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Manufacturing data, explained in the language of the plant

Not a data-vendor blog. How decisions actually get made across daily, weekly, monthly and quarterly cycles — and what has to be true of your data models, architecture and practices before those decisions get better.

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If any of this sounds like your plant, you are in the right place

These are data problems wearing operational clothes. Each one has a write-up below.

The morning meeting starts by reconciling numbers

Production, maintenance and procurement each arrive with a different version of the same week. The decision gets the last ten minutes.

The schedule lives in a twelve-tab spreadsheet

One planner owns the logic. No version history, no tests, no handover — and every downstream system inherits its errors.

Calls are made from experience, not evidence

Usually correct, never written down, unavailable at 2am and gone when the person retires.

CI, IT and re-engineering work the same loss separately

Three definitions, three funding lines, three roadmaps — and the downtime is still there next quarter.

Dashboards exist, trust does not

Nobody can show where a number came from in under a minute, so everyone keeps their own copy.

Every question needs a project

The answer takes a quarter to build and arrives after the decision it was meant to inform.

Topics

six threads, written for manufacturing people

Data practices and quality

The unglamorous habits that decide whether the shop floor trusts a number: naming, validation, freshness, warnings, ownership.

Analytics and data science

Where models pay, where rules win, and what has to exist before either one changes a decision on the floor.

Plant intelligence notes

One short email a month: rollout playbooks, connection guides and what actually converts a pilot into production use. No product announcements.

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Reading is cheap. Connect one source and see your own number.

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