... The rest have a 2030 problem.
Three years from now, walk a European dairy or beverage plant at 03:00.
The CIP cycle is finishing on a cream line, on a juice line, on a soft-drink filler. It is finishing because the line is clean, not because the timer says it should be. The next changeover ends the moment product meets water, not 40 seconds later with a diluted batch downstream. No one is pulling samples to the lab to confirm what the operator already saw on screen ten minutes ago. No one is calibrating a probe at this hour, because the probe doesn't drift.
This is not a pilot. It is how the plant runs. The control room calls it the Zero-Loss Factory: every CIP cycle and product transition measured in real time, every loss event visible, every avoidable loss stopped across every site in the group.
You have seen the gap. Mass-balance has been telling you for years. The lab samples that don't quite match the calculation. The yield that creeps off plan. The wastewater load that slowly climbs. The losses are not news to anyone running a modern dairy or beverage plant. The news is that you can now find them, and stop them. For a single plant, that is meaningful. Across a network, it is strategic.
Liquid losses are the largest controllable cost in dairy and beverage processing. They are also the largest controllable source of resource consumption. Across European dairy and beverage manufacturing, lost product runs 3–7% of throughput. At group scale, that is hundreds of millions of euros: in product written off, in water consumed and treated, in energy spent making batches that go to drain, in retreatment and replanning when something invisible breaks something measurable. At twenty plants on the Collo model, that is above €120M annually.
The sustainability case and the financial case are the same case. For dairy, the carbon is in the milk. Raw milk accounts for 80% of a dairy company's CO₂ footprint. For beverage, the cost is in the water. In regions where every litre matters, the cleaning cycle becomes the sustainability story. Either way, every drop preserved is emissions avoided, water saved and cost recovered. There is no trade-off to make.
AI is only as good as the data it sees. Legacy inline measurement — conductivity, turbidity, flow — reads one parameter at a time and drifts. Collo's RF-based analyser reads eight in parallel and holds calibration for years. That is the foundation. See how the inline measurement technologies compare →
At the same time, a new generation of AI learned to read a CIP cycle, anticipate a changeover interface, and act in real time. Trained on the physics of liquid processing. For the first time, what is happening inside the line is something you can see, measure and act on — ground truth from data that never existed before.
This is industrial AI that runs the Zero-Loss Factory.
Collo is the enterprise-scale industrial AI platform for dairy and beverage production. The platform delivers intelligence in two ways: Insights and Signals.
Insights shows the loss. This is the find half. A unique RF-based analyser sits inline on the process line, reading the electromagnetic fingerprint of the liquid passing through. No probes to drift. No optical windows to foul. The signal is interpreted by AI analytics trained on millions of CIP cycles and product transitions across dairy and beverage production. Where the loss is, when it happens, how much it costs. With this visibility, operators recover the value by tuning the cycles they already run. Recovering 15–25% savings on water, time and capacity.
Signals stops it. This is the remove half. The same data drives closed-loop, state-based process control. CIP cycles stop when the line is clean, not when the timer runs out. Changeovers cut to product the moment the interface arrives. Process anomalies are caught at the second they appear, not the morning after. What operators can't recover by hand stops at source, automatically, in every cycle.
This is what industrial AI looks like when it leaves the strategy deck and arrives on the production floor. It informs the existing automation. The savings appear in water, energy, chemicals, product and hours.
Valio. A cream production line at one of the Nordic region's leading dairy innovators. Existing automation insisted there was no loss. Flow meters and mass balance both clean. With Insights measuring inline, the team finally saw what had been happening for years. Changeovers ending seconds late. Water sliding into product tanks. 1–2% of every batch silently diluted. Significant annual savings followed, plus a measurable contribution to Valio's commitment to a carbon-neutral milk chain by 2035.
See more customer success stories →
First, network EBITDA. The proof points are already public. €542,000 a year from a single beverage production line. Four million litres of water and 300 hours of cleaning time recovered every year. Modelled across a multi-site network, those line-level results point to an 8–12% EBITDA uplift on treated lines. Recovered product. Reduced CIP load. Lower effluent charges. Reclaimed production hours. Roll those numbers across a multi-site network and the number changes the conversation at the board.
Second, the 2030 commitments. Most European food and beverage groups have committed to a 25% CO₂ reduction by 2030. The Zero-Loss Factory puts that target inside reach without renegotiating energy contracts or rebuilding assets. The carbon comes off the line by not making product that gets thrown away.
Third, the data compounds. Every site added sharpens what the next one sees. A group that starts in 2026 reaches 2030 with four years of network-wide learning behind it. A group that starts in 2028 cannot buy that back with hardware.
By 2030, every European dairy and beverage group will know if they were early, on time, or late. The question is not whether the Zero-Loss Factory becomes the new operating standard. It is whether your network sets it, joins it, or chases it.
That is how we turn every drop into value.
Some plants already run this way. The next ones are decided this year.
Pick your top sites. We'll model the EBITDA and CO₂ impact across them, using your data, in your context.