Integration of Artificial Intelligence in Production Planning: A Systematic Literature Review in Operations Management

Abstract

This study investigates the integration of artificial intelligence (AI) into production planning within operations management, highlighting its growing importance in modern industrial environments. Traditional systems such as ERP and MES increasingly struggle to meet the flexibility, responsiveness, and data-driven requirements of Industry 4.0 and emerging Industry 5.0 systems. To address this gap, a systematic literature review (SLR) was conducted to identify current AI applications, assess their benefits and challenges, and evaluate their impact on organizational performance. Results reveal that machine learning (ML) and deep learning (DL) are the most widely applied AI techniques in production planning, supporting tasks such as demand forecasting, scheduling, real-time monitoring, and resource optimization. These technologies enable more accurate decision-making, improved responsiveness, and greater efficiency in planning processes. Furthermore, AI supports autonomous systems and enhances the resilience and adaptability of production environments. However, effective implementation remains dependent on high-quality data, appropriate algorithm selection, and organizational readiness. Key challenges include data preparation, integration with existing systems, model transparency, and workforce skills. Additionally, the findings emphasize the need for cross-functional collaboration and employee upskilling to achieve successful AI adoption. Overall, AI provides substantial potential to enhance production planning performance, increase productivity, and strengthen competitiveness. At the same time, companies must address technical, organizational, and human-related factors to fully leverage AI-driven planning systems.