Cloud solutions for manufacturing

Ready to increase productivity and reduce expenses? Ready to get introduced to Cloud solutions for manufacturing?

Discover how you can benefit from Aliz AI Solutions for optimizing manufacturing processes.


How AI can help you with running and maintaining an optimum in Cloud solutions for manufacturing processes? 

Reaching optimal values on the settings to control a process potentially result in reducing energy consumption, human operative hours, or other resources. These have a direct affect on budget-related KPIs and metrics and result in lower manufacturing costs.

Boost profitability and gain revenue directly from optimizing the process and reach your KPIs more effectively. Conflicting KPIs and continually changing environmental factors are no longer a problem thanks to automatized optimization by our AI platform.

Our proprietary AI algorithms automatically detect patterns in sensor data that exceed the capabilities of human perception or the precision of other heuristic approaches. This provides a head start and can prevent breakdowns or improve product quality. These issues affect the maintenance and production costs directly. Optimization potentially saves the majority of unexpected costs or helps reach better budgetary KPIs.

The most common Aliz Cloud solutions for manufacturing

Quality optimization
Improving on quality by optimizing process and control metrics. This results in less faulty products lowering
Predictive maintenance
 Predictive analytics leveraging on ML technology can significantly raise the potential of finding early indicators of quality issues. Instead of modeling with some old-fashioned mathematics that rely heavily on quantitative domain knowledge, an AI-based solution can result in an optimal state of a specific maintenance process and prevent loss or actually maximize revenue. Leveraging risk in maintenance processes is available without resource-heavy research or implementation work. Optimization to improve the quality of the production (produced goods)
Process optimiaztion
An optimization process covers the exploration of the value space of the multiple variables that can be recalibrated (within some constraints). By leveraging ML routines, the exploration discovers certain schemas. By utilizing these settings, the desired KPis can be achieved without sketching the actual mapping functions by hand, which would require a high level of quantitative knowledge in some dedicated fields (e.g. thermodynamics, fluid dynamics)

Benefits of AI based solution with Aliz in manufacturing optimization

As the desired KPIs often conflict, the problem to be solved can easily result in a local optimum or suboptimal state. Optimization with an AI solution can mitigate this risk. Because of the iterative modeling and simulation phases over all the objectives, it will result in fine-tuned optimization despite such obstacles.

The solution runs in a SaaS model. Therefore you don’t need to invest in servers, spend money on maintenance, allocate resources to set up the system, or delay the start by waiting on implementation. You can jump start by collecting the historical sensor data and upload it through the web interface or API.

The solution is not dependent on any vendor, it doesn’t require a specific IoT device or platform. You can have any type of sensors, we are agnostic from manufacturer and model. The solution includes automatic data cleaning and assisting services and it only requires lightweight feature engineering which our data engineering experts will be there to support you.

You can either ingest the data and manage the settings of the solution with a lightweight API or through the interface of our web application.

Use cases for operating and
maintaining ML models in production

What happens after sign up for the Cloud solution for manufacturing

  • Step 1
    Data ingestions

    You will meet a consultant who will support you on how to prepare your data. Then you can obtain your sensor data, transform into the required format and ingest with our API or web application.

  • Step 2
    Document objectives

    Fill out documentation, generated based on historical sensor data, to inform about your objectives and desired KPIs.


  • Step 3

    The Solution automatically generates a model to simulate the KPIs. Then the AI model is trained to customize the algorithm that will optimize your settings based on the objectives.

  • Step 4

    Ingest newly generated data. The algorithm will process these and you can obtain the value set for optimal settings of the manufacturing process.

  • Step 5
    Retraining and maintenance

    Maintaining desired results and KPIs, requires continuous ingestion of new sensor data to retrain the algorithm. This can be done with no serious efforts through the web interface or can be automated by using our API.

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