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An AI-driven analytics solution designed to help standardize batch production and further enable proactive operational optimization.
TwinThread announced the release of Perfect Batch, an AI-powered manufacturing analytics solution designed to help manufacturers standardize and more consistently replicate their best-performing or “golden batches.” The solution identifies ideal batch profiles from historical data and provides recommendations to help improve efficiency and support proactive optimization.
Unlike legacy systems that rely on passive alerts and static manual settings, Perfect Batch applies industrial AI to dynamically identify ideal batch profiles and recommend actions for improving performance. This helps to enable organizations to shift from reactive operations to proactive optimization within shorter timeframes.
Perfect Batch visualizes optimal batch profiles and process variations to support real-time performance optimization
Platform capabilities include:
Rapid Speed to Value: Connects to existing batch execution systems and analyzes historical data to help build digital twins and apply models within a very short period of time.
Dynamic Profile Learning: Learns ideal control limits and process centerlines based on actual process capability and historical performance.
Capacity Optimization: Identifies bottlenecks and lost production time through granular cycle time analysis, helping to support improved utilization of existing assets.
Optimization by Exception: Provides automated alerting and issue diagnosis, allowing teams to focus more time on resolving critical issues.
Optional Closed-Loop Action: Enables automated responses, diagnostics, and corrective actions through integration with a real-time workflow engine.
Automated Compliance: Supports material tracking, quality and yield conformance, and audit-ready reporting for regulated industries.
Perfect Batch supports continuous monitoring and optimization across many key operational dimensions, including utilization, cycle time, quality, process control, and yield. It is available in multiple deployment tiers and is designed to scale across different manufacturing environments.
The platform also helps to enable broader visibility across manufacturing operations by providing a unified view of batch performance and asset utilization. This supports better alignment across operations, engineering, and supply chain teams, helping to enable coordinated and data-driven improvement initiatives.
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