Every businesses has vital processes that it needs to optimise and control. These could be physical processes, such as chemical or materials production or discrete manufacturing. Or they could be business processes, such as supply chain management or logistics. Whatever your processes, you want to maximise and maintain the quality of outputs while minimising the cost and effort input. And you are often trying to achieve these goals using incomplete, sparse, or noisy data.
Alchemite™ extracts maximum value from such data. Originally designed for complex scientific problems, it can be applied to any numerical dataset. Example uses include selecting process inputs to deliver the optimal combination of outputs based on historical data, monitoring inputs and outputs in order to recognise outliers and maintain quality, and understanding and managing risk in your processes.
White paper – predictive maintenance and process optimisation in manufacturing
This Intellegens white paper discusses the application of deep learning in manufacturing, including how software methods trained on noisy, historical manufacturing data can help guide engineers with suggested process calibrations.
Alchemite™ for process optimisation and control
Analyze data to identify outliers, even for sparse, noisy data
- Quantify uncertainty to focus process improvements on routes most likely to succeed
- Understand where more data is needed on your production processes
- Design optimal process parameters to improve quality, energy efficiency, and performance
- Monitor production data to refine models and identify potential process changes