Year: 2025
Type: article
Abstract: This paper investigates the current state and potential of Artificial Intelligence (AI) in forecasting within Enterprise Resource Planning (ERP) systems. While AI-driven forecasting offers significant advantages in accuracy and insight generation, its adoption within ERP platforms remains limited. We compare traditional forecasting models with advanced machine learning approaches across major ERP systems, revealing a significant gap in the integration of modern techniques. A case study demonstrates the substantial performance improvements achievable through AI-driven forecasting. By leveraging feature engineering and advanced models like XGBoost, we show significant gains in forecast accuracy compared to traditional methods. The findings highlight the transformative potential of AI in optimizing supply chains and improving decision-making within ERP systems, advocating for greater integration of machine learning techniques to unlock the full potential of AI-powered forecasting.
Authors: Junghans, Sebastian, Neumann, Tim, Teich, Tobias
Institution: Westsächsische Hochschule Zwickau
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7035865363
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