Pernod Ricard: Scaling operational intelligence across 30 facilities
Altizon’s DFX Platform unified Pernod Ricard’s production, quality, and sustainability data by replacing fragmented, paper-based…
Used in the right order, AI helps a plant catch losses earlier, hold quality steadier, and act on problems as they happen instead of after the shift. The payoff depends on a trusted production record for the AI to work from. That record is what DFX, an AI-native MES, is built to create as the line runs.
The AI worth the investment does two things at once: creates real value and reaches across the whole operation. That is why agentic AI, acting on one shared record, pays off where single-point tools cannot.
A machine vision system inspects dimensions, labels, seals, or foreign objects and rejects the units that fail. Valuable for quality, but single-purpose: it improves one inspection point.
Predictive maintenance reads sensor data, often vibration, to flag a machine before it fails. The payback is real but narrow: a few critical assets, not every machine, and it is oversold as the place to start. It works best on a trusted production record, where it shifts maintenance from calendar-based to condition-based.
Process analytics reads live process data to find where quality drifts or yield slips. The value pays off on the few steps with variable chemistry, such as cooking and pasteurizing, and adds little where the process is stable (blending) or biological (fermentation). Much of the plant is mechanical, like filling and packing, with nothing to tune. Point it at the chemistry, not the whole line.
Energy optimization is similar and just as local. The biggest users, refrigeration, compressed air, and heating, are already known and predictable, so there is little to uncover. It pays off most when consumption is read against the production record, indexed to units produced.
A digital twin is a virtual model of a machine or line that stays in sync with the real thing through live data. Its job is to mirror the equipment now and surface drift early, before it becomes scrap or a stop.
The value is real but overstated. The full simulation promise, testing a change before you run the batch, rarely holds: many SKUs and product mixes make the line hard to model. A twin is best used to watch and anticipate, and it gives agents a foundation to act on.
Autonomous agents model how an experienced person would act on the data in front of them. Built on large language models, they hold context, reason through messy data, and handle situations no one wrote a rule for, working across the whole line rather than one point.
An agent can sequence the next run, work out why a line slowed, or recommend holding or releasing a batch, while people keep the judgment calls. Value and reach meet here, so the payoff is the broadest of the four. But agents only work on a record they can trust, which is why DFX, the AI-native MES , runs them on the live production record it captures as the line runs.
AI pays off in a specific order. First, the plant floor becomes one reliable record. Then AI is layered on by payoff, from local tools to agents that work across the whole line.
Network the equipment and integrate the OT and IT sides. A unified namespace gives every machine and system one common way to publish its data.
Machine signals are only half the picture. Capture quality checks, maintenance checks, and operator observations digitally.
Machine and human data have to exist in one central record that scales across lines and plants, not local systems tied to a single line or site. This is the MES layer, and an AI-native MES like DFX is built to be that record from day one, so the AI you add later has a trusted foundation to act on.
Add line-level systems for specific quality issues such as fill, seal, label, and foreign-object detection. Choose ones that connect and share data rather than sit in a silo.
Use digital twins to mirror the lines and surface drift on the steps with a real, continuous process such as thermal and mixing stages. Treat a twin as a way to watch and anticipate, not to run full simulations.
Deploy agents to support teams on day-to-day decisions, reasoning through complex, changing situations rather than routine tasks. People still make the judgment calls, with agents doing the legwork and surfacing the options, all on the same production record the earlier steps put in place.
Ingredients are natural and vary from lot to lot, so the same recipe can behave differently from one batch to the next. There is no fixed normal to model against, and the AI has to keep up with conditions that change on their own.
Altizon’s DFX Platform unified Pernod Ricard’s production, quality, and sustainability data by replacing fragmented, paper-based…
Altizon’s DFX Platform helped large beverages major connect multi-OEM bottling lines and utilities into a…
We will connect a line, build the record from your machines, and show you the daily decisions AI can take on it.