This is where we train purpose-built machine learning models for specific use cases. Once trained, the models are deployed within the platform so that they can run automatically in production, analyzing new data in real time and delivering actionable predictions directly to operations.
By using this layered approach of applying context first, then the intelligence ensures that AI is applied to data that actually makes sense, so that the resulting decisions can have as much impact as possible on the shop floor.
AW: So how do MES and industrial data platforms — working in tandem and then leveraging AI — improve production and operational reliability and minimize downtime?
FAL: When MES and data platforms work together and are enhanced by AI, they form a powerful foundation for improving your personal reliability and minimizing downtime.
As an example, the MES ensures consistent execution by managing the production orders, enforcing processes, and handling real time control and traceability. The data platform continuously ingests these high frequency data from machine sensors and systems and enriches it with MES context.
The AI and machine learning models trained on this rich data set can predict potential failures before they occur. For example, the models can identify process deviations or root causes of downtime to optimize schedules and settings to avoid excess stress on equipment.
But it goes further than this. These models can also alert operators or technicians before a problem is visible. They can suggest parameter settings to trigger maintenance activities or automatically trigger responses like stopping a machine, re-routing production or executing a specific MES transaction.
This closes the loop from insight to action — sometimes even autonomously — and the result is a shift from a reactive to proactive and even autonomous reliability management to reduce plant downtime, extend asset life and improve production stability.
