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THE MOST COMMON ALIZ CLOUD SOLUTIONS FOR MANUFACTURING
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.
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.
Fill out documentation, generated based on historical sensor data, to inform about your objectives and desired KPIs.
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.
Ingest newly generated data. The algorithm will process these and you can obtain the value set for optimal settings of the manufacturing process.
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.