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Batch process digitalization using PI EventFrames to improve OEE and batch Quality
Intuitive approach to batch data collection and visualisation for process improvements.
What is a batch process?
A batch process is a manufacturing method where production occurs in distinct, separate batches rather than as a continuous flow. Much like baking a cake, this process requires following a predefined sequence of steps – mixing ingredients in precise amounts, maintaining the right conditions, and completing one batch before starting the next.
Monitoring key batch parameters such as temperature, pressure, reaction time, and material usage ensures consistent quality and process efficiency while identifying potential deviations.
Collecting the batch data plays a vital role in improving Overall Equipment Effectiveness (OEE) and it enables visibility into equipment availability, performance and yield.
Context:
Our client, a UK-based herbicide manufacturing company, was looking to improve their batch data collection and visualisation. They were looking for a new PI System to replace their old, custom-built in-house legacy system, which was becoming difficult to maintain. It needed a batch execution system, batch-related KPIs to quicker calculate OEE and a way to compare each batch against a ‘Golden batch’.
Solution:
Eigen deployed a PI system as the basis of the operator’s digitalisation infrastructure in compliance with the ANSI/ISA-88 (S88) standard to enable batch data collection.
We built an asset framework structure and developed different calculations, from complex PI Analysis to simple formulas, as well as adding different enumeration sets to track different stages of the batch. The new framework used PI Event Frames (EF) which allowed batch events to be captured and effectively structured. Batch data was organised hierarchically into Unit batch, Operations, and Phase.
- Unit batch: A single execution of a recipe within a specific unit.
- Operations: The major steps required to complete the batch such as raw material charging, reaction, separation, and product transfer.
- Phase: Represents specific control actions like valve sequencing, temperature ramping, or agitation control.
By structuring batch data in this way, operators and engineers could track performance at multiple levels, analyse deviations, and optimised production efficiency.
Furthermore, we used Eigen’s PI Event Frame Manual Data Entry plug-in for insight using Sub-Event Frames. Our client can now manually select a reason code from a drop-down menu to explain downtimes as well as input lab results. This enables the operator to learn more systematically to help trouble-shoot and get ahead of issues.
Impact:
By optimising OEE, our client can now boost production, reduce downtime, and improve availability, meeting demand without major investments.
Traffic light indicators display status, allowing for real-time comparisons between ongoing batches and a golden batch benchmark. Historical data is now easily accessible enabling operations teams to identify variations impacting OEE, quality and performance.
Features such as batch search, results tables, batch trends, and Gantt charts provided a structured view of production data, helping pinpoint variations in cycle times, reaction efficiency, and material consumption.
Being able to identify what causes each downtime event (performance downtime, availability downtime and recipe) means necessary adjustments can easily be made to improve productivity:
- Performance downtime could be noted if production did not align with the golden batch benchmark.
- Availability downtime captured reasons for a batch being put on hold.
- Recipe events recorded key process steps within the batch execution.
Additionally, KPI calculations have been implemented using PI Analysis for OEE monitoring. Now the operations teams can correlate batch execution data with equipment states, downtime events, and operator inputs.
Using the new system configured by Eigen the process engineers on site now have a better understanding of their downtimes and losses.
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