Simcenter Testlab: Siemens Links Physical Testing, Simulation and Data

Siemens has announced a series of updates to Simcenter Testlab, its software for testing, data management and engineering analysis. The news was reported by Engineering.com on September 29, 2026. The stated goals are to reduce manual work, encourage consistent test procedures and make engineering data available earlier in the development cycle.

What changes in Testlab

The updates cover four areas: structural dynamics, system-level prediction of noise, vibration and harshness (NVH), preparation of AI-ready data, and preparation of shock and vibration tests. The last of these is described as virtual shaker testing, used to plan physical tests.

Structural dynamics: the move to Testlab Neo

This update completes the migration to the latest Simcenter Testlab Neo workflows. For test engineers, this means integrated workflows for test setup and execution, designed for both experienced and less experienced users.

According to Siemens, Testlab Neo brings together in a single environment:

• shaker acquisition;
• real-time visual feedback during setup;
• full support for Simcenter SCADAS hardware.

Siemens cites efficiency gains of up to 35% across the entire structural dynamics process.

System-level NVH prediction

The new workflows combine component characterization with virtual prototyping. This makes it possible, again according to Siemens, to assess performance up to 60% faster, before physical prototypes exist.

The 35% and 60% figures are Siemens claims and have not been independently validated.

It is worth remembering that these are maximum values ("up to") tied to scenarios chosen by the vendor, and real-world results will depend on each company's processes, products and data maturity. In addition, for the AI data preparation and virtual shaker testing features, the information currently available is limited to the announcement summary, with no technical detail.

Why it matters for industry

The update is a concrete example of the idea behind the digital twin: linking physical test data with simulation and virtual prototypes so that each improves the other. Data measured on components feeds system models, while simulation helps plan real tests more effectively.

Two other aspects directly concern technical and IT leaders: the automation of test procedures, which reduces variability between operators and sites, and the preparation of data for AI, which presupposes structured, documented and reusable data.

A practical takeaway

Regardless of the vendor you choose, the announcement suggests a few questions to ask internally:

• Are your test procedures standardized and repeatable, or do they depend on individual experience?
• Is test data stored with enough metadata to be used by simulation models and AI algorithms?
• Is simulation used to plan physical tests, not just to validate them after the fact?
• Are vendor-claimed benefits verified through a pilot project on a real use case?

Before drawing conclusions about the impact of these updates, it is advisable to wait for the full technical documentation and, where possible, measure the results on your own process. The underlying message, however, is clear: value grows when testing, simulation and data management stop being separate silos.