Manufacturing
Analytics and automation for production lines: quality, performance, failure prediction and continuous data-driven optimisation, with per-line dashboards and alerts when the process drifts away from its usual pattern.
Analytics and automation for production lines
Production problems rarely announce themselves: rejects creep up, machines drift towards failure, OEE arrives at month end when nothing can be fixed. Captia AI applies analytics and automation to line data, covering quality, performance, failure prediction and continuous data-driven optimisation. The main pieces are operational analytics to explain line behaviour, predictive maintenance and early alerts to warn before failures, and the industrial digital twin to test changes without touching real production.
Use cases mature at different speeds: threshold alerts work from day one, analytics needs weeks of clean data, failure prediction needs history. Start with one line where the problem hurts and the data exists, validate with the production team and measure against the plant’s own indicators. If line signals are not yet integrated, the connectivity layer for manufacturing in Captia Connect covers that first.
Frequently asked questions
- Which production line problems can data analytics address?
- The three classic fronts: quality, performance and availability. Analytics helps explain why rejects rise in certain shifts, where pace is lost against standard and which patterns precede stoppages. It does not replace process knowledge; it gives it data to confirm or discard hypotheses previously argued from memory.
- How much data history do I need to predict machine failures?
- It depends on the asset and how often the failure occurs. As a rule, you need enough history for the pre-failure pattern to appear several times. That is why projects usually start with threshold and deviation alerts, which work from day one, adding predictive models once the history allows it.
- Does continuous optimisation require changing my MES or ERP?
- No. Captia AI works on the data those systems already generate, integrated into a common data layer. MES and ERP keep doing their job; the intelligence layer adds analysis, alerts and automation on top. If system integration does not exist yet, Captia’s connectivity area solves that part first.
- How do you measure whether a manufacturing AI project works?
- With the indicators the plant already uses: reject rate, line availability, plan compliance, incident response time. Before starting, you agree which indicator must move and by when. If the pilot does not move it, the project is rethought; the technology is not the goal, the improvement is.