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Chemical plant

Overall Equipment Effectiveness (OEE)

Challenge

An in-depth understanding of Overall Equipment Effectiveness (OEE) across a site is critical to identify process bottlenecks and maximize production. OEE analysis can be difficult to standardize and scale across many similar or dissimilar assets at a manufacturing site, which often limits visibility and slows improvement efforts. By leveraging the Seeq Advanced Analytics and AI Suite, organizations can automate OEE calculations, apply a consistent scoring method, and monitor performance across assets in real time. This enables engineers, operations managers, and executives to detect inefficiencies, compare effectiveness between units, and prioritize corrective actions. With Seeq, process industries can move beyond static reporting to achieve predictive insights, asset reliability, and continuous improvement, ultimately driving higher productivity, quality, and sustainable growth.

Solution

A large-scale manufacturing operation implemented Seeq’s advanced analytics to unlock deeper visibility into production. In Workbench, teams now utilize intuitive Point and Click tools to identify unique modes of operation and measure time spent in each mode, giving operations a clear picture of efficiency. Engineers are empowered with historical benchmarking to establish appropriate threshold limits that distinguish between ideal and non-ideal equipment operation. The Seeq integration with existing asset hierarchy systems makes it possible to seamlessly scale analysis across all site equipment, enabling consistent comparisons and faster insights. By combining predictive analytics, asset performance monitoring, and real-time data connectivity, Seeq helps organizations reduce downtime, improve quality, and accelerate digital transformation. This approach not only standardizes OEE and reliability analysis but also drives sustainable business outcomes across process industries.

Results

Implementing the plant-wide OEE Dashboard, including high-level comparison across process units, unveiled some unexpected bottlenecks. While the site had historically been looking only at uptime as a means of measuring OEE, they were ignoring large periods of time when the unit was running but under some constraint. This analysis across assets enabled them to identify which processing units saw the greatest rate constraints while running, investigate the root cause of those constraints, and invest in those areas by installing capital projects to de-bottleneck the process.

Data Sources

  • OSIsoft PI + Asset Framework

Data Cleansing

Seeq capsules were created using Value Search to differentiate between each of the various modes of operation.

Calculations and Conditions

  • Asset Trees
  • Asset Swapping
  • Treemap
  • Histogram
  • Value Search
  • Signal from Condition
  • Scorecard Metric
  • Formula

Reporting and Collaboration

A high-level dashboard was created to showcase the “big picture” at the top of the dashboard. The treemap color-coded by OEE score of the various process units is interactive, and consumers of the report can click into a unit shown in red to gain further insight into what aspects of that unit are driving the low OEE.