Batch 360™ - AI Enabled Production Optimization

pateikė SymphonyAI Industrial

Al-driven multivariate model that harnesses data to manage production for batch optimization

SymphonyAI Industrial channels the power of rapidly evolving IIOT, Artificial Intelligence, and big data technologies to deliver industrial insight that provides exponential value to our customers.

Batch 360™ moves manufacturers from traditional Statistical Process Control (SPC) which is univariate and provides late alerts, causing scrap and quality overruns, to a highly sophisticated Al-driven multivariate model that harnesses all available data to manage production at highest levels for batch optimization.

These models perform the following:

· Understand potential factors of deviations

· Act in real-time to bring a process back on track

· Make set point recommendations under dynamic operating conditions to generate a "golden batch"

With Batch 360™, known variables characterizing ideal production conditions are input and operators are prompted for proper procedures for the current run. Batch 360™ provides real-time monitoring and supervisory control. Runtime data is historized and analyzed for golden batch conditions profiling and Al optimizes batch characterization.

SymphonyAI Industrial solutions serve a broad range of industries from process to discrete to naval and marine.

Batch 360™ is unique in that it provides:

· Data Acquisition - Connectors to a wide-range of data acquisition systems and an efficient data management system capable of streaming and batch analytics

· Data Pre-processing - Automated de-noising, imputation and contextualization using advanced ML algorithms

· Soft-sensing - Soft-sensing of critical production parameters in real-time

· Templatized Al Models - Explainable Al modules with parameters tuned for the manufacturing process with deep learning and system identification modules

· Deep Forecasting - Deep forecast module for KPOVs with KPIVs with track-and compare features

· Golden Batch Analytics - Identification of golden batch conditions from historical runs combined with ability to track deviations of batch runs in real-time

· Anomaly Detection and Prediction - Anomaly detection and prediction using deep self-learning Al models

· Real-time Quality Assessment - Real-time monitoring of production quality with automated defect detection, production unit health scores, alerts and notifications

· Predictive Asset Health - Explainable Al leading with automated cause analysis for predictive maintenance of equipment

· Optimization and Control - Real-time supervisory control with sophisticated state estimation and optimizers to maintain production at highest levels

· Model Management - Automated model re-training for anomaly detection, forecasts, soft-sensing, and optimization

· MLOps, Continuous Integration, Continuous Deployment - No resource drain for end-users or IT staffs to keep up with new releases and feature enhancements

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