Case study · Food & Beverages

The plant that put artificial intelligence into every kilowatt

Real time sensors, AI that anticipates breakdowns, and models that optimize efficiency. Lower cost, fewer stoppages, and a carbon footprint that is finally measurable.

AI predictive maintenance

Real time energy analytics (IoT)

Measurable sustainability (ESG)

Where it all started

It produced its own energy, but ran it blind and on the back of breakdowns

A large scale food manufacturer produces at scale (refrigeration, cooking, processing), and energy is one of its biggest cost items. To reduce that dependency, it generates part of its own energy through cogeneration.

But that cogeneration ran like a black box. Performance data lived scattered and was reviewed too late. Engine breakdowns came by surprise, with unplanned stoppages that sent costs soaring. And the carbon footprint, increasingly demanded, was almost impossible to calculate rigorously.

In a context of expensive, volatile energy, and with sustainability already on the regulatory agenda, running blind had stopped being an option.

The challenge

Cogeneration data scattered and reviewed too late, with no real time view of performance.

Engine breakdowns by surprise, with unplanned stoppages and soaring cost.

Energy efficiency managed by intuition, with no forecasting or optimization.

Energy cost hard to attribute to each process in a market of volatile prices.

Carbon footprint and ESG targets impossible to measure and prove with consolidated data.

What we proposed

Connect the plants' sensors to a modern data platform that unifies everything in real time. On top of it, predictive maintenance AI that anticipates breakdowns before they stop production, and predictive and prescriptive models that not only measure efficiency but recommend how to operate to maximize it. And, above that, sustainability reporting that turns the carbon footprint into a reliable, auditable figure.

The shift

From reacting to breakdowns to anticipating them with AI

What used to be endured (stoppages by surprise, energy run blind, sustainability with no data) becomes something to anticipate. AI does not stop at what has happened: it predicts what is going to happen and recommends what to do.

Before

Cogeneration performance reviewed too late and blind

Breakdowns by surprise, with unplanned stoppages

Efficiency managed by intuition, with no optimization

Carbon footprint impossible to measure rigorously

Now

Each plant's performance in real time, data point by data point

AI that anticipates breakdowns before they stop production

Models that prescribe the optimal operating point

Carbon footprint measured, auditable and ready for ESG reporting

IoT sensors on a real time platform

Predictive maintenance AI and prescriptive models

1 %

Sustainability reporting (carbon footprint)

The value generated

What changes when energy becomes intelligent

Fewer stoppages, more production

Predictive maintenance anticipates breakdowns before they halt the plant.

Efficiency optimized, not just measured

Prescriptive models recommend how to operate to get the most out of every kilowatt.

Energy in real time

Performance is visible instantly, with the sensors connected to the platform.

Cost under control

Every process knows its consumption and its cost, even with volatile prices.

Sustainability you can prove

The carbon footprint is measured and audited, ready for ESG reporting.

Replicable and scalable

The same approach works for any plant or energy intensive industry.

What the client says

Before, we waited for an engine to fail and then reacted. Now AI warns us beforehand, we operate at the optimal point, and we measure our footprint with data. It is a different way of running the plant.

Industrial Management

Food manufacturer / Food & Beverage sector

F&Q

Frequently asked questions

What is AI predictive maintenance?

It is the use of AI models on equipment data to anticipate a breakdown before it happens. Instead of repairing when something breaks, you act beforehand, avoiding unplanned stoppages. At BertIA, an AI and data consultancy based in Barcelona, we apply it in the food industry, where a cogeneration stoppage is a direct cost.

What is the difference between monitoring and prescribing?

Monitoring is seeing what is happening; predicting is knowing what is going to happen; prescribing is recommending what to do. Prescriptive models go beyond showing efficiency: they recommend the operating point that maximizes it.

Does it help with sustainability regulation?

Yes. Measuring the carbon footprint with consolidated, auditable data is the foundation of the ESG reporting that European regulation already requires. It turns an obligation into a reliable figure and a management advantage.

Do you have to install new machinery?

No. The solution builds on the existing plants and sensors, connecting their data to a modern platform with no new works and no need to stop production.

Is it replicable in other industries?

Absolutely. Unifying energy data in real time, applying predictive AI and optimizing performance is replicable in any energy intensive industry, inside and outside food and beverage.