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- Beitrag in einer Konferenzveröffentlichung (6) (entfernen)
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- Artificial Intelligence (2)
- Logistics (2)
- Analytics (1)
- Chatbot (1)
- DSRP (1)
- Data Analytics (1)
- Design of Experiments (1)
- Generative AI (1)
- Large Language Model (1)
- Large Language Models (1)
- Mapping (1)
AI-based chatbots as enabler for efficient external knowledge management in public administration
(2024)
This study addresses the pressing issue of staff shortages in German public administrations through the lens of digitalization, focusing on the potential of AI-based chatbots to solve this problem by replacing human labour. Employing a Design Science Research Process (DSRP) methodology, the research synthesizes theoretical foundations and regulatory frameworks to develop a robust chatbot concept. The artifact presented is a comprehensive architectural framework integrating user-centric design, linguistic processing, and regulatory compliance. The proposed artifact navigates complex federal structures and diverse IT infrastructures, promoting accessibility and inclusivity. Implications suggest enhanced efficiency and accessibility in public service delivery for potentially increasing citizen satisfaction and decreasing employee workload. The study underscores the importance of legal compliance and the evolving regulatory landscape in AI deployment. Future research will involve prototyping and evaluating the artifact's performance and applicability throughout the course of the DSRP, thus contributing to the advancement of digital transformation in public administrations.
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.
Against the setting of an increasing need for innovation and low margins, companies in the logistics
sector are facing highly competitive pressure. One field with high potential for optimization lies within
damage quotas. The use of big data analytics or data mining represents a promising approach to face
this challenge. However, within supply chain management, data mining is hardly being researched on
regarding damage quotas and thus not being utilized to its full possible extend. At the current time it
seems to predominantly be used for route and utilization optimization while the analysis of delivery
damages is hardly considered.
The aim of this research is therefore to showcase an initial approach for data mining in logistics to predict
delivery damage probabilities and to validate this by means of a multiple case study research. To create
a sound basis for evaluation, the groundwork is laid out based on CRISP-DM by the analysis of reference
data (German road-cargo market).
As a central result it is noted that data mining can systematically be used to help reducing the damages
by forecasting the probabilities of damages occurring during transport in dependence of different factors.
The approach can be utilized across different markets as long as sufficient data tracking delivery
damages is being collected within a company. Challenges arise in the field of air- and sea-freight.