In aerospace and defense manufacturing, equipment-related KPIs sit at the core of production visibility, certification readiness, and multi-site reporting. ISO 22400 gives a shared vocabulary for these KPIs, including overall equipment effectiveness (OEE), availability, and utilization, without forcing plants into a single calculation method. For aerospace organizations building a digital thread across MES, ERP, PLM, and quality systems, aligning with the ISO 22400 manufacturing KPI standard helps ensure that equipment performance numbers mean the same thing at every site and supplier.

This article explains how ISO 22400 treats equipment-focused KPIs conceptually, how its OEE-related models (OEEA, OEEB) differ from traditional TPM-style OEE, and how aerospace manufacturers can adopt the terminology without rewriting every KPI formula. The focus is on definitions and structure, not on prescribing a specific way to improve performance.

Why Equipment KPIs Are Central in ISO 22400

The role of equipment behavior in manufacturing performance

ISO 22400 pays particular attention to equipment because, in most aerospace production environments, complex assets drive both throughput and certification risk. A five-axis machining center, composite layup cell, or engine test stand may be the bottleneck for an entire aircraft program or propulsion line. If those assets are not running when planned—or if they run but produce nonconforming hardware—schedule and cost performance quickly degrade.

The standard therefore structures many KPIs around how equipment spends time and what it produces in that time. Time spent in specific states (RUN, STOP, IDLE, SLOW) is aggregated into concepts such as operating time, busy time, and downtime. Quantities produced in those time windows are tracked as good parts, rejections, and rework quantities. From these foundational elements, ISO 22400 defines equipment-oriented KPIs that can be compared across different plants, systems, and products.

For aerospace, this provides a disciplined way to separate questions like “Was the machining center available?” from “When it was busy, did it produce conforming hardware?” That distinction is important when determining whether issues belong to maintenance, production planning, or quality engineering.

Linking equipment KPIs to MOM and enterprise decisions

ISO 22400 places equipment KPIs within the broader context of manufacturing operations management (MOM). KPIs such as availability, utilization, and conceptual OEE are positioned at the level where work orders, routings, and resource assignments are executed—typically managed by MES or similar systems. These indicators feed upstream decisions in enterprise systems without forcing those systems to adopt a particular database or UI technology.

In an aerospace plant, this linkage might look like:

• MES level: Tracks equipment state changes, order execution, and scrap events; calculates ISO 22400-aligned indicators.
• ERP level: Consumes summarized equipment KPIs to refine capacity planning, program cost forecasts, and promise dates.
• PLM / configuration management: Uses production execution and quality-related KPIs to assess manufacturability of new designs and the impact of engineering changes.

By using a common ISO 22400 vocabulary across these systems, aerospace organizations can reason about equipment constraints, schedule risk, and capacity investments without debating what “availability” or “utilization” actually mean.

Conceptual View of OEE in ISO 22400

OEE as a composition of time and quantity indicators

ISO 22400 treats overall equipment effectiveness as a conceptual construct rather than as a single mandated formula. In the standard, OEE is assembled from well-defined time-based and quantity-based indicators. Time indicators describe how much of the calendar period is actually used for productive operation, while quantity indicators describe how much of the resulting output meets quality criteria.

This leads to a layered structure:

• Time-based elements: planned time, operating time, busy time, downtime categories, and related state-derived durations.
• Quantity-based elements: produced quantity, accepted quantity, rejected quantity, and reworked quantity.
• Derived indicators: equipment availability, utilization, and conceptual effectiveness ratios built from combinations of the above.

For aerospace plants, this abstraction allows different product families (e.g., composite structures vs. precision machined parts) to maintain their own cycle-time assumptions and yield profiles, while still reporting high-level OEE-related KPIs in a comparable way across programs.

How OEEA and OEEB models are structured conceptually

Within this conceptual framework, ISO 22400 introduces multiple OEE-related models, often referenced in literature as OEEA and OEEB. Each model describes a different way to compose time and quantity elements into an effectiveness measure while staying within the same terminology set.

