Infor M3

MEC Mapping Configuration: Field Transformations, Cross-References, and Validation Patterns

MEC mappings are where raw trading partner data becomes M3-ready transactions. A single purchase order mapping may involve 50-100 field transformations, cross-reference lookups, conditional routing, and validation checks. Getting mappings right the first time saves weeks of production firefighting. This guide covers the patterns that experienced M3 integration architects use to build reliable, maintainable MEC maps.

Field Mapping Fundamentals and Cross-Reference Tables

Every MEC mapping translates source fields to M3 target fields. Simple mappings are direct copies, but production scenarios require cross-reference tables for code translation—converting partner item numbers to M3 item IDs (ITNO), partner ship-to codes to M3 delivery addresses (ADID), and external UOM codes to M3 unit of measure (ALUN). Cross-reference tables in CUGEX1 or custom CRS880-based tables provide the lookup layer.

  • Use CRS882 (Generic Cross-Reference) for standard code translations between partner and M3 formats
  • Map partner item IDs to ITNO via MITMAS (MMS001) alias field or CUGEX1 custom cross-reference
  • Translate external UOM codes (EA, CS, PL) to M3 ALUN values using MMS080 alternate UOM table
  • Configure fallback values for missing cross-references to prevent silent transaction failures
  • Version cross-reference tables alongside MEC maps for coordinated deployment and rollback

Conditional Logic, Calculated Fields, and Validation Rules

Production MEC mappings require conditional logic for routing, calculated fields for derived values, and validation rules that reject bad data before it reaches M3. Conditional routing sends orders to different M3 companies based on partner codes. Calculated fields derive values like requested delivery dates from lead time offsets. Validation rules enforce mandatory fields and format requirements.

  • Implement conditional company routing: map partner warehouse codes to M3 CONO/DIVI combinations
  • Calculate DWDT (requested delivery date) from order date plus partner-specific lead time offsets
  • Validate mandatory OIS100MI fields (CUNO, ORTP, FACI, WHLO) before API submission
  • Use MEC scripting for complex transformations: string parsing, date format conversion, numeric scaling
  • Build reusable mapping fragments for common patterns like address parsing and name formatting

Testing, Versioning, and Production Deployment

MEC mapping changes carry significant production risk. A single field misalignment can corrupt thousands of M3 records before detection. Rigorous testing with partner-representative sample data, version-controlled map deployments, and automated regression testing prevent costly production incidents. Netray AI agents can auto-validate mappings against historical transaction patterns.

  • Test every mapping with 20+ representative documents covering edge cases and optional fields
  • Use MEC's built-in test mode to validate transformations without writing to M3 production tables
  • Maintain mapping version history with rollback capability for rapid production incident response
  • Automate regression testing: run previous month's production documents through updated mappings
  • Monitor post-deployment error rates for 48 hours before confirming mapping release stability

Let Netray's AI validate your MEC mappings against thousands of known patterns—get started today.