Transforming Manufacturing Performance with AI-Driven ERP Integration

Dipak Kulkarni

Abstract: These In the era of Industry 4.0, the convergence of Artificial Intelligence (AI) and Enterprise Resource Planning (ERP) systems is reshaping the manufacturing landscape. While traditional ERP platforms have long served as the backbone of enterprise operations, their static nature limits real-time responsiveness and predictive capabilities. Integrating AI into ERP systems introduces intelligent automation, predictive analytics, and adaptive decision-making that significantly enhance manufacturing efficiency, agility, and strategic planning. This paper explores the multifaceted applications of AI-enhanced ERP systems in manufacturing, with a focus on Oracle Cloud ERP as a representative platform. Key functional areas such as demand forecasting, inventory optimization, predictive maintenance, supply chain management, quality control, financial forecasting, workforce planning, and intelligent procurement are examined in detail. The study highlights how AI models-leveraging machine learning, natural language processing, and real-time data streams?enable smarter operations by forecasting trends, automating workflows, identifying anomalies, and enhancing user interactions. The paper also outlines practical benefits, including improved operational efficiency, cost savings, enhanced customer satisfaction, and greater adaptability to market changes. Finally, it addresses the challenges of AI integration-such as data quality, change management, and cybersecurity-and provides a roadmap for successful implementation. The insights presented serve as a strategic guide for manufacturers seeking to future-proof their operations through AI-driven ERP transformation.

Keywords: AI-Integrated ERP, Smart Manufacturing, Industry 4.0, Digital Transformation

How to Cite?: Dipak Kulkarni, "Transforming Manufacturing Performance with AI-Driven ERP Integration", Volume 14 Issue 5, May 2025, International Journal of Science and Research (IJSR), Pages: 1296-1299, https://www.ijsr.net/getabstract.php?paperid=SR25519233029, DOI: https://dx.doi.org/10.21275/SR25519233029