Conceptually, OEEA tends to emphasize the relationship between busy time and produced volume, while OEEB more explicitly separates availability-related time losses from speed and quality-related losses. Both models rely on the same building blocks:

• Time structure: definitions of when equipment is considered operating, busy, or down.
• State mapping: rules for assigning RUN, STOP, IDLE, and other states into those time buckets.
• Output structure: distinctions between good pieces, scrap, rework, and test output.

ISO 22400 does not require any aerospace manufacturer to select one model over the other. Instead, it provides a common language so that, for example, a machining center in a European airframe plant and a test stand in a North American propulsion facility can both declare which conceptual OEE model they use and how it maps to their local KPI definitions.

Availability, Performance, and Quality Concepts

Time categories used to express availability

Availability in ISO 22400 is based on carefully defined time categories, rather than on informal labels such as “uptime.” Typical categories include:

• Planned time: the portion of the calendar where equipment is scheduled to be available for production or test.
• Operating time: the subset of planned time when the equipment is in a state that can produce, even if it is not actually busy.
• Busy time: the subset of operating time during which equipment is actively executing work (for example, producing a part or running a test sequence).
• Downtime: periods within planned time where the equipment is not available for production due to failures, setups, or other reasons.

For aerospace, this distinction between operating and busy time is important. A radar test bench that is powered on and ready (operating) but waiting for engineering sign-off or configuration data is not busy, yet it still consumes facility resources and may block other work. ISO 22400’s definitions support KPIs that highlight this difference.

Quantity concepts behind performance and quality factors

ISO 22400 also standardizes how quantities are treated within KPIs. Rather than mixing concepts like throughput and yield, it separates them into well-defined indicators:

• Produced quantity: total units processed by the equipment in a period, regardless of quality.
• Accepted quantity: units that meet defined quality criteria and are released for downstream use (assembly, test, shipment).
• Rejected quantity: units that are scrapped or quarantined as nonconforming.
• Reworked quantity: units that initially failed criteria but are successfully recovered through rework.

From these quantities, performance and quality-related factors are derived. In aerospace and space hardware production, where part genealogy and serial-level traceability are mandatory, tying these quantity concepts back to specific work orders, serial numbers, and configurations is essential. ISO 22400 does not define the traceability system itself, but it ensures that KPIs built on top of that system have consistent meaning.

Equipment States and Time Categories

RUN, STOP, IDLE, SLOW and their meaning

Equipment state models in ISO 22400 act as the bridge from control-system signals to conceptual KPIs. Common states include:

• RUN: equipment is executing productive work, such as machining, drilling, or test execution.
• STOP: equipment is not running, often due to a fault, changeover, or scheduled maintenance.
• IDLE: equipment is ready and capable of running but is waiting for material, instructions, or authorization.
• SLOW: equipment is running below its nominal performance, for example due to conservative feed rates on a new material or risk mitigation on a first article.

In an aerospace environment, these states can be further enriched with domain-specific reasons—such as waiting for nonconformance disposition, engineering change approval, or special process certification. ISO 22400 does not prescribe reason codes, but it ensures that however they are defined, they roll up consistently into state-based KPIs.

Mapping states to operating, busy, and downtime categories

Once state definitions are clear, ISO 22400 describes how to group them into time categories that underlie availability and utilization indicators. A typical mapping might be:

• Operating time: RUN, IDLE, SLOW, and possibly certain test or warm-up states.
• Busy time: RUN and SLOW, where equipment is actively processing a work order or test program.
• Downtime: STOP states that occur within planned time, subdivided into planned and unplanned downtime.

For aerospace manufacturers with highly engineered products, this mapping clarifies discussions where engineering or quality events appear as “downtime” from a scheduling perspective. If a composite cure oven is STOP because of an engineering hold on a material batch, the corresponding downtime can be categorized consistently across sites. That, in turn, supports comparisons across programs and facilities when analyzing bottlenecks in a regulated production network.

Comparing ISO 22400 OEE with Traditional TPM OEE

Where definitions align and where they differ

Traditional TPM-style OEE is often implemented as a product of three factors—availability, performance, and quality—each computed according to locally agreed formulas. ISO 22400 aligns with this general structure but is more explicit about the time and quantity elements that underlie each factor. Instead of declaring a single canonical OEE equation, it formalizes the building blocks and offers alternative composition models.

