When it comes to getting value from your test data, how you choose to interact with, it is just as important as what the content of the data is. Traditional Quality Management Software and Statistical Process Control often limit its applicability to detailed monitoring of a few key parameters in a product. This methodology might help detect severe quality breaches before a product is shipped out. However, it fails significantly when it comes to facilitating cost-optimization and Continuous Improvement.
A top-down approach
WATS enables a top-down approach for working with your test data, an approach that is superior for Continuous Improvement and Total Quality Management. One where any pass/fail information is aggregated up to a global True First Pass Yield metric, through a series of meta-data categories. Categories such as locations, products, revisions and more. Users can then easily identify the categories where failures most frequently occur, prioritize properly and spend their resources investigating these.
Detect failures where they are introduced
An important source of data in WATS’ top-down is all the various test processes a finalized product goes through. Each one is a potential source of failures, and each one is a potential candidate for poor-test coverage. The 10x Cost Rule of Manufacturing tells us that the cost of fixing an early failure grows exponentially for each step it is allowed to pass in the manufacturing process. So it is essential that a proper continuous improvement implementation lets you detect the failures where their symptoms first appear and fix them there.
Learning through the lifecycle
A key benefit of this top-down approach is also that you can effectively prioritize improvement initiatives based on frequency and severity, vs the efforts needed to fix them. By combining test data analytics with RMA and Repair data analytics, your prioritisation machine is taken to the next level. A level where you can directly link the symptoms detected in manufacturing testing, to some of the most costly activities that an OEM carries responsibility for.
Available features in WATS to support this approach for analytics includes:
- Yield Reporting, where you can see true yield metrics for all of your products.
- Product and Test Yield, where you can break yield down by product revisions, and test software versions
- Station Yield, where you can see the yield metrics per test station
- Periodic Yield, where you can break down yield by different periods.
- Total Process Yield, that shows you the yield for the final test operation applicable for your specific products
- Rolled Throughput Yield, showing you the yield throughout all of your sequential test operations
- Process Capability Analysis, that you can use to evaluate how suitable your test limits are, or if the individual measurements appears to be unstable.
- Repair Analysis, that shows you powerful statistics for the different repairs that you document throughout the entire lifecycle of a product.
- Overall Equipment Efficiency, that you can use to evaluate the availability, throughput and quality of your different test assets.
- Connection and Execution Time gives you time statistics for testing scenarios, so that you can evaluate how much resources really goes into testing.
- Gauge RnR, that shows you information on how much variance comes from your test systems themselves.
- Events and Alarming Module, that will provide you with notifications on pre-defined scenarios detected in your data.