Decision-making in smart manufacturing leverages four main analytics approaches—each with distinct data requirements:
- Rule-based systems use predefined logic (e.g., if–then conditions) and require no data. They are ideal for automating repetitive tasks and enforcing workflows early in digitalization.
- Statistical analysis utilizes structured historical data to identify trends, variability, or patterns. These methods require moderate data volumes and are used in quality control, maintenance, and inventory planning.
- Narrow AI (e.g., machine learning, deep learning) solves well-defined tasks like predictive maintenance or anomaly detection. It needs large, labeled datasets to learn from and perform effectively.
- Generative AI (GenAI) requires the highest data volume. It synthesizes diverse, integrated data across domains—such as production, quality, CRM, or supply chains—to produce insights, content, or adaptive decisions. This method depends on digital maturity and system interoperability.
To enable these analytics layers, data must be continuously generated, transmitted, and processed. This requires a foundational infrastructure known as a Cyber-Physical System (CPS). A CPS connects operational technology (OT) with information technology (IT), integrating systems like ERP, MES, SCADA, and PLCs. It enables real-time monitoring, traceability, and cross-system communication.
Without CPS, data remains siloed and fragmented—limiting the scope and effectiveness of analytics, especially for AI and GenAI. A mature CPS ensures scalable, structured, and real-time data flow, forming the backbone of intelligent manufacturing.
Are you truly ready for AI / GAI or should you first build the foundation?
