Transparent pricing.
Modular by design.

SMARTR.factory is licensed modularly. The price is made up of two clearly separable components: the functional scope you want to use (e.g. machine data capture, maintenance or planning), and the usage scope of your company, factors such as the number of sites, workstations, lines or data connections.

You only pay for what you actually need. The three packages on this page show how the price is composed for typical configurations. Your individual price is calculated based on your specific requirements.

You can adjust the functional and usage scope at any time, both upward and downward. Opening a new site, adding machines or lines, or want to enable an additional module like maintenance or planning? We implement it on a monthly basis. You can just as easily reduce components when your situation changes.

The license model grows with you, without forcing you to commit today to tomorrow's configuration.

During the first 12 months SMARTR.factory is cancellable monthly. So you can start with a pilot project and convince yourself step by step, without committing long-term from the start. From the 13th month on, the contract continues with a term of 12 months at a time.

This structure gives you what is rare in classic MES contracts: real freedom of choice in the decisive early phase.

We deliberately do not offer a free demo version for self-testing. The reason: MES requirements are very individual, and a standard demo account would not give you a realistic picture; it would more likely mislead you.

Instead, you get to know SMARTR.factory in depth before your decision, in a live demonstration tailored to your production. If you decide for us, we start together with a pilot project that remains cancellable monthly within the first 12 months. So you test the system not in an abstract sandbox but directly in your real environment, with real machines, real data and full functionality. This path gets you to your goal faster and more reliably than any demo version.

Implementation time depends on the scope of your project. Our recommendation: start with a pilot project, a focused initial implementation in a clearly defined area of your production. With that you typically go into production in around three weeks.

From there we extend together step by step: additional machines, more modules, deeper processes. So you see concrete results quickly while keeping full control over the rollout pace.

You can choose between three operating models:

• Our cloud: hosted in German data centers, GDPR-compliant, with no IT effort on your side
• Your own cloud: you operate SMARTR.factory in your own cloud environment (e.g. Azure, AWS or a private cloud)
• On-premise: installation and operation on your own servers, fully under your control

Which model is right for you depends on your IT strategy, compliance requirements and internal resources. We are happy to advise which variant fits your situation.

Everything that we can configure too. With SMARTR.factory there is technically no difference between what our experts configure and what your key users can do themselves.

During onboarding we train your key users so that they can operate, extend and adapt the system independently to new requirements, from new dashboards to additional analyses to new machine connections. This keeps you independent and avoids commissioning a service provider for every small change.

If your internal capacity is insufficient or you plan a more complex extension: our support is always available, and our team can also assist you at any time after onboarding.

Yes. SMARTR.factory meets both the technical and the organizational requirements to be used as an Energy Management System, for example as part of an ISO 50001 certification. Through the existing data connections you capture energy consumption granularly at machine, line or site level, evaluate it in real time, and integrate it seamlessly into your existing reporting and control processes.

This saves you a separate EMS system and gives you the added value of analyzing production and energy data in a shared context.