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.

The TEEPTRAK approach

Machine learning in a factory starts with one data set: what each machine did.

Most use cases on this site need the same foundation: machine states, stops with reasons, cycle counts and scrap, recorded automatically and time-stamped. TEEPTRAK collects exactly that on any machine, and the plant gets live OEE from it long before a model is trained.

Machine states and cycle counts, time-stamped
Stop reasons entered by operators: the labels most data sets lack
Open export of the full history for data teams

Used in 450+ factories. At Hutchinson, OEE rose from 47% to 72%. Installed with a module wired into the electrical cabinet and a sensor at the edge of the line. No PLC changes. TEEPTRAK is not an MES: it measures, so you know what to fix first.

See how TEEPTRAK works →Map OEE data to ML features →

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.

Put it to work

Rank your use cases on what actually decides them

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