Industrial AI for coffee, culinary, snacks and foodservice groups
A food group that roasts coffee, cans vegetables, produces pasta and couscous and also supplies its own restaurants doesn't run one factory: it runs four technologically distinct processes under a single manufacturing directorate. Roasting, extrusion and drying, canning and retort, and central kitchen. Each with its own vocabulary, its own critical machines and its own failure modes.
When those plants have also been brought in through acquisition, in different countries and from different owners, the problem that defines the sub-sector appears: every plant explains itself in its own language. It measures waste, yield and cost per kilo on its own definitions, on its own ERP and with its own paper logs. The practical consequence is that the group's factories cannot be compared with each other, and every integration takes months to produce visibility.
These pages hold twelve real industrial AI cases applied to that reality: capturing what is lost today in each process without changing anyone's habits, stitching together the systems that already exist instead of replacing them, putting machine vision where the human eye can't keep up with line speed, and letting the audit evidence pack assemble itself. The final case coordinates all of them so the plants start speaking one operational language — without replacing a single ERP.
Capture without changing habits: photo, voice, email, integrations
The roast log, without paper
Payback 4-9 months · from 4-8 h of latency to second zero
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The old roaster starts talking
Payback 5-10 months · defers the replacement CAPEX
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The restaurant's alert reaches production
Payback 3-7 months · from hours of latency to seconds
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The shift lead dictates while walking
Enabling piece · knowledge stops dying at the handover
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Nothing enters the ERP without a sign-off
Enabling piece · without it nothing is signed in committee
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The lab spreadsheet feeds the ERP
50-80% fewer quality licenses · payback 4-9 months
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Green coffee received with one photo
From an hour of blocking to minutes · payback 3-8 months
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The coder stops being typed
Payback 3-6 months · the fastest return in the matrix
See the case →Machine vision on the line, in real time
Agents that plan, document and coordinate
More cases from this series
- From two weeks reconstructing traceability to zeroEvery critical change generates its dossier: before/after photos, decisions and signatures, reorganised…
- Ten acquired factories, one operational languageA capture and normalisation layer over the systems that already exist makes M&A-acquired factories speak…
- Lot, date and allergens stop depending on someone typingLot, date and allergens travel from the active order to the coder via API. Zero typing on the machine…
- Goods-in stops blocking the dock manager for an hourPhotograph the docket and the sack labels: iLEAN extracts lots and origin certificates, the manager…
- What the shift lead knows stops evaporating every eight hoursThe shift lead dictates issues while walking between retort and filler; the AI structures them, links…
- The shift quality sheet stops being retyped by handiLEAN reads the quality sheet as it is saved, normalises it to the group model and, after sign-off,…
Which of these twelve pieces would solve your problem first? An AI Discovery session sorts that out in a morning." ---
We work on your plant's real data, not ours. Assessment with no commitment.
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