Alignment occurs at the conceptual level: unplanned downtime reduces availability; reduced speed and micro-stops affect performance; nonconforming output reduces quality. Differences arise in the degree of standardization. TPM implementations sometimes blur distinctions between operating vs busy time or between scrap vs rework, whereas ISO 22400 requires these concepts to be clearly separated and named. For aerospace programs seeking audit-ready KPI definitions, this additional clarity is an advantage.

Avoiding confusion in multi-site OEE reporting

One of the main risks in global aerospace production is believing that OEE numbers are comparable when they are not. Two plants might both report “OEE = 78%,” yet one includes test re-runs in performance losses while another does not. ISO 22400 helps avoid this by encouraging organizations to document which conceptual model they use (e.g., OEEA or OEEB), how states are mapped to time categories, and which quantity indicators are included in each factor.

For a multi-site aerospace or space hardware manufacturer, this documentation should be part of the digital manufacturing infrastructure: MES configuration, KPI catalogs, and integration contracts between systems. When a central team aggregates OEE data from different facilities, they can verify that the underlying ISO 22400 concepts match before drawing conclusions about best-performing plants or suppliers.

Using ISO 22400 Equipment KPIs Without Over-Prescription

Selecting which equipment KPIs to track

ISO 22400 defines a structured catalog of KPIs, but it does not tell an aerospace organization which ones it must use. In practice, plants choose a subset aligned with their operational priorities and regulatory context. A few examples:

• Engine test facilities may emphasize utilization and test-cycle adherence KPIs, since test cell access is a scarce resource.
• Precision machining centers may focus on availability and first-pass yield to manage capacity against long-cycle aerospace parts.
• Composite curing operations may track oven utilization and batch conformance indicators, as cure cycles often drive schedule risk.

All of these KPIs can be named and structured according to ISO 22400, even if not every KPI from the standard is implemented. That approach gives aerospace manufacturers a consistent conceptual basis while retaining flexibility.

Communicating definitions across plants and suppliers

The greatest value of ISO 22400 in aerospace manufacturing often appears at the boundaries between organizations: between a prime contractor and its tiered suppliers, or between an OEM and its MRO network. When contracts or performance reviews reference KPIs like equipment availability or utilization, tying those terms to ISO 22400 definitions reduces ambiguity.

Practically, this may involve maintaining an ISO 22400-aligned KPI dictionary within a digital manufacturing platform. Each KPI entry describes which states, time categories, and quantity indicators are used; which OEE-related model (if any) is assumed; and how the KPI is reported. When onboarding a new supplier or bringing a new plant into the network, that dictionary becomes the reference for configuring local MES and reporting systems, and it complements broader discussions of standardized KPIs as described in the ISO 22400 manufacturing KPI standard hub content.

Practical Considerations for Aerospace and Space Hardware Production

Integrating ISO 22400 concepts into MES and digital thread

To make ISO 22400 actionable, aerospace organizations embed its concepts in their MES and digital thread architecture. At the MES layer, equipment states, order events, and quality decisions are captured with sufficient granularity to derive ISO 22400-aligned time and quantity indicators. At higher layers, these indicators are associated with specific configurations, serial numbers, and engineering baselines, tying equipment performance back to the product definition.

In practice, this could mean:

• Standardizing state models and time-category mappings across all test stands or machine types in a program.
• Ensuring that part genealogy records link nonconforming quantities to the exact equipment and time window where they were produced.
• Configuring dashboards so that availability and utilization KPIs are derived from the same ISO 22400 building blocks across different plants.

This creates a consistent KPI layer in the digital thread, improving both operational decision-making and audit readiness.

Working within AS9100 and regulated environments

AS9100 and related aerospace quality requirements emphasize documented processes, traceability, and evidence-based decision-making. ISO 22400 contributes by providing standardized, auditable definitions of what key equipment-related KPIs mean. While AS9100 does not mandate specific OEE values or formulas, it does expect organizations to monitor and control processes that affect product quality and delivery.

By adopting ISO 22400 terminology, an aerospace manufacturer can demonstrate that KPIs used in management review and continuous improvement activities are consistently defined across programs and facilities. This reduces the risk that different sites interpret the same KPI differently during customer or regulatory audits and supports clear linkage between process performance and quality outcomes.

