AI Solutions for manufacturing
Ready to increase productivity and reduce expenses?
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 manufacturing processes?
The most common Aliz manufacturing solutions
Improving on quality by optimizing process and control metrics. This results in less faulty products lowering
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)
Optimizing manufacturing process with AI
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
What happens after sign up for the
Step 1You 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 2Fill out documentation, generated based on historical sensor data, to inform about your objectives and desired KPIs.
Step 3The 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 4Ingest newly generated data. The algorithm will process these and you can obtain the value set for optimal settings of the manufacturing process.
Step 5Maintaining 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.
Retraining and maintenance