AI in Practice / Industry · Source-led case
TITAN — AI optimisation and predictive maintenance
Published evidence, operating questions and an evaluation plan for a comparable application.
Documented application · Source reviewed 6 November 2026
What is established
TITAN’s digitalisation overview describes AI-supported real-time production optimisation and predictive maintenance, including CemAI. It establishes application areas; it does not provide a single comparable plant-level baseline for the whole programme.
The operating problem
Maintenance teams must decide which warning deserves attention and whether an intervention is worth the disruption. A useful AI service needs to fit the maintenance schedule and the plant’s operating limits.
Illustrative evaluation workflow by AIStrategy.gr; not a disclosure of the organisation’s internal architecture.
Design a comparable pilot
Select one production line and one failure category. Reconstruct historic alerts against maintenance logs, then run a prospective advisory trial. Keep the operator’s existing authority and record why alerts were accepted or rejected.
What to measure
- Useful warning time before a verified failure
- False alerts per operating week
- Unplanned downtime under comparable load
- Maintenance and review hours
- Energy and quality effects where relevant
What the evidence cannot settle
Changes in product mix, loading and planned shutdowns can distort comparisons. Avoid counting every predicted anomaly as an avoided failure. Request asset-level event logs, missing-data rules and the cost of false alarms.
Questions for the project owner
- What is the baseline, comparison period and representative operating envelope?
- Which data are missing, and how do those gaps affect decisions?
- Who can override the system, stop the trial or correct an error?
- What results remain after integration, review, training and support costs?
Scale, revise or stop
Agree thresholds before the trial. Expand only when the responsible team can reproduce useful outcomes, explain remaining errors and sustain the operating process. Revise the design when benefits depend on exclusions that do not reflect normal work.
Editorial boundary. The primary-source account above is distinct from our proposed pilot design. Reported outcomes have not been independently audited by AIStrategy.gr.
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