The library

Use cases that actually pay

Twelve deployments with published or vendor-reported numbers behind them — each linked to the entry that explains the mechanism.

Use case

Scrap & quality root-cause discovery

Unsupervised models trace scrap spikes to material-lot, tooling and drift interactions. Strongest published results in the category — up to −58% scrap (vendor-reported).

Use case

Predictive quality scoring

Score in-process product against final quality and catch bad runs mid-run instead of at inspection.

Use case

Golden-run replication

Mine the best historical runs and guide operators back to their settings, shift after shift.

Use case

Vibration-based failure prediction

Dedicated sensing plus labeled fault libraries on rotating assets; prescriptive diagnostics with confidence levels.

Use case

Process-signal drift detection

Failure precursors visible in process variables before any vibration change — no new hardware required.

Use case

Maintenance-plan optimization

From calendar-based to condition-based intervals; 20–50% of unplanned stops typically recovered.

Use case

Energy optimization

Find the settings and sequences that cut kWh per unit — published results around −20% on optimized lines.

Use case

Utilities & compressed-air anomaly detection

The classic invisible leak, caught as a deviation from learned utility baselines.

Use case

Material-yield optimization

Recipe industries: squeeze give-away and formulation loss against learned quality envelopes.

Use case

Automated visual inspection

Deep-learning defect classification at line speed — the single most-adopted industrial AI use case (~11% share).

Use case

Digitized quality checks

Operator-station checks with anomaly flags, feeding the same defect library as the cameras.

Use case

GenAI copilots on the ML stack

Alert explanations, auto-drafted 8D reports, natural-language historian queries — the language layer on validated ML outputs.

Common thread: models trained on the plant's own data, first insight in days, operated by the plant's own engineers. The 2026 report quantifies it.