Recommender Systems for Capability Matchmaking

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Standard

Recommender Systems for Capability Matchmaking. / Badewitz, Wolfgang; Stamer, Florian; Linzbach, Johannes et al.
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 SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Harvard

Badewitz, W, Stamer, F, Linzbach, J, Dann, D, Weinhardt, C & Lichtenberger, S 2021, Recommender Systems for Capability Matchmaking. in JPA Almeida, D Bork, G Guizzardi, M Montali, HA Proper & TP Sales (Hrsg.), Proceedings - 2021 IEEE 23rd Conference on Business Informatics: Volume 2 - CBI Forum and Workshop Papers. IEEE Conference on Business Informatics, Bd. 2, Institute of Electrical and Electronics Engineers Inc., S. 87-96, 23rd IEEE Conference on Business Informatics, Bozen, Italien, 01.09.21. https://doi.org/10.1109/CBI52690.2021.10059

APA

Badewitz, W., Stamer, F., Linzbach, J., Dann, D., Weinhardt, C., & Lichtenberger, S. (2021). Recommender Systems for Capability Matchmaking. In J. P. A. Almeida, D. Bork, G. Guizzardi, M. Montali, H. A. Proper, & T. P. Sales (Hrsg.), Proceedings - 2021 IEEE 23rd Conference on Business Informatics: Volume 2 - CBI Forum and Workshop Papers (S. 87-96). (IEEE Conference on Business Informatics; Band 2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBI52690.2021.10059

Vancouver

Badewitz W, Stamer F, Linzbach J, Dann D, Weinhardt C, Lichtenberger S. Recommender Systems for Capability Matchmaking. in Almeida JPA, Bork D, Guizzardi G, Montali M, Proper HA, Sales TP, Hrsg., Proceedings - 2021 IEEE 23rd Conference on Business Informatics: Volume 2 - CBI Forum and Workshop Papers. Institute of Electrical and Electronics Engineers Inc. 2021. S. 87-96. (IEEE Conference on Business Informatics). doi: 10.1109/CBI52690.2021.10059

Bibtex

@inbook{cf099f0f3b224dfdb990fee51eecb543,
title = "Recommender Systems for Capability Matchmaking",
abstract = "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.",
keywords = "Capability Matchmaking, Decision Support System, Machine Selection, Recommender System, Engineering",
author = "Wolfgang Badewitz and Florian Stamer and Johannes Linzbach and David Dann and Christof Weinhardt and Sebastian Lichtenberger",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 23rd IEEE Conference on Business Informatics - CBI 2021, CBI 2021 ; Conference date: 01-09-2021 Through 03-09-2021",
year = "2021",
doi = "10.1109/CBI52690.2021.10059",
language = "English",
isbn = "978-1-6654-2070-9",
series = "IEEE Conference on Business Informatics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "87--96",
editor = "Almeida, {Joao Paulo A.} and Dominik Bork and Giancarlo Guizzardi and Marco Montali and Proper, {Henderik A.} and Sales, {Tiago P.}",
booktitle = "Proceedings - 2021 IEEE 23rd Conference on Business Informatics",
address = "United States",

}

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 -

DOI