Balancing standardization with local optimization

Finally, ISO 22400 is explicit about its own limits: it standardizes definitions, not business strategy or improvement methods. Aerospace plants remain free to set their own OEE targets, to prioritize certain KPIs over others, and to implement local optimization practices aligned with their mix of programs and technologies.

The practical balance is to keep KPI definitions standardized (names, time and quantity concepts, state mappings) while allowing each plant to decide how aggressively to improve them. A propulsion test center and a structures assembly plant may share the same availability definition but choose different thresholds for what counts as acceptable performance. ISO 22400 supports that diversity by providing a common measurement language rather than enforcing a uniform scorecard.

Modeling Manufacturing KPIs with ISO 22400: Time, States, and Quantities

ISO 22400 gives aerospace manufacturers a precise vocabulary for how key performance indicators (KPIs) are structured, not just what they are called. For production, MRO, and space hardware lines that must coordinate across multiple plants, suppliers, and digital systems, this structure is what keeps “utilization” or “availability” comparable from one site to another. This article explains how ISO 22400 models KPIs from raw signals through indicators to aggregated KPIs, and how time categories, equipment states, and quantity relationships shape data models used in aerospace manufacturing platforms such as ISO 22400 manufacturing KPI frameworks.

The focus here is conceptual: how to design a KPI model that is faithful to ISO 22400, while leaving room for aerospace-specific metrics, regulatory constraints, and digital thread requirements.

From Raw Data to Standardized KPIs in ISO 22400

ISO 22400 distinguishes clearly between what happens on the factory floor, how it is captured, and how it is eventually represented as KPIs. For aerospace engineering and manufacturing teams, understanding these layers is crucial for building traceable, auditable performance data across complex, multi-part assemblies and long product lifecycles.

Raw signals: events, counters, and timestamps

At the lowest level, production systems emit raw signals. These are direct outputs from control systems, machine controllers, test rigs, automated fastening cells, and inspection stations. Typical examples include:

• Binary equipment status (ON/OFF, RUN/STOP bits)
• Cycle counters (number of fastening cycles, pressure tests, or cure cycles completed)
• Timestamps for state transitions (start of test, end of cure, pause/resume events)
• Piece count pulses (part passed a sensor, panel left a station)
• Alarm and interlock events (door open, vacuum loss, E-stop)

These signals are typically stored in historians, SCADA archives, or MES event logs. On their own they are not KPIs; they are facts about what equipment did and when. ISO 22400 treats them as the foundation from which more meaningful indicators are derived.

Derived indicators built from raw signals

Derived indicators are intermediate measures created by processing raw signals. Common examples in aerospace environments include:

• Time spent in a given equipment state (e.g., minutes in RUN vs. IDLE for a drilling cell)
• Duration of a production order, operation, or test sequence
• Total quantity produced, accepted, rejected, or reworked for a work order
• Number of changeovers or configuration switches for a test stand

These indicators are often calculated in MES or a manufacturing data platform by grouping raw events by order, work center, or resource and applying rules aligned with ISO 22400 definitions. The critical point is that indicators are still not KPIs; they are standardized building blocks from which KPIs are constructed.

Aggregated KPIs as standardized conceptual objects

KPIs in ISO 22400 are selected, aggregated indicators that have a specific meaning in manufacturing operations management. Examples include equipment utilization, order execution effectiveness, and quality-related performance indicators. They are characterized by:

• Clear definitions: what they measure and which indicators they depend on
• Time behavior: whether they apply to a shift, day, campaign, or order lifecycle
• Scope: work unit, line, area, plant, or enterprise
• Intended users: operator, supervisor, engineering, or management

An ISO 22400-aligned aerospace data model therefore needs explicit objects (or tables) for raw events, derived indicators, and KPIs, with traceable relationships among them. This traceability is especially important in regulated environments (e.g., AS9100-compliant plants) where performance numbers must be auditable back to their underlying production evidence.

Time-Based Foundations of ISO 22400 KPIs

Time is the backbone of many ISO 22400 KPIs. For aerospace and defense manufacturing—where long takt times, complex assemblies, and certification-critical tests are common—time structure must be modeled carefully to avoid misleading utilization or turnaround metrics.

