Dircksen, Michael
Refine
Year of publication
Document Type
- Part of a Book (16)
- Article in Conference Proceedings (5)
- Article (4)
- Book (3)
- Contribution to a Periodical (2)
Has Fulltext
- no (30)
Is part of the Bibliography
- no (30)
Keywords
- Logistik (3)
- Transport (2)
- 3D-Druck (1)
- 4PL (1)
- Additive Fertigung (1)
- Artificial Intelligence (1)
- Artificial Intellogence (1)
- CO2-Emissionen (1)
- Data Analytics (1)
- Design of Experiments (1)
Institute
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.
