Glossary

Master Data Management (MDM)

Master Data Management (MDM) is the governance and technical approach used to define, maintain, and synchronize core business data across systems.

Quality, Compliance and Traceability Non-Conformance Management in Aerospace: Digital Workflows, Compliance, and Continuous Improvement MES, ERP, PLM and Data Integration

Master Data Management (MDM) is the combination of governance practices, data standards, and technical processes used to define, maintain, and synchronize an organization’s core business data across systems. In manufacturing and industrial operations, it focuses on ensuring that foundational data such as materials, parts, equipment, products, recipes, customers, and suppliers is consistent, accurate, and controlled across ERP, MES, PLM, quality, and other OT/IT systems.

MDM typically covers the creation, approval, change control, distribution, and retirement of master records so that different systems reference the same agreed source of truth. It may be implemented using dedicated MDM platforms, or through coordinated processes and integrations between existing enterprise systems.

What master data includes in manufacturing

While exact scope varies by organization, MDM in an industrial context commonly includes:

Material and product master data such as item codes, revisions, units of measure, product families, and regulatory attributes.
Bill of materials (BOM) and routings including structure, operation sequences, and key parameters referenced by MES and planning systems.
Equipment and asset records such as machine identifiers, locations, capabilities, and maintenance classifications.
Customer and supplier data including identifiers, sites, and key contractual or regulatory attributes.
Reference and code sets such as reason codes, status codes, test codes, and standardized attribute lists.

MDM usually excludes highly transactional data like individual work orders, production events, or sensor readings, although those transactions rely on consistent master data values.

Operational role in regulated manufacturing

In regulated or highly controlled manufacturing environments, MDM commonly supports:

Data governance by defining ownership, approval workflows, and change control of master records across functions such as engineering, quality, operations, and supply chain.
System integration by providing harmonized identifiers and attributes so ERP, MES, LIMS, QMS, PLM, and data historians can exchange and interpret data consistently.
Traceability and genealogy by ensuring that part numbers, batch identifiers, and configuration data are unambiguous when linking production, quality, and supply-chain records.
Reporting and analytics by standardizing core dimensions (for example, product, plant, line, customer) used in OEE, NPT, COPQ, and other operational performance metrics.

Common components of an MDM approach

A typical MDM approach in manufacturing environments may include:

Data model and standards defining required attributes, naming conventions, code sets, and validation rules for each master data domain.
Governance and stewardship roles that assign responsibility for creating, reviewing, and approving master data changes.
Workflows and controls for requests, reviews, and releases of new or changed master data, often integrated with document control or PLM processes.
Integration and synchronization mechanisms (for example, APIs, middleware, or MDM hubs) that distribute approved master data to ERP, MES, QMS, and other consuming systems.
Data quality monitoring for detecting duplicates, conflicts, missing values, and misaligned codes across systems.

Common confusion

MDM vs. data warehouse / data lake: A data warehouse or lake stores consolidated data for analysis. MDM governs and synchronizes the core reference records that operational and analytical systems use. They are complementary but not the same.
MDM vs. ERP or MES master data modules: ERP and MES often contain master data, but MDM refers to the broader, cross-system governance and harmonization of that data, which may involve multiple platforms.
MDM vs. document management: Document management controls documents such as work instructions or procedures. MDM controls structured data elements (for example, item codes, attributes) that may be referenced within those documents and systems.

A formal 8D is warranted when a nonconformance or escape is significant enough that simple correction is not sufficient. Typical triggers include recurring issues, customer or supplier escapes, flight or mission critical impact, systemic process failures, and cases needing cross-functional containment, root cause verification, and documented corrective action. The threshold depends on risk, contract requirements, and QMS practice.

What is the difference between corrective action and preventive action in aerospace?

Corrective action addresses a nonconformity or other detected problem after it is found and aims to remove its root cause so it does not recur. Preventive action addresses a potential problem before it happens. In aerospace, the distinction matters because both require evidence, traceability, and change control, and the quality of the action depends heavily on investigation rigor, process maturity, and system integration.

Which stakeholders benefit most from digital non-conformance management?

The biggest beneficiaries are usually quality, manufacturing, MRB, supplier quality, and leadership teams that need faster containment, clearer accountability, and better evidence trails. The actual value depends on workflow design, system integration, data quality, and validation discipline. In brownfield plants, benefits are often limited until NCR workflows coexist cleanly with ERP, MES, PLM, and QMS.

How can we estimate the cost of a non-conformance in aerospace production?

Estimating the cost of a non-conformance in aerospace production requires a structured cost-of-poor-quality model, not a single formula. You typically break costs into direct (scrap, rework, MRB labor), indirect (line stoppage, schedule impact), and external (returns, field findings), and rely on data from MES/ERP/QMS. Precision depends heavily on data quality, routing accuracy, and maturity of time-tracking and traceability systems.

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