The Quartic.ai Product Control Module creates context from heterogenous discrete and analog data sources to detect hidden CPV variability signals with machine learning to create a real-time dynamic product control view for the enterprise.
Build a real-time CPV program
A real-time CPV program can only be built on a foundation of CPV signals designed to identify potential new variation or unexpected patterns in the data while the process is operating within the operation space of the product control strategy. The existing process control systems and SPC are neither intended to, nor capable of, measuring these signals. A big data and machine learning solution addresses all these challenges.
Monitor the lifecycle of product performance with big data and machine learning
A typical product control strategy produces thousands of data points that span the lifecycle of the product and the manufacturing infrastructure of the enterprise including CMO’s and CDMO’s. The CPV signal discovery therefore requires understanding complex multi-dimensional, often non-linear, relationships and slow emerging patterns from a large volume of heterogenous data.
In general, CPV signals assess predicted performance based on previous process experience. The development and effectiveness of these signals depend on statistical techniques sensitive to the size and inherent variability of the existing data set.
The Quartic CPV and Product Control Strategy Solution is built on the Quartic Platform. The Platform connects to industrial OT (Operational Technology), condition measurement systems, MES and CMMS systems; contextualizes the data to an asset and allows users to build intelligence agents using machine learning (ML) and complex event (rules) processing (CEP).
Automated data preparation and pre-processing.
Organizes, contextualizes, and prepares data from different sources and formats with an automated processing pipeline
Automated Machine Learning
Built for the product, process, and manufacturing engineers or practitioners with little to no data science or programming expertise
Model performance monitoring and re-training
Model performance and robustness is monitored continuously with notifications for retraining