Recommender Systems for Capability Matchmaking
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Standard
Proceedings - 2021 IEEE 23rd Conference on Business Informatics: Volume 2 - CBI Forum and Workshop Papers. Hrsg. / Joao Paulo A. Almeida; Dominik Bork; Giancarlo Guizzardi; Marco Montali; Henderik A. Proper; Tiago P. Sales. Institute of Electrical and Electronics Engineers Inc., 2021. S. 87-96 (IEEE Conference on Business Informatics; Band 2).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - CHAP
T1 - Recommender Systems for Capability Matchmaking
AU - Badewitz, Wolfgang
AU - Stamer, Florian
AU - Linzbach, Johannes
AU - Dann, David
AU - Weinhardt, Christof
AU - Lichtenberger, Sebastian
N1 - Conference code: 23
PY - 2021
Y1 - 2021
N2 - Supply chain planning in global production networks is a very difficult task due to the high diversity of products and machines and the immense number of possible configurations. An important task in this area is capability matchmaking: finding machines that are capable to produce specific parts. Today, this is done by experienced engineers who have the necessary knowledge to assess the feasibility and efficiency of solutions. However, they have limited knowledge of all available machines in the network and are strongly influenced by their personal familiarity with specific products, machines and locations. We present a decision support system that expands the search space, thereby facilitating the process and ultimately improving the implemented solution. To this end, we built an implicit recommender system that predicts possible machine types for parts based on their historical production patterns. This approach constitutes an effective and lightweight option for capability matchmaking in brown-field settings.
AB - Supply chain planning in global production networks is a very difficult task due to the high diversity of products and machines and the immense number of possible configurations. An important task in this area is capability matchmaking: finding machines that are capable to produce specific parts. Today, this is done by experienced engineers who have the necessary knowledge to assess the feasibility and efficiency of solutions. However, they have limited knowledge of all available machines in the network and are strongly influenced by their personal familiarity with specific products, machines and locations. We present a decision support system that expands the search space, thereby facilitating the process and ultimately improving the implemented solution. To this end, we built an implicit recommender system that predicts possible machine types for parts based on their historical production patterns. This approach constitutes an effective and lightweight option for capability matchmaking in brown-field settings.
KW - Capability Matchmaking
KW - Decision Support System
KW - Machine Selection
KW - Recommender System
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85123570235&partnerID=8YFLogxK
U2 - 10.1109/CBI52690.2021.10059
DO - 10.1109/CBI52690.2021.10059
M3 - Article in conference proceedings
AN - SCOPUS:85123570235
SN - 978-1-6654-2070-9
T3 - IEEE Conference on Business Informatics
SP - 87
EP - 96
BT - Proceedings - 2021 IEEE 23rd Conference on Business Informatics
A2 - Almeida, Joao Paulo A.
A2 - Bork, Dominik
A2 - Guizzardi, Giancarlo
A2 - Montali, Marco
A2 - Proper, Henderik A.
A2 - Sales, Tiago P.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE Conference on Business Informatics - CBI 2021
Y2 - 1 September 2021 through 3 September 2021
ER -