Based on my experience with multiple DAM 2022 upgrades, here’s a comprehensive perspective on the migration versus rebuild decision:
Legacy Data Audit Considerations:
Before deciding, perform a thorough audit of your existing planning data. Key metrics to evaluate:
- Data completeness: Are all required fields populated across your 18 months of history?
- Relationship integrity: Do all schedule-to-resource and material-to-order links resolve correctly?
- Customization complexity: How many custom fields and tables have you added to the planning schema?
- Usage patterns: Which historical data actually feeds into your current scheduling algorithms?
Run the Data Quality Analyzer tool and aim for 90%+ integrity score for migration to be viable. Below that threshold, you’re accumulating technical debt that will haunt you in the new version.
Hybrid Cloud Compatibility Strategy:
The hybrid deployment question is somewhat independent of the migration decision, but impacts your approach:
Cloud-appropriate components: User interfaces, reporting, analytics, and long-term data storage work well in cloud with DAM 2022’s improved architecture.
On-premises recommendations: Keep the planning calculation engine, real-time scheduling processor, and direct shop floor integrations on-prem if you need sub-second response times. The network latency for cloud-based real-time calculations can impact production schedule accuracy during high-frequency rescheduling events.
Hybrid configuration: DAM 2022 supports active-active hybrid deployment where planning calculations can run in both locations with data synchronization. This provides failover capability but requires careful configuration of data replication rules.
Workflow Mapping Reality:
This is where the rebuild argument becomes strongest. DAM 2022’s workflow engine is fundamentally different from 2019:
- Event-driven architecture replaces polling-based triggers
- New expression language for business rules
- Different API endpoints for custom actions
- Enhanced state machine capabilities but incompatible with legacy state definitions
Your custom workflows will need rebuilding regardless of data migration approach. Budget 40-60% of your upgrade timeline for workflow reconfiguration. The silver lining is that 2022’s workflow tools are significantly more powerful - you can likely simplify some complex customizations.
Recommended Hybrid Approach:
Migrate selectively rather than all-or-nothing:
-
Master data migration: Bring forward materials, resources, BOMs, and routing definitions. These are relatively stable and migration tools handle them well.
-
Partial historical migration: Import the most recent 3-6 months of schedule history for algorithm training, but archive older data externally. This gives your predictive scheduling enough context without migrating problematic legacy records.
-
Full workflow rebuild: Start fresh with workflows using DAM 2022 patterns. Document legacy logic but don’t try to replicate it exactly - take the opportunity to optimize.
-
Phased go-live: Run parallel systems for 2-4 weeks, comparing planning outputs between old and new. This validates that your migrated/rebuilt configuration produces acceptable schedules.
Business Impact Perspective:
The rebuild approach causes 4-8 weeks of reduced scheduling accuracy as algorithms retrain on new data. For high-mix manufacturing, this can mean 15-20% increase in schedule changes during the transition period. If your business can’t tolerate this disruption, selective migration with aggressive data cleanup is worth the extra effort.
For hybrid cloud deployments specifically, start with a cloud-first architecture design, then selectively move latency-sensitive components back on-premises based on performance testing. Don’t assume on-prem is always faster - DAM 2022’s cloud optimization often surprises people.
Ultimately, the decision hinges on your data quality audit results and business tolerance for transition disruption. Clean legacy data with simple customizations favors migration. Complex customizations with questionable data quality favors rebuild with selective import of critical master data.