Planned time vs. actual time

ISO 22400 draws a strong distinction between planned time and actual time:

• Planned time represents the scheduled availability of a resource or work unit—for example, a composite layup cell planned to run from 06:00 to 14:00 with a specific crew and product mix.
• Actual time captures what really happened during that horizon—when the cell was running parts, in setup, in scheduled maintenance, blocked by missing material, or waiting for quality signoff.

In a compliant data model, planned time and actual time should be represented as separate but related concepts, often linked by production order, resource, and calendar. KPIs such as utilization or schedule adherence draw on both, making it essential that the model preserves their differences.

Busy, operating, and downtime categories

ISO 22400 defines multiple time categories, including concepts such as busy time, operating time, and various forms of downtime. While the exact categorization is specified in the standard and supporting literature, the typical aerospace interpretation includes:

• Operating-related time: intervals when the resource is technically able to operate, regardless of whether it is currently producing.
• Busy time: sub-intervals of operating-related time where the equipment is actively performing its intended operation (e.g., machining, curing, testing).
• Planned downtime: scheduled maintenance, calibration, or qualification runs that intentionally take the resource out of normal production.
• Unplanned downtime: failures, quality holds, missing parts, and other disruptions.

In KPI modeling, these categories are usually computed from sequences of equipment states and calendar rules. For example, a test stand in state RUN during planned production hours contributes to busy time, while the same RUN state during an off-shift period might be treated differently depending on the organization’s definition of planned operating time.

Mapping time categories to performance concepts

Many ISO 22400 KPIs can be understood as relationships among these time buckets. For instance, indicators related to utilization, availability, or schedule fulfillment can be conceptually defined as ratios involving busy time, operating-related time, and planned time. ISO 22400 does not mandate specific formulas; instead, it describes which time concepts are relevant to a given KPI.

For aerospace plants, this mapping has practical implications:

• Long-duration processes like autoclave curing must clearly distinguish between active process time and waiting or setup intervals.
• Shared resources (e.g., metrology labs) require unambiguous rules for how time is allocated to different orders and programs.
• MRO lines may need different time categorizations for turnaround commitments versus deep-dive inspections.

A data model aligned with ISO 22400 therefore needs explicit structures for time segments, their categories, and their relationships to equipment, orders, and shifts.

Equipment States and Their Role in KPI Calculation

Time categories are derived from equipment states, which act as the bridge between automation signals and management-level KPIs. For aerospace and space hardware production, properly modeling these states is critical when multiple systems (test cells, tooling, handling robots) must report performance in a consistent way.

RUN, STOP, IDLE, SLOW and other state concepts

ISO 22400 uses conceptual states such as RUN, STOP, IDLE, and SLOW to characterize what equipment is doing. In practice, an aerospace facility might map detailed PLC codes or machine-status words into these generalized states:

• RUN: machine is executing its defined operation on a work order (e.g., drilling a fuselage panel, performing a structural load test).
• STOP: equipment is halted and not available to produce; cause codes may include fault, safety stop, or interlock.
• IDLE: equipment is technically available but not currently processing—waiting for material, schedule, or operator.
• SLOW: equipment is operating below its defined nominal rate, potentially due to conservative process settings, rework, or part complexity.

These conceptual states do not replace detailed fault codes or process statuses; they sit above them as standard categories that can be interpreted consistently across sites and vendors.

How states map to standardized time buckets

To compute ISO 22400-aligned indicators, each interval in which a resource is in a given state is mapped to one or more time categories. For example:

• RUN during planned production is usually part of busy time.
• STOP during planned production may fall under unplanned downtime.
• IDLE within planned operating hours might correspond to waiting or micro-downtime categories.
• RUN during calibration may be counted as planned downtime or a specialized category depending on policy.

The mapping logic is where MES, SCADA, and integration platforms implement business rules. ISO 22400 describes which combinations of time and state concepts a KPI depends on, but it leaves organizations free to tailor detailed mapping as long as the conceptual meaning is preserved.

Consistency across automation and MES systems

In aerospace manufacturing, it is common to operate mixed equipment fleets—legacy test stands, new digital workstations, and custom rigs from different suppliers. Without a clear state model, each system may use its own vocabulary, making multi-site KPIs impossible to compare.

