AI in Forecasting: A Missing Component in ERP Systems?

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.

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Publication Details

Journal
Journal of the Association for Information Systems
Published
2025-08-15
Primary Topic
Military Technology and Strategies
Type
article
Field-Weighted Citation Impact
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article

AI in Forecasting: A Missing Component in ERP Systems?

Journal of the Association for Information Systems
Military Technology and Strategies
article

AI in Forecasting: A Missing Component in ERP Systems?

article en

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.

Journal of the Association for Information Systems
Westsächsische Hochschule Zwickau (DE)
Openalex Percentile: Top 36%
Military Technology and Strategies
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AI in Forecasting: A Missing Component in ERP Systems? · Journal of the Association for Information Systems (2025) | TGRS Research Map | TGRS