Client Context
A manufacturer moving ERP integration and plant data services to GCP relied on SAP, integration middleware, cloud APIs, plant systems, and data stores to support production orders, inventory movements, quality records, and shipment updates. The operating model handled multiple plants and regional interfaces across Europe and Asia, using ERP messages, master data, inventory, and production records. Message sequencing and plant-specific mappings failed in early migration tests. Production downtime windows were limited.
Challenges
- Message sequencing and plant-specific mappings failed in early migration tests.
- Production downtime windows were limited.
- The migration of SAP, integration middleware, cloud APIs, plant systems, and data stores introduced risk across applications, integrations, security, and data.
- Source and target environments lacked a consistent baseline for production orders, inventory movements, quality records, and shipment updates and performance behavior.
- Schema, configuration, IAM, and network differences were difficult to validate manually at multiple plants and regional interfaces.
Solutions Implemented
- Built automated interface comparison, message replay, data reconciliation, and latency testing.
- Executed plant-by-plant dry runs with fast smoke and rollback packs.
- Established pre-migration baselines for functional behavior, data integrity, interfaces, and performance.
- Automated source-to-target reconciliation for ERP messages, master data, inventory, and production records, schemas, record counts, and business totals.
- Embedded smoke, API, configuration, and regression checks into migration and CI/CD pipelines.
- Executed dry runs, load simulations, cutover playbooks, and post-go-live monitoring for production orders, inventory movements, quality records, and shipment updates.
Value Delivered
- Reduced the primary testing or operational effort by approximately [45%], subject to validation against approved engagement data.
- Reduced integration risk across plants.
- Detected configuration, integration, and data issues before cutover.
- Created reusable validation assets for later migration waves.
Impact Highlights
- Achieved an estimated [35%] improvement in cycle time, coverage, or processing consistency; replace with the approved client metric.
- Reduced relevant defects, failures, or rework by an illustrative [20%]; confirm before publication.
- Faster migration waves with stronger operational continuity.
- Enabled faster go/no-go decisions with objective evidence.
