AI-powered analytics software optimizes batch production
Perfect Batch AI-powered, manufacturing-analytics software optimize production by letting users standardize and consistently replicate their best-performing or “golden” batches. It continuously monitors and simultaneously optimize five dimensions—utilization, cycle time, quality, process control, and yield—and gives operations teams gain actionable intelligence to reduce investigation times, minimize waste, and accelerate continuous improvements.
Unlike legacy systems that rely on passive alerts and static manual settings, Perfect Batch applies AI to identify ideal batch profiles from historical data, and recommend actions for increasing efficiency. This lets users rapidly shift from reactive firefighting to proactive optimization in a matter of weeks, instead of months or years.
Perfect Batch’s main activities include:
- Rapid speed to value. Connects to existing batch execution systems, and automatically interrogating past data to build digital twins and apply models in hours.
- Dynamic perfect profile learning. Instead of setting limits manually, Perfect Batch dynamically learns ideal control limits and process centerlines based on actual process capability and historical performance.
- Unlocks hidden capacity. Granular cycle time analysis identifies bottlenecks and lost production time, facilitating capacity improvements from existing assets without new capital investment.
- Optimization by exception: Automated alerting and issue diagnosis empowers operations teams to focus on solving problems, without getting bogged down with endless troubleshooting and investigations.
- Optional closed-loop action. Thread Builder, a real-time workflow engine that works with Perfect Batch, automates anomaly responses, performs automatic diagnoses, and can automatically trigger specific corrective actions.
- Automated compliance. Tailored for regulated industries, Perfect Batch provides automated material tracking, quality and yield conformance, and audit-ready histories.