Free tool

Rank your ML use cases on what actually decides them

Data availability, annual value, build effort and operational risk: weighted, scored out of 100, with eight common industrial use cases already seeded. Data availability carries the most weight because it is the one column you cannot buy your way out of.

Done: your download is ready.

Download the Matrix (.xlsx)

Background reading: the use-case library and the encyclopedia entries.

What’s inside

Eight seeded industrial use cases
Four weighted criteria, scored 1-5
Automatic priority score out of 100
Colour banding so the shortlist is obvious
A reading guide for what each band means

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 700+ 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 →