lineage

Lineage in industrial and manufacturing contexts commonly refers to the documented chain of origins and transformations that shows where something came from and how it has changed over time. It is a type of traceability record that can apply to data, materials, parts, equipment, or maintenance events.

Data lineage

Data lineage describes the lifecycle of data as it moves through OT and IT systems. It records how data is created, transformed, aggregated, and consumed across sources such as PLCs, historians, MES, LIMS, QMS, ERP, and cloud analytics platforms.

In practice, data lineage typically includes:

• Data source details (equipment tags, databases, files, interfaces)
• Transformation logic (calculations, filters, mappings, KPI formulas)
• System hops (which applications or services handled the data)
• Timing and sequencing of events (timestamps, processing order)
• Dependencies between datasets (what inputs feed which reports or KPIs)

Data lineage supports validation of analytics, KPIs, and reports by making it possible to reconstruct how a value was produced, which systems it passed through, and which versions of configurations or master data were used.

Material, part, and maintenance lineage

In manufacturing and MRO operations, lineage can also describe the traceable history of a physical item or asset. This is closely related to genealogy and as-built records.

Examples include:

• Material or part lineage: the chain from raw material lots, through intermediate and subassemblies, to finished goods, including work orders, process steps, and inspections.
• Maintenance lineage: the sequence of maintenance actions, repairs, part replacements, and configuration changes performed on an asset over its life.

These forms of lineage are often captured by MES, CMMS/EAM, and MRO systems, and are used to support traceability, investigation of nonconformances, and configuration control.

Operational relevance

Lineage information commonly appears in:

• Audit trails and evidence packages for investigations or reviews
• Root cause analysis and CAPA workflows
• Validation of KPIs and dashboards derived from multiple data sources
• Change control and version governance for recipes, routes, and calculation logic

Maintaining clear lineage typically requires consistent identifiers, timestamps, governed integrations between systems, and controlled changes to transformation logic and master data.

Common confusion

• Lineage vs genealogy: Genealogy in manufacturing often refers specifically to the parent-child relationships of parts and materials in a product structure. Lineage is broader and can include process steps, maintenance actions, and data transformations.
• Lineage vs traceability: Traceability is the overall ability to track and link items or data across their lifecycle. Lineage is the documented chain that provides the detailed path behind that traceability.

Link to ISO 22400 and analytics

When ISO 22400 KPIs are implemented using data lakes or cloud analytics, lineage clarifies which source events, systems, and transformations contributed to each KPI. This supports data governance, validation, and consistent interpretation of performance metrics across MES, ERP, and analytics environments.