Manufacturing data systems have gone through several major changes, evolving from transaction-based systems to big data platforms and now to generative AI solutions. This new phase is transforming how factories handle data, shifting the focus from centralized architectures to hybrid approaches that leverage both edge and cloud systems. Generative AI offers opportunities to simplify operations in some areas while adding complexity in others, all to achieve industry-leading results.
Successive Generations of Data Systems
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Early Factory Software
In the early days of factory data systems, enterprise software like ERP (e.g., SAP or Infor) and MES (e.g., Dassault Apriso, Siemens Camstar) were the backbone of operations. These tools managed workflows and provided dashboards and reports for human decision-making. Data was smaller in scale, collected less frequently, and primarily human-focused. While these systems were critical for managing operations, they weren’t designed to handle the increasing need for large-scale data analysis-creating an opportunity for IoT-based infrastructures to fill the gap.
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Machine Data for Engineers
Machine data, such as PLC tags, process data, and logs, have been essential for factory operations. Traditionally used by process engineers through SCADA systems, this data became increasingly valuable with the rise of IoT. Factories began extracting machine data to unified data lakes, allowing data scientists to build physics-based and statistical models to optimize factory performance.
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Big Data for Machine Learning
The third generation ushered in big data. Factories started collecting massive amounts of structured data into platforms like AWS, Azure, or Snowflake. These centralized data lakes enabled machine learning teams to train predictive models for anomaly detection and predictive maintenance tasks. For example, ML models could predict equipment failures weeks in advance by analyzing complex relationships in high-dimensional data. However, centralizing and managing such vast datasets introduced significant complexity.
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Generative AI and Smarter Workflows
The latest generation-generative AI-is a game-changer. Unlike traditional ML models that require extensive training on centralized data, generative AI leverages pre-trained models to generate insights and solutions. Factories can now use niche physics-based and ML models as triggers for agentic workflows. Generative AI excels at interpreting previously “unstructured” data-like dashboards, audio recordings, and videos-and integrating it into decision-making processes. These capabilities enable real-time root cause analysis and guidance directly on the factory floor, reducing the need for centralized IoT initiatives to manage such data formats.
Moving Past Old Systems
Generative AI allows factories to leapfrog older systems, much like skipping landlines for mobile technology. Here are key strategies for modernizing data systems:
- Centralized Digital Twins: Factories should create centralized digital twins–comprehensive models that represent the enterprise–to coordinate actions like real-time problem-solving and process optimization. These models provide a unified view while supporting local flexibility.
- Edge Data Utilization: Generative AI doesn’t require all data to be centralized. Pre-trained models can work effectively with edge data, including operator voice memos, dashboards, and machine logs. Keeping such data local reduces delays and enables faster decision-making.
- Balancing Edge and Centralization: Successful systems combine centralized data for long-term planning and edge data for immediate, localized actions. Generative AI thrives in this hybrid setup, pulling insights from both to drive smarter workflows.
Why Generative AI Matters for Factories
Generative AI bridges the gap between human intuition and machine efficiency. It processes human-like data, such as reports and operator interactions, to solve problems and optimize processes. By turning static data systems into dynamic tools, generative AI helps factories adapt to challenges in real time.
Factories no longer need to store data solely for remote analysis. Generative AI enables the creation of intelligent workflows that automatically address issues and improve efficiency. This new approach empowers factories to respond faster and more effectively to operational demands.
Conclusion
Generative AI is reshaping manufacturing data systems. By integrating centralized digital twin models with rich edge data, factories can unlock new levels of efficiency and insight. Data architects must embrace this hybrid approach, balancing advanced analytics with real-time workflows to create agile, AI-driven systems that meet both current and future needs.