What our customers say

We produce gearboxes for wind turbines so a single scrap can mean a very expensive loss.

We use Smart Anna as a key component to collect CNC data. The focus is parameter validation as a closed loop, aiming at fewer operator errors and scrap cost reduction.

What we valued most was the team's agility. When something came up, it was quickly solved, fixed, or adjusted. In the meantime, user management is embedded in our ecosystem, which helps us move forward. The groundwork was done over the previous years. Now the focus is implementation across sites.

In practice, it helps us steer operator interaction: if a parameter is out of spec, this is highlighted to the operator. It is also a plus that we can look back at data for troubleshooting and process analysis.

We are rolling it out in Tianjin (China), followed by Coimbatore (India).

Our pallet-system CNC machines can run unattended overnight, but only if the process stays stable. When something went wrong, we lacked a clear, shared view of what actually happened. That made discussions slow and sometimes subjective.

We needed objective visibility into machine performance and interruptions. Smart Anna gives us a factual timeline of how the night run went, so we can focus immediately on the real causes and the right corrective actions.

We use Smart Anna as a practical daily follow-up tool. Each morning, we review how long the machine ran, where it stopped, and what triggered the interruption. Then we follow up with the operator involved. Because the data is transparent, conversations become constructive and action-oriented instead of defensive. We started with a solid baseline setup and we are progressively refining the interpretation of specific edge cases, for example pallet-control cycles that look like runtime while no actual machining takes place.

Downtime reasons are logged and can be reviewed per shift. This creates a consistent dataset for continuous improvement. We also use Smart Anna to estimate realistic cycle times, including acceleration, deceleration, and frequent tool changes, which improves planning and expectation management.

We see clear potential to extract even more value from the data over time. Step by step, we are expanding how we interpret insights and turn them into measurable operational improvements.