By adopting ISO 22400 state concepts as a common abstraction, manufacturers can:

• Map heterogeneous PLC and controller states into a unified state set.
• Ensure that utilization or downtime KPIs mean the same thing for a composite cell and a final assembly line.
• Provide auditors and customers with a transparent explanation of how performance metrics are constructed.

Platforms that integrate MES, historians, and supervisory control—such as a digital infrastructure used in aerospace plants—benefit from keeping this state model explicit and configuration-driven, rather than hard-coding vendor-specific semantics.

Quantity-Based Elements: Good Units, Scrap, and Rework

While time is central to many KPIs, ISO 22400 also structures quantity-based elements—produced units, scrap, and rework—especially important in aerospace where traceability, serial-number control, and configuration management are mandatory.

Material-related quantities in ISO 22400

ISO 22400 introduces standardized notions of material-related quantities such as:

• Input quantity: material or units entering an operation or work center.
• Output quantity: material or units leaving the operation, which may be further categorized.
• Good quantity: output that meets defined quality requirements and is accepted into the next step or inventory.
• Scrap quantity: output that cannot be used and is discarded or downgraded.
• Rework quantity: output that requires additional processing before acceptance.

In aerospace, these quantities must often be tracked at the level of serialized components, build positions, or configuration-controlled assemblies, not just bulk counts. An ISO 22400-aligned model should therefore link quantities to orders, product definitions, and serial numbers while preserving the standard’s conceptual roles.

Relating quantity measures to time categories

Many performance indicators relate quantities to time: for example, output per hour, accepted units per shift, or rework load relative to busy time. ISO 22400 encourages expressing these relationships using time categories and material-related quantities that have consistent definitions.

In an aerospace setting, such relationships are crucial when comparing:

• Different programs with varying levels of complexity and inspection intensity.
• Prototype phases with high rework levels against stabilized series production.
• MRO lines handling different fleet ages and modification packages.

By modeling time categories and material quantities explicitly, engineers can construct KPIs that distinguish between true performance shifts and changes driven by mix or configuration.

Conceptual links to quality and throughput KPIs

Quality-related KPIs—such as first-pass yield or defect ratios—are conceptually built from good, scrap, and rework quantities. Throughput indicators combine these quantity measures with time segments to highlight the pace at which acceptable units flow through the system.

ISO 22400 does not prescribe how aerospace organizations should act on these KPIs, but it ensures that the meaning of “good quantity” or “rework” is unambiguous. When a prime contractor and a tiered supplier both reference ISO 22400-aligned definitions, their reported quality and throughput figures can be compared or combined without semantic confusion.

Designing a Data Model Aligned with ISO 22400

For architects and data engineers building aerospace manufacturing platforms, the key challenge is turning ISO 22400 concepts into a coherent data model that remains stable over time and flexible enough for program-specific extensions.

Representing indicators, KPIs, and relationships

A practical ISO 22400-aligned model typically introduces distinct structures for:

• Events and states: raw signals, state transitions, and alarms, linked to equipment, order, and timestamp.
• Time segments: contiguous intervals of a given state and category (busy, planned downtime, etc.).
• Quantity records: material movements and quantity outcomes per order, operation, and resource.
• Indicators: computed aggregates (e.g., total busy time, scrap quantity) with clear derivation rules.
• KPIs: structured objects that reference indicators, define their scope, and capture metadata such as unit of measure and trend direction.

Keeping indicators and KPIs separate is important. It allows aerospace plants to introduce new KPIs—such as program-specific readiness metrics—without disrupting the underlying event or indicator structures that support other standards and reports.

Dealing with multiple levels: work unit to plant

ISO 22400 is consistent with hierarchical models used in manufacturing integration standards. KPIs can be defined at multiple levels: work unit, work center, area, site, or enterprise. Aerospace operations often add another set of slices: by program, platform, major assembly, or aircraft tail number.

To reconcile these views, a robust data model should:

• Maintain clear relationships between equipment and organizational units (lines, cells, areas, plants).
• Support aggregations by physical hierarchy (e.g., test stands in a lab) and by logical groupings (e.g., all resources supporting a specific program).
• Allow KPIs to be defined at one level and rolled up or drilled down without redefining their meaning.

