Dataprophet PRESCRIBE uses advanced predictive and prescriptive machine learning capabilities to predict defects, faults and quality errors, and prescribe optimum control parameters to improve production.
The solution, which is already in use in multiple Tier 1 US automotive suppliers, can be integrated into a manufacturer's existing data environment in as little as four to eight weeks.
PRESCRIBE ingests and analyzes process and quality control information from databases, spreadsheets, streaming sensors and PLC sources, and can record up to a thousand data points a second. State-of-the-art machine learning models are able to learn from this data to identify optimal control bounds.
To enable process change, PRESCRIBE provides operators with an easy-to-use web-interface. It helps operators to identify when operational parameters have exceeded the target control bounds and prioritize the changes required to optimize production and reduce scrap.
Access to the web-interface can be strictly controlled, limiting features, reports, and parameter access accordingly; while client manufacturing and application data are housed in a fully isolated database for maximum security.
Additional features allow users to compare reports across different time periods to ascertain if process changes are being actioned correctly, as well as explore reports across different steps in the manufacturing process. Users are also able to filter uncontrollable and optimized parameters to focus on critical changes that need to be made and search for parameters crucial to a particular piece of equipment or process change.
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