Process Chain for Artificial Intelligence-Based Demand Forecasting and Procurement Scheduling

Maximilian Hohn, Philipp Maximilian Sieberg

2025

Abstract

This position paper introduces a conceptual general process chain for leveraging artificial intelligence (AI) in demand prediction and procurement scheduling for small and medium-sized enterprises (SME). While AI offers significant advantages, such as reducing inventory costs, improving delivery reliability, and optimizing logistics, its adoption in SME is hindered by limited expertise, restricted access to AI tools, and psychological barriers like trust and acceptance. The proposed framework integrates probabilistic modeling, clustering algorithms, feature extraction methods and temperature scaling to enhance prediction accuracy and efficiency. By aggregating demand forecasts, the system enables risk-adjusted and cashflow-optimized scheduling. A preliminary result is presented, demonstrating robust predictions within confidence intervals. While the findings are preliminary, this paper highlights the transformative potential of AI in SME scheduling and outlines future research directions, including model optimization and the integration of explainable AI methods to further enhance traceability and user acceptance.

Paper Citation

in Harvard Style

Hohn M. and Sieberg P. (2025). Process Chain for Artificial Intelligence-Based Demand Forecasting and Procurement Scheduling. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 724-731. DOI: 10.5220/0013647700003967

in Bibtex Style

@conference{data25,
author={Maximilian Hohn and Philipp Sieberg},
title={Process Chain for Artificial Intelligence-Based Demand Forecasting and Procurement Scheduling},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={724-731},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013647700003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Process Chain for Artificial Intelligence-Based Demand Forecasting and Procurement Scheduling
SN - 978-989-758-758-0
AU - Hohn M.
AU - Sieberg P.
PY - 2025
SP - 724
EP - 731
DO - 10.5220/0013647700003967
PB - SciTePress