Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Sustainable manufacturing: trends and research challenges
This map shows the geographic impact of Marco Taisch's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marco Taisch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Taisch more than expected).
This network shows the impact of papers produced by Marco Taisch. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marco Taisch. The network helps show where Marco Taisch may publish in the future.
Co-authorship network of co-authors of Marco Taisch
This figure shows the co-authorship network connecting the top 25 collaborators of Marco Taisch.
A scholar is included among the top collaborators of Marco Taisch based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Marco Taisch. Marco Taisch is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Calà, Ambra, et al.. (2019). Architectural Blueprint Solution for Migrating Towards FAR-EDGE. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 366–371.1 indexed citations
Jansson, Kim, et al.. (2014). V&V assessment package instantiation in ICT-based manufacturing experiences from ten industrial use cases. 1–9.
10.
Stahl, Bojan, et al.. (2013). DEVELOPMENT OF COMPETENCE FOR SUSTAINABLE MANUFACTURING BY USING SERIOUS GAMES. Transactions of FAMENA. 36(4). 63–72.3 indexed citations
Emmanouilidis, Christos, Marco Taisch, & Dimitris Kiritsis. (2013). Advances in Production Management Systems. Competitive Manufacturing for Innovative Products and Services: IFIP WG 5.7 International Conference, APMS ... in Information and Communication Technology). Springer eBooks.1 indexed citations
May, Gökan, et al.. (2012). Assessment of Sustainable Practices in New Product Development. HAL (Le Centre pour la Communication Scientifique Directe). 437–447.3 indexed citations
15.
Rossi, Mónica, et al.. (2011). Lean Product Development: Fact Finding Research in Italy. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–9.5 indexed citations
16.
Rossi, Mónica, et al.. (2011). Proposal of a method to systematically identify wastes in New Product Development Process. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–9.7 indexed citations
17.
Taisch, Marco, et al.. (2008). Development of PROMISE Architecture and PDKM Semantic Object Model. Cambridge University Engineering Department Publications Database.10 indexed citations
18.
Gerosa, Marco Aurélio & Marco Taisch. (2008). A novel industrial services reference model. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–8.2 indexed citations
Cavalieri, Sergio & Marco Taisch. (1996). Neural Networks in Hybrid Intelligent Manufacturing Systems. Aisberg (University of Bergamo). 591–596.4 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.