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Sachsenmilch
Food & Beverage · Germany
Pump failure spotted weeks ahead
Siemens Senseye detects an ageing pump at Sachsenmilch weeks before failure, pilot repaid in the low six-figure range
Cloud AI and ML predictive maintenance running on existing plant sensors with no new hardware required. The Senseye Maintenance Copilot lets engineers query machine condition in natural language. Pilot investment fully recovered through a single prevented pump failure.
Friction
Sachsenmilch processes 4.7 million litres of fresh milk per day, 365 days a year. An unplanned production stop in a non-stop dairy facility disrupts the entire supply chain: 170 trucks per day, strict quality standards, and perishable product with no buffer. Reactive maintenance meant expensive calamities.
Breakthrough
Siemens Senseye Predictive Maintenance runs on existing plant historians with no new sensors or hardware needed. ML models detect anomalies and predict failure before it occurs. The Maintenance Copilot provides a natural language interface for engineers to query machine condition. Planned integration with SAP Plant Maintenance for automated work order generation.
Impact
Pilot fully repaid with savings in the low six-figure range (confirmed on record by Technical Manager Roland Ziepel). Early detection of ageing pump prevented a costly unplanned production stop. Planned pump replacement resulted in significantly shorter planned downtime versus emergency repair. High machine availability maintained in a 365-day operation.
Unlock the full analysis with breakthrough, impact, what made it smart and its technical approach below!
Problem
In food processing, unplanned downtime creates a cascading problem: the facility must stop, perishable product cannot be held indefinitely, quality processes are disrupted, and customer supply commitments are broken. Unlike manufacturing of durable goods, you cannot just pause and restart. The product is already in process.
What made it smart
The fully brownfield deployment, running on existing plant historians with no new hardware, means the entire value was captured from an installation investment that was already made. This is the model for mid-market industrial AI: extract value from existing sensor infrastructure before investing in new hardware.
Technical approach
Senseye Predictive Maintenance ingests data from existing plant historians including vibration, temperature, and operational parameters via a cloud API. ML models establish baseline behaviour per asset and flag deviations. Remaining useful life estimates generate maintenance scheduling recommendations. Maintenance Copilot provides engineers with natural language access. Planned integration with SAP PM for automatic work order creation.
Strategic lesson
The barrier to predictive maintenance in most industrial facilities is not technology. It is the assumption that new hardware is required. Most plants have years of sensor data in existing historians. The intelligence is already there; it just needs a model on top of it.
Reflection question
What sensor data does your organisation already collect but not fully analyse, and what would it mean if that data could predict failures rather than just record them?