
LCC and S-LCA. Brundage et al. present an analysis of sustainability methods for
feedback to design with IoT from the manufacturing phase (Brundage 2018). The
research by Kim et al. and Gu et al. focuses on the application of decision support
in the End of Life (EoL) phase with data collection via sensors in the use phase
(Kim et al. 2017; Gu et al. 2017). In addition, a new conceptual framework for IoT
application in LC(S)A is developed by Tao, et al., who present a four-layer model
using IoT-technology for data collection and integrating the bill of material (BoM)
for data storage (Tao 2014). The solution aims to evaluate product individual energy
consumptions along the whole lifecycle. The authors also discuss integration with
existing enterprise IT-systems. In a similar approach Tao, Wang et al. present a framework for IoT (Tao 2016). A conceptual framework with different digital tools, that
support along the lifecycle to optimize the sustainability of manufacturing systems,
is proposed by Cerri et al. (2016).
Summary
In general, the literature review shows a wide scope of research for the application of
different technologies in the framework of digitization and Industry 4.0. Against the
background of a digitized product and service lifecycle, especially IoT-Technologies,
CPS, DTs and AI may solve data and feedback challenges through new ways of data
collection, transfer, validation and intelligent analysis. For example, automated data
collection with sensors in the context of Industry 4.0 and IoT may supply current
data for a real-time LCSA-execution. AI and semantics can be used to identify and
analyze dependencies between indicators and support decision making.
335
LCA. They enable modelling of product lifecycles (Wellsandt 2017), collection of
product-specific primary data via sensors and IoT-capabilities (Barni, et al. 2018),
online-assessment (Rückert et al. 2018) as well as domain- and user-specific decision
support (Barni et al. 2018; Riedelsheimer et al. 2018). A DT has the capabilities for
automatic and autonomous decision-making up to action taking (Riedelsheimer et al.
2018), which can be used for decision support systems.
Internet of Things (IoT)
The search for IoT in the context of LCSA resulted in 18 papers with a main emphasis
on LCA. After filtering, ten papers are considered as highly relevant, as they propose
new technological solutions for the execution of LCSA (1), LCA (9), LCC (2) and/or
SLCA (1). In the analyzed research, sensors are named as enablers for the collection of accurate, product-individual and actual data. Most of the papers focus on
energy consumption during Begin of Life (BoL). A highly relevant approach for this
research, is presented under the term ubiquitous Life Cycle Assessment by Raihanian Mashhadi and Behdad. The authors propose automated data collection during
the manufacturing phase by using sensors and IoT-products on the example of a
whole series of hard disc drives (Raihanian Mashhadi and Behdad 2018). Tu et al.
propose an approach for a dynamic carbon footprint (CF) based on IoT-technology
(Tu, et al. 2017). Garcia-Muiña et al. also use sensors and meters from a digitized
production environment (Industry 4.0) as well as the input from manufacturing ITSystems (MES) for a detailed impact analysis (Garcia-Muiña et al. 2018) in LCA,
LCC and S-LCA. Brundage et al. present an analysis of sustainability methods for
feedback to design with IoT from the manufacturing phase (Brundage 2018). The
research by Kim et al. and Gu et al. focuses on the application of decision support
in the End of Life (EoL) phase with data collection via sensors in the use phase
(Kim et al. 2017; Gu et al. 2017). In addition, a new conceptual framework for IoT
application in LC(S)A is developed by Tao, et al., who present a four-layer model
using IoT-technology for data collection and integrating the bill of material (BoM)
for data storage (Tao 2014). The solution aims to evaluate product individual energy
consumptions along the whole lifecycle. The authors also discuss integration with
existing enterprise IT-systems. In a similar approach Tao, Wang et al. present a framework for IoT (Tao 2016). A conceptual framework with different digital tools, that
support along the lifecycle to optimize the sustainability of manufacturing systems,
is proposed by Cerri et al. (2016).
Summary
In general, the literature review shows a wide scope of research for the application of
different technologies in the framework of digitization and Industry 4.0. Against the
background of a digitized product and service lifecycle, especially IoT-Technologies,
CPS, DTs and AI may solve data and feedback challenges through new ways of data
collection, transfer, validation and intelligent analysis. For example, automated data
collection with sensors in the context of Industry 4.0 and IoT may supply current
data for a real-time LCSA-execution. AI and semantics can be used to identify and
analyze dependencies between indicators and support decision making.