Automated test for production scheduling fails on MRP run due to BOM mismatch

MES GE Vernova
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1

We’ve built automated regression tests for our MRP run validation in production scheduling module. The test suite worked fine for months, but now consistently fails during the BOM explosion phase. The failure occurs when the automated test tries to validate material requirements against expected outputs.

Our data-driven test cases pull from master data tables, but we’re seeing mismatches between BOM structures and routing data. The test expects specific component allocations but gets different results:


Expected: Component A-123 qty=5 for Parent P-001
Actual: Component A-123 qty=3 for Parent P-001
Test Status: FAILED - BOM mismatch detected

This blocks our release pipeline since we can’t certify the MRP calculations. The BOM and routing sync seems off, but manual MRP runs appear correct. Has anyone dealt with automated MRP testing where master data synchronization causes false failures?

2

I’ve seen this exact scenario. Your test data is probably referencing cached or stale BOM versions while the production system uses real-time data. Check if your test framework is pulling BOM snapshots versus live master data. We had similar issues where our automated tests were validating against yesterday’s BOM structure, but overnight engineering changes updated the actual BOMs. The solution was implementing a pre-test data refresh step that synchronizes test reference data with current master data before each MRP validation run.

3

I’ll share our complete solution since we solved this exact problem six months ago.

Master Data Synchronization Strategy:

First, implement a smart data refresh mechanism that doesn’t blindly reload everything. We created a change-detection service that monitors BOM and routing modifications:


// Pseudocode - Key implementation steps:
1. Query master data change logs for BOMs/routings modified since last test run
2. Compare change timestamps with test baseline snapshot timestamp
3. If changes detected, pull only affected items and their dependencies
4. Update test reference data incrementally (not full reload)
5. Log synchronization actions for audit trail
// See GPSF API: MasterDataChangeService.getModifiedItems()

Data-Driven Test Case Design:

Restructure your test cases to be master-data-aware. Instead of hardcoding expected quantities, calculate them dynamically:


// Test validation logic:
BOM currentBOM = getBOMVersion(partNumber, effectiveDate);
expectedQty = currentBOM.getComponentQuantity(componentId);
assertEquals(actualQty, expectedQty, "Component allocation mismatch");

This makes tests self-adjusting to BOM changes while still catching real MRP calculation errors.

Automated MRP Regression Testing Framework:

Implement three-tier validation:

  1. Pre-flight checks: Verify BOM/routing data consistency before MRP run
  2. Calculation validation: Execute MRP and compare results against dynamically calculated expectations
  3. Post-run analysis: Check for data integrity issues that might cause false positives

For the pre-flight phase, add these validations:

  • BOM effectivity dates cover test scenario date ranges
  • Routing operations reference valid work centers
  • Component lead times are populated
  • No orphaned BOM components (items without valid item master records)

We also added a “baseline drift” report that alerts when test reference data diverges significantly from production master data. This catches synchronization issues before they cause test failures.

Handling Engineering Changes:

Create a notification bridge between your PLM/engineering change system and test automation framework. When ECOs are released that affect BOMs or routings, trigger automatic test baseline updates. This keeps your regression tests aligned with current product definitions without manual intervention.

Implementing this reduced our false failure rate from 23% to under 2%, and we haven’t had a release blockage due to master data sync issues since deployment.

4

That makes sense. We do have a test data snapshot mechanism, but it runs weekly. If engineering makes BOM changes mid-week, our automated tests would be comparing against outdated expected values. How frequently should we refresh the test reference data? Daily seems excessive for our CI/CD pipeline runtime.

5

Don’t just focus on refresh frequency - look at your BOM versioning strategy. In GPSF 2021, production scheduling should respect effectivity dates on BOMs. Your automated tests need to account for this. Are you testing with specific effectivity date ranges? We implemented a hybrid approach: major test runs (nightly) use fresh data pulls, but quick smoke tests use cached data with version checks. If BOM versions mismatch between cache and live system, the test triggers a targeted refresh only for affected items rather than full dataset reload.