Batch 360™: AI-Enabled Production Optimization
Can You Attain the "Golden Batch"?
Operators have difficulty consistently manufacturing high quality batches within specifications. Identifying when a batch is out of control can be a challenge.
It is essential to be able to quickly analyze each batch, identify root causes for poor performance and learn the right settings to generate a “golden batch”.
Process Engineers are typically tasked with this exercise, but the tools are either not available, or if they are, need a lot of data to perform SPC – which takes time.
In effect you are running blind.
- Poor quality of the batch can impact throughput and overall cost of a plant
- Downstream products can be impacted if the batch is bad and not contained
- Delays in quickly Identifying deviations can result in costly wastes of time and product loss
- Characterizing of Golden Batch by correlating input variables (KPIVs) and output variables (KPOVs)
- Predictive capability extracts previously undetermined factors to ideal production conditions
- Auto-identification of Golden Batch based on KPIVs only for subsequent runs
- Speeds up visual batch inspection
- Identifies issues in near real time
- Historized data (including CTQ metrics)
- Root Cause Analysis
- Inline identification of process drift
- In-situ identification of potential scrap
- Establish an Electronic Batch Record in the format of your choice
- Work instructions sent to operator stations
- Scalable framework
- Fast implementation / Time to Value
- Platform-neutral integrations
- Per-plant pricing - Unlimited tags
- Purpose-built AI models for industrial process
- High responsiveness (low latency)
How it works
- Input known variables characterizing your ideal production conditions
- Operators prompted for proper procedures for current run
- Real-time monitoring
- Runtime data is historized and analyzed
- AI optimizes Batch Characterization
EurekaAI Platform
Batch 360 runs on the secure, scalable, flexible, and performant EurekaAI Industrial Platform. The platform is available on the cloud, in a private data center, or entirely on-premise. It leverages proven open-source technologies along with proprietary components that are built to tackle the unique challenges of industrial data.
