332
T. Riedelsheimer et al.
reviews with a focus on the use of Big Data, LCA and data collection have been
executed (Mieras et al. 2019; Song et al. 2018; Cooper 2013). Nevertheless, a holistic
review of progress of LCSA from a technological perspective is still missing.
Therefore, a systematic and extensive literature review is conducted to answer the
following questions:
• Which current digital solutions exist for LCSA and how can they be categorized
and clustered?
• Which challenges of LCSA do those digital solutions address and which may they
solve?
21.3.1 Approach
The following steps were executed for performing the literature review:
1. Explorative search of relevant papers in the field of new technologies with the
objective to enable progress in LC(S)A, which resulted in 10 papers,
2. Keyword analysis of these papers and categorization of abstracts, which led to
the following clusters: (I) approach (LCA, LCC, S-LCA, LCSA) in focus and
(II) technologies considered,
3. For each cluster, the sub clusters were identified and respective search strings
defined. Cluster II includes the technological trends AI, Big Data, Blockchain,
Cyber-Physical-System (CPS), Digital Twin (DT), and Internet of Things (IoT),
4. The respective search strings were defined and the search has been conducted
in the database SCOPUS, which is one of the leading databases for scientific
research.
5. The resulting papers (in total 180 papers) for each combination of search strings
were then filtered for relevance and the abstracts of the remaining relevant papers
(110 papers) were read and analyzed.
21.3.2 Results
The results of the literature review were analyzed in two steps. First, the overall
number of papers per cluster and the papers with overlapping research topics from
different clusters were identified. The overview is shown in Fig. 21.1. The nodes
show the search term and the number of related papers in brackets. The number of
papers, which treat two search terms is depicted on the edge between two nodes. The
analysis of the sustainability assessment approach (cluster I, highlighted in green)
shows that most papers focus on LCA. The total number of papers of a category may
not be equal to the sum of the papers filtered by topic, since some papers deal with
multiple topics.
T. Riedelsheimer et al.
reviews with a focus on the use of Big Data, LCA and data collection have been
executed (Mieras et al. 2019; Song et al. 2018; Cooper 2013). Nevertheless, a holistic
review of progress of LCSA from a technological perspective is still missing.
Therefore, a systematic and extensive literature review is conducted to answer the
following questions:
• Which current digital solutions exist for LCSA and how can they be categorized
and clustered?
• Which challenges of LCSA do those digital solutions address and which may they
solve?
21.3.1 Approach
The following steps were executed for performing the literature review:
1. Explorative search of relevant papers in the field of new technologies with the
objective to enable progress in LC(S)A, which resulted in 10 papers,
2. Keyword analysis of these papers and categorization of abstracts, which led to
the following clusters: (I) approach (LCA, LCC, S-LCA, LCSA) in focus and
(II) technologies considered,
3. For each cluster, the sub clusters were identified and respective search strings
defined. Cluster II includes the technological trends AI, Big Data, Blockchain,
Cyber-Physical-System (CPS), Digital Twin (DT), and Internet of Things (IoT),
4. The respective search strings were defined and the search has been conducted
in the database SCOPUS, which is one of the leading databases for scientific
research.
5. The resulting papers (in total 180 papers) for each combination of search strings
were then filtered for relevance and the abstracts of the remaining relevant papers
(110 papers) were read and analyzed.
21.3.2 Results
The results of the literature review were analyzed in two steps. First, the overall
number of papers per cluster and the papers with overlapping research topics from
different clusters were identified. The overview is shown in Fig. 21.1. The nodes
show the search term and the number of related papers in brackets. The number of
papers, which treat two search terms is depicted on the edge between two nodes. The
analysis of the sustainability assessment approach (cluster I, highlighted in green)
shows that most papers focus on LCA. The total number of papers of a category may
not be equal to the sum of the papers filtered by topic, since some papers deal with
multiple topics.