This multi-level design is especially important for OEM–supplier networks, where each organization may report ISO 22400-based KPIs at different granularities while still needing a consistent structure for contract performance reporting.

Supporting future extensions without breaking the model

ISO 22400 intentionally does not cover every aerospace-specific metric. Plants may need indicators tied to regulatory audits, customer-specific milestones, or advanced digital thread use cases. A well-structured data model should therefore:

• Store KPI definitions as configurable metadata rather than hard-coded columns.
• Allow additional attributes (e.g., safety relevance, certification impact) to be attached to KPIs.
• Keep clear lineage from KPIs back to raw events, enabling review when definitions change.

This approach lets organizations extend their KPI portfolios—adding, for example, metrics focused on engineering change-cycle time or test re-run rates—while maintaining alignment with the ISO 22400 concepts already in use.

Using an ISO 22400-Aligned Model in Connected Environments

Aerospace manufacturing operates as an interconnected network of systems: ERP for orders and contracts, PLM for product definition, MES for execution, QMS for nonconformance and corrective action, and specialized tools for test, inspection, and configuration management. ISO 22400 provides the shared KPI vocabulary these systems can use when exchanging performance data.

Interfacing with ERP, MES, SCADA, and historians

In a connected environment, each system contributes part of the data needed to construct ISO 22400 KPIs:

• ERP supplies production orders, planned quantities, due dates, and sometimes planned calendars.
• MES orchestrates operations, tracks execution, and records completion quantities and statuses.
• SCADA and historians capture real-time equipment states, alarms, and process variables.
• QMS manages inspection results, nonconformances, and dispositions that influence good/scrap/rework quantities.

An ISO 22400-aligned manufacturing data infrastructure must consolidate these sources, align identifiers (orders, resources, serial numbers), and then compute indicators and KPIs according to the standard’s conceptual model. The goal is that a utilization figure or quality KPI computed from this integrated dataset carries the same meaning regardless of which plant, supplier, or system contributed the inputs.

How platforms like an aerospace digital operations layer consume and expose KPI structures

A digital manufacturing platform used in aerospace environments typically implements ISO 22400 concepts in its data layer while providing domain-specific experiences on top. It may:

• Ingest state and quantity data from existing MES and test systems.
• Normalize time, state, and quantity semantics to align with ISO 22400.
• Expose KPIs through dashboards, APIs, and reports that can be filtered by program, tail number, or supplier.

Because the KPIs are grounded in the standard’s structure, engineering and operations teams can compare performance more reliably across programs, plants, and external partners, even if their underlying automation landscapes differ.

Maintaining conceptual purity when adding custom KPIs

Aerospace organizations nearly always need KPIs beyond the 34 defined in ISO 22400-2—examples include metrics for airworthiness signoff cycle time, configuration-change backlog, or digital thread completeness. When adding such KPIs, it is important to maintain a clear separation:

• Label ISO 22400-conformant KPIs explicitly, including references to the relevant parts of the standard where appropriate.
• Mark organization-specific KPIs as custom while still building them on the same indicator and state/quantity structures.
• Document how any custom KPIs relate to, or differ from, standard KPIs to avoid confusion in supplier and customer reporting.

This approach preserves the integrity of ISO 22400 semantics while still giving aerospace plants the flexibility they need for regulatory, contractual, and engineering-driven performance measures.

Conclusion: ISO 22400 as a Structural Guide for Aerospace KPI Modeling

ISO 22400 does not tell aerospace manufacturers which KPIs they must use or how to run their factories. Instead, it defines how KPIs should be structured—how time categories, equipment states, and quantity concepts relate to each other and to the underlying raw data. By modeling these elements explicitly, aerospace organizations can build performance reporting that is consistent, auditable, and interoperable across plants, programs, and suppliers.

For data architects and engineering teams, the value of ISO 22400 lies in treating KPIs as well-defined conceptual objects rather than ad hoc formulas. That discipline makes it possible to align digital thread initiatives, supplier scorecards, and internal improvement programs around a shared, standards-based understanding of manufacturing performance.