Industry & Manufacturing.

Operational continuity

Failures, quality defects, consumption drifts: in industry, AI creates value by anticipating operational deviations before they affect margin, service or capacity.

• Unplanned downtime on critical equipment;
• Quality defects detected too late or inconsistently;
• Energy consumption, scrap or material losses that are difficult to explain;
• Maintenance / industrial availability trade-offs insufficiently supported by data.

01: Predictive Maintenance & Operational Continuity

Anticipating failures helps improve the trade-off between preventive intervention, downtime cost and industrial availability.

 In an industrial case, a predictive model detected 26 failures before occurrence out of 39 observed failures, with 8 false alerts at the selected threshold. Potential savings were estimated at €520k, based on the client’s cost assumptions.

02: Visual quality control

Automated inspection can improve defect detection while maintaining human judgement on ambiguous or critical cases.

In a visual classification pilot, the model detected 79% of actual defects on the test set, with no false rejection observed within that scope, and an estimated economic impact of €90k per year based on client assumptions.

03: Consumption drifts and material losses

Models can help identify the production conditions that precede energy drift, scrap or material loss.

The objective is not to model the entire factory, but to isolate actionable signals on the equipment, lines or references where the economic impact is material.

When should a diagnosis be initiated?

A diagnosis is relevant when three conditions are met:

• production, quality or maintenance history is available;
• the event to anticipate is clearly defined: failure, defect, scrap, drift;
• the economic cost is sufficient to justify predictive intervention.

Qualify an industrial use case