Glossary

Identifier mapping

Identifier mapping is the association of one system's IDs with another system's IDs for the same item, record, or entity.

Data Mapping and System Interoperability ERP / MES / PLM Interoperability

Identifier mapping is the association between identifiers used in different systems, data models, or business processes to represent the same real-world object, record, or entity. In manufacturing and regulated operations, it commonly refers to linking IDs such as part numbers, material codes, equipment IDs, batch numbers, supplier IDs, work order numbers, or employee records across MES, ERP, PLM, QMS, LIMS, and other connected systems.

The purpose of identifier mapping is to preserve referential consistency when data moves between systems that do not share the same native key structure. A mapping may be one-to-one, one-to-many, many-to-one, or conditional, depending on how the source and target systems are designed. It can be maintained in middleware, master data services, integration logic, data warehouses, or application-level configuration.

What it includes

Cross-references between internal and external IDs for the same entity
Mappings between legacy and current identifiers after migration or system replacement
Translation of plant-specific, supplier-specific, or system-specific codes into a common reference
Rules for handling alternate identifiers, revisions, prefixes, formatting differences, or composite keys

What it does not mean

Identifier mapping is not the same as changing the identifier itself. It does not require a single universal ID, and it is not identical to data transformation in general. Data transformation may change values, formats, or structures, while identifier mapping specifically concerns which identifier in one context corresponds to which identifier in another.

How it appears in operations

In practice, identifier mapping often appears in integrations where one system must recognize records created or controlled elsewhere. Examples include linking an ERP material number to an MES item ID, matching a supplier lot reference to an internal batch record, or associating a PLM part revision identifier with the manufacturing record used on the shop floor. Accurate mapping supports traceability, genealogy, transaction posting, and consistent reporting across systems.

Common confusion

Identifier mapping is commonly confused with master data management, record matching, and field mapping.

Master data management governs authoritative data and ownership. Identifier mapping is one mechanism used within or alongside it.
Record matching is the process of determining whether two records refer to the same entity. Identifier mapping is the stored relationship once that correspondence is established.
Field mapping defines how data fields align between systems, such as source and target columns. Identifier mapping is narrower and focuses on the IDs that represent entities.

Manufacturing example

A company may store the same serialized component under one identifier in PLM, another in ERP, and a third in MES. Identifier mapping links those values so that engineering, production, quality, and traceability records can refer to the same component without assuming the systems use identical keys.

In aerospace plants, ERP plans and accounts for the business (orders, MRP, costing, finance), while MES controls and records execution on the shop floor (work instructions, data collection, traceability). They must coexist and integrate; trying to make one system do both usually breaks under aerospace compliance, validation, and traceability needs.

What integration standards pair well with ISO 22400 in aerospace manufacturing systems?

ISO 22400 defines KPI semantics, not a complete integration architecture. In aerospace manufacturing, it pairs best with standards for equipment connectivity, enterprise-control integration, quality data, and product/manufacturing information exchange. The right mix depends on brownfield realities, data governance, validation scope, and how much semantic mapping your existing MES, ERP, PLM, QMS, and historian stacks can support.

Which data sources should feed an aerospace compliance dashboard?

An aerospace compliance dashboard should pull from the systems that generate controlled records, not from a standalone reporting layer alone. In practice that usually means QMS, MES, ERP, PLM, document control, training, supplier quality, calibration, and audit systems, with careful attention to record ownership, data quality, timestamp alignment, and validation.

How does a unified KPI framework enable predictive analytics and AI?

A unified KPI framework helps predictive analytics and AI only if it standardizes metric definitions, timestamps, context, and data lineage across systems. It improves model training, comparability, and trust, but it does not fix poor source data, weak integrations, or immature processes. In brownfield plants, value usually comes from governed coexistence, not full platform replacement.

Related Glossary

brownfield environments

Existing industrial sites or systems that are already built and operating, where new equipment, software, or controls are added or modernized.

Integration boundary

The defined limit between systems, processes, or data domains where information is exchanged and responsibility changes.

applicability

Applicability describes which products, configurations, locations, lots or time periods a given requirement, change, or data record is valid for in manufacturing.