We recently implemented an automated testing framework for Manufacturer Change Orders that cut our regression testing cycle from 3 days to under 6 hours. Our engineering team was drowning in manual MCO workflow validations every release.
The solution leverages Agile SDK with custom PX extensions to simulate MCO lifecycle scenarios. We built reusable test modules covering workflow transitions, approval routing, BOM synchronization, and supplier notifications. The framework runs nightly against our test environment and generates detailed reports.
Key achievement was reducing our MCO validation cycle time by 75% while improving test coverage from 60% to 95%. Now our QA team focuses on exploratory testing instead of repetitive workflow checks. The framework has caught several regression issues before they reached production, particularly around complex approval chains with multiple manufacturers.
Great question. We implemented a test fixture framework that creates isolated test data sets. Each test suite has a setup phase that creates manufacturers, parts, and BOMs with unique naming conventions (TEST_MCO_2025_xxx).
For cleanup, we tag all test objects with a custom attribute and run a nightly purge script. The script queries objects older than 7 days with the test tag and removes them along with their dependencies. We use SDK batch operations to handle cascade deletes efficiently.
We also maintain a golden dataset of reusable test manufacturers and parts that persist across test runs. This hybrid approach - disposable test data for specific scenarios plus stable reference data - has worked well for us.
This is exactly what we need. Can you share more details about your PX extension architecture? We’re struggling with the same manual testing bottleneck for MCOs. How did you handle the workflow state transitions programmatically? Did you use the standard SDK workflow APIs or build custom extensions?
How do you manage test data setup and teardown? MCOs have complex dependencies with manufacturers, parts, and BOMs. We’ve found data cleanup to be a nightmare in our manual testing.