Applying Artificial Intelligence in Industrial Logistics: Insights from Standard-Based System Integration

The rapid development of Artificial Intelligence (AI) and Machine Learning (ML) technologies offers significant opportunities across various industrial domains, particularly in logistics, including procurement, accounting, manufacturing, production support, and maintenance systems. Leveraging standardized frameworks, such as ISA-95 (2003), companies can build robust production planning models that provide a structured foundation for integrating AI-based solutions. These models can be populated with operational data, enabling the training of AI models to detect anomalies such as data inconsistencies or accounting errors, depending on the specific training objectives. Given the standardized parameter handling prescribed by ISA-95, these models are inherently compatible with integration into any manufacturing execution system, regardless of vendor. While updated standards and advanced modeling tools are often commercially restricted, the core principles remain applicable, supporting scalable and interoperable AI deployments. This paper explores current applications, best practices, and the role of standardization in facilitating the adoption of AI/ML technologies in industrial logistics and production environments.