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Glossary Tag: leading indicators

data sources
Data sources are the systems, devices, repositories, or files from which data is obtained for use in applications, analytics, reporting, or integration workflows. In industrial and manufacturing environments, data sources commonly include shop floor equipment, control systems, business systems, and manually maintained records.

What data sources typically include

In regulated and industrial operations, data sources often refer to:
- Operational technology (OT) systems such as PLCs, SCADA, DCS, historians, and machine controllers that generate process and equipment data.
- Manufacturing IT systems such as MES, LIMS, QMS, WMS, CMMS, and production scheduling tools that generate and store transactional and event data.
- Enterprise IT systems such as ERP, CRM, and financial systems that provide order, customer, material, and cost data.
- Files and databases including CSV/Excel files, SQL/NoSQL databases, data warehouses, and data lakes used to persist and consolidate information.
- Manual and semi-manual records such as electronic logbooks, digital forms, or structured spreadsheets maintained by operators, engineers, or quality staff.
A data source is defined by both where the data resides or originates (for example, a specific MES instance) