Save on energy consumption by optimizing settings
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 effect on budget-related KPIs and metrics and result in lower manufacturing costs.
Improve productivity by modelling and optimizing for KPIs
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.
Reduce maintenance cost by predictive analytics
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.
As our development partner in building the core of our product, Aliz fully embraced [our] guiding principles like it was their own venture. They innovated a unique, flexible architecture and worked with us to develop a product that today receives consistent praise from clients and market analysts alike for the client-centric approach, flexible data structure, and speed of front-end visualization.
Founder & CEO
Predictive analytics leveraging on ML technology can significantly raise the potential of finding early indicators of quality issues. 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).
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).
Improving on quality by optimizing process and control metrics.
This results in more faultless products.
Successful over multiple conflicting objectives
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.
Infrastructure agnostic solution
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.
Easy to use
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.
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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.
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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