OEE, Cycle Time, Utilization

Digital Twin Use Case: Throughput and Efficiency

Overall Equipment Effectiveness (OEE) and Cycle Time are critical metrics describing how a product is performing in its manufacturing process, and the efficiency and speed of that process and its constituent machinery. Executive leadership needs these metrics to maintain profitability, efficiency, and manufacturing velocity. With Fusion, users can have real-time insight into analytics that not only drive these metrics, but also have root-cause workflows to identify, investigate, and resolve any OEE, Cycle Time, and utilization issues.

OEE and Cycle Time workflow with drill-down to underlying causes


  • Optimizing throughput and equipment utilization
  • Trending event duration
  • First Pass, Last Pass, and Rolled Throughput Yield
  • Planned and Ideal Production Time
  • Serialized unit traceability and history


  • Normalized Data from Test, Process, and Assembly events
    • BOM/Assembly/genealogy
    • Data model that supports parametric and process analysis
  • Serialized part history and traceability
    • Event by iteration for FPY
  • Standard data formats and connectors/adapters


  • Accelerated time-to-market
    • OEE
    • Cycle Time
    • Rolled Throughput Yield (RTY)
    • First Pass Yield (FPY)
  • Better operational efficiency
    • Retest metrics
    • Boneyard metrics
    • Boneyard metrics

In this OEE and Cycle Time workflow seen above, a user can quickly see real-time and historical information on OEE and Cycle Time. By utilizing Fusion’s data model and Microsoft Azure and SSAS, the user can click on different parts of the analytic to explore why cycle time and OEE are performing the way they are (i.e., is ICT taking more time than expected? If so, I can click on ICT to see what the last calibration date for the testers, the trending failure rate, the trending SPC for all measurements for that model tested on those ICT testers, compare the ‘bone-yard’ of units waiting to be tested at each ICT tester, the yield rate of different ICT testers to see which is less capable than others, etc).

With this kind of business intelligence integrated in a workflow from the business outcome and metrics, an organization leverage the digital twin to not only know how they are performing in key business metrics like OEE and Cycle Time, but can also be cued to the correct actions to improve their performance.


Prevent Customer Escapes, Improve Efficiency

Heatmap Overlay

Rapid Root-Cause Identification and Resolution

As-Built, As-Maintained, As-Is

Full Traceability and Genealogy


Fuse Your Enteprise: Test, Inspection, Supplier, and R&D data into a single source of truth.