Overall Equipment Effectiveness provides a useful overview of how effectively a production system operates. It combines three factors:
- Availability
- Performance
- Quality
OEE can show that production performance is below target. However, the metric alone does not explain why losses occur.
A low result may be caused by machine downtime, micro-stops, reduced operating speed, long testing cycles, rejected products or repeated inspections. Manufacturers need more detailed information to distinguish between these causes.
Integrated testing and connected process data provide this context. Test results show whether the product meets its requirements. Process data helps explain how it was manufactured and why a deviation may have occurred.
Why OEE Requires Context
Two production systems can have the same OEE while facing completely different problems.
One line may lose availability because of frequent equipment stops. Another may run continuously but produce below its target cycle time. A third may achieve the required output but lose quality through scrap and rework.
Useful OEE analysis therefore requires more than one overall percentage. Manufacturers need to understand:
- Where production losses occur
- Which products and variants are affected
- Which stations cause interruptions
- Why products are rejected
- Whether test equipment or the product caused a failure
- How process values change before a problem occurs
Production analytics can combine machine status, cycle times, process values and quality results. HAHN Analytics, presented within the HAHN Automation Group’s digitalization solutions for manufacturing, creates production transparency and helps identify opportunities to improve OEE.
Integrated Testing Creates Diagnostic Quality Data
Automated testing is often treated as a final decision: the product either passes or fails. The underlying measurements can provide considerably more value.
Depending on the application, integrated testing may generate:
- Vision inspection results
- Force and distance values
- Torque and angle data
- Electrical measurements
- Leakage or flow values
- Dimensional measurements
- Calibration data
- Functional test results
- Rejection reasons
These results can be connected with the product identifier, production station, active recipe, tool or mould cavity.
Instead of knowing only how many parts were rejected, manufacturers can determine which defect occurred and under which production conditions it was detected.
A practical example is the integrated testing used in connector manufacturing. The system verifies high-voltage performance, contact presence and pin position. Non-conforming parts are sorted by cavity and defect type, providing a more useful basis for troubleshooting and process optimization.
Improving the Quality Component of OEE
The most direct connection between integrated testing and OEE is the quality factor.
Inline inspection detects deviations before additional value is added to a defective product. Test data can also help manufacturers:
- Identify recurring defect patterns
- Associate failures with tools or cavities
- Detect gradual process drift
- Reduce scrap and rework
- Investigate false rejects
- Improve first-pass yield
First-pass yield is particularly important. A product that passes only after a repeated test still requires additional time and capacity. If retests are not recorded separately, part of the performance loss may remain hidden.
The HAHN Automation Group solution for high-OEE pipette tip production combines 100% visual inspection with cavity-specific sorting and end-to-end traceability. The resulting system achieves an OEE of 97% while reducing scrap to less than 1%.
Testing does not improve quality merely by rejecting defective products. Its greater value lies in providing the information required to stabilize the upstream process.
Improving Availability Through Better Downtime Analysis
Integrated test stations can also influence equipment availability. Interruptions may be caused by:
- Unstable electrical contacts
- Contaminated sensors
- Calibration issues
- Communication failures
- Incorrect product positioning
- Loading and unloading problems
- Implausible measurement values
A recorded downtime event does not automatically show whether the product, testing equipment or upstream production process caused the interruption.
A useful data structure should therefore distinguish between:
- Product defects
- Process deviations
- Machine faults
- Test equipment faults
- Missing material
- Operator intervention
- Planned downtime
This classification helps maintenance and production teams focus on the actual cause rather than treating every interruption in the same way.
Trend data can also reveal whether a sensor, fixture or electrical contact is gradually becoming unstable. Maintenance can then be planned before the issue causes longer production interruptions.
Improving Performance by Identifying Bottlenecks
Testing is often one of the longest processes in an automated production line. A product may be assembled within a few seconds, while electrical, functional or leakage testing takes considerably longer.
If testing capacity is not aligned with assembly output, the test station becomes the bottleneck.
Connected cycle-time data can show:
- Which station determines the overall cycle time
- Where micro-stops occur
- How often products are retested
- Which variants require longer test sequences
- Whether loading limits testing capacity
- How much waiting time exists between stations
Possible improvements include parallel test nests, automated product connection, separation of individual test steps or early testing during assembly.
The objective is not to reduce test coverage. The testing sequence should be distributed and designed so that the required quality controls support the target production rate.
In a production line for electronic housings, HAHN Automation Group combined MES-connected testing and traceability with electrical and mechanical inspection. Manual tray loading was decoupled from the automated process, helping maintain flexibility without unnecessarily interrupting production.
Connecting Process Data with Test Results
The greatest optimization potential is created when final test results can be compared with upstream process data.
A connected production record may contain:
- Product and variant identification
- Material or component batch
- Machine and station data
- Active recipe
- Tool or cavity information
- Process parameters
- Cycle times
- Inspection and test results
- Rejection reason
- Machine status and timestamp
This information allows manufacturers to investigate relationships such as:
- Whether a specific force-distance profile leads to later failures
- Whether one cavity produces more rejected parts
- Whether a material batch is associated with changing test values
- Whether process values are moving towards their limits
- Whether a failure occurs only with one product variant
The data does not replace engineering analysis. It narrows the search area and provides evidence for targeted process improvements.
Complex production systems can also use analytics to compare products, recipes and operating conditions. The flexible production line for implantable medical devices combines vision systems and integrated analytics to create process transparency across more than 30 product variants.
Turn Production Data into Actionable Information
Collecting more machine data does not automatically improve OEE. Data should be selected according to the production losses that manufacturers want to understand.
A useful approach begins with clear questions:
- Which OEE losses need to be investigated?
- Which machine, process and testing data explains them?
- How will products and data records be connected?
- Are fault codes consistent across the system?
- Who will review the information?
- Which action should follow an identified deviation?
Relevant dashboards may display:
- OEE by machine, station or variant
- First-pass yield
- Scrap by defect type or cavity
- Number of retests
- Average testing time
- Most frequent downtime causes
- Process values approaching their limits
Reliable product identification, consistent timestamps and clearly defined failure codes are essential. Without them, data from different stations may be difficult to compare.
Common Mistakes
Common problems when combining testing, process data and OEE include:
- Recording only pass-or-fail results
- Using inconsistent fault codes
- Failing to distinguish product defects from equipment faults
- Storing information in separate local databases
- Collecting data without a defined purpose
- Ignoring retests and rework
- Optimizing one station instead of the complete line
- Considering testing capacity too late
These issues can create large volumes of data without providing the information required for meaningful improvement.
Conclusion
OEE identifies losses in availability, performance and quality, but it does not explain their causes.
Integrated testing provides detailed information about product quality. Process data records the conditions under which each product was manufactured. When both data sources are connected, manufacturers can identify bottlenecks, investigate recurring defects, distinguish equipment faults from product failures and detect process drift earlier.
The result is not an automatic increase in OEE. It is a more reliable basis for targeted engineering, maintenance and process optimization.
Improve the Performance of Your Automation System
HAHN Automation Group combines automated testing, process monitoring and data analytics within complex production systems.
Our experts help manufacturers create the transparency required to identify production losses, improve process stability and increase the long-term performance of their automation equipment.
