TY  - CONF
A1  - Kühl, Linus
A1  - Wiethölter, Jost
A1  - Dircksen, Michael
T1  - Enhancing Supply Chain Risk Identification: Analyzing the Impact of LLM  Parameters for precise Classification
T2  - Building sustainable connectivity through logistics and supply chains : proceedings of the 28th International Symposium on Logistics (ISL 2024) : 07-10th July 2024
N2  - This study investigates the impact of Large Language Model (LLM) parameters, specifically 
temperature and top P, on Supply Chain Risk Detection (SCRD). With a heightened focus 
on Supply Chain Risk Management (SCRM) using AI, the research employs a Design of 
Experiments (DoE) approach. The results reveal optimal temperature values for valid 
assessments in SCRD applications. The study emphasizes the importance of tailored LLM 
parameter settings, contributing insights for future research and practical applications in 
enhancing supply chain resilience. Suggestions for incorporating Response Surface 
Methodology (RSM) and refining the process are proposed for further investigation.
KW  - Artificial Intelligence
KW  - Large Language Model
KW  - Data Analytics
KW  - Design of Experiments
KW  - Logistics
Y1  - 2024
UR  - https://www.hb.fh-muenster.de/opus4/frontdoor/index/index/docId/18096
UR  - https://www.islconf.org/wp-content/uploads/2024/07/ISL_2024_Proceedings_Final.pdf
SN  - 978-0-85358-354-7
SP  - 197
EP  - 205
PB  - Centre for Concurrent Enterprise, Nottingham University Business School
CY  - Nottingham, UK
ER  -