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.
Challenge
- 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
Solution
- 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
Benefits
- 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.
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