Synopsis:
This module fully supports automated data loading. Staff defines the business rules to determine what is good vs bad data and to discern what is engineering vs. production data. Comes with the ability to integrate with Manufacturing Execution System (MES) or other external systems to perform key data validation checks.
Description:
Providing a lights-out data loading functionality, data cleansing, and data mapping capabilities the Automated Data Loading module releases IT engineers from tedium. Instead of writing scripts, they can focus on writing the business rules that evaluate the data to determine clean vs dirty data and engineering vs production data. Found in this module, the Smart Logic data cleansing engine addresses data quality issues by mapping and correcting critical fields that are missing or inaccurate as data is loaded into the data warehouse.
Correctly discerning engineering vs production data assures engineers can interpret the test data appropriately. Verifying the data completeness and integrity of the test data enables trusted analysis. Engineers can develop the rules for this verification which reflect their unique factory environment and their product’s development stage. Clean data means the identifiers for the data exist and the data is complete and accurate; clean data equates to a trusted data source, high data provenance. While highly valued for all industry sectors, data provenance is absolutely required for devices found in the automotive and life sciences.
These business rules work behind the scenes to provide your engineers clean data. Once engineers and operators set up the business logic, this module supports automated loading into a factory’s MES or other external systems to perform the key data validation checks. As part of our customer success values, yieldWerx support engineers readily partner with customers as needed to guarantee successful business logic implementation.
First Pass Yield (FPY) is a helpful KPI monitor that helps calculate the effectiveness of manufacturing quality units. FPY which is also known as throughput yield calculates quality units produced as a % of the total units that were in the beginning of the process. The FPY formula in its simplest form looks like.
Improving FPY or throughput yield requires a variety of factors which include understanding the performance of equipment, operators, processes and also having accurate data to track Key-Performance Indicators (KPIs).
Engineering Roles Impacted:
- IT admin
- Product and Test Engineer
- Process Integration Engineer(PIE)
- Yield Enhancement(YE) Engineer
- Assembly Engineer
- Test equipment engineer
Dependencies on other modules:
- Data Integrity
- Completeness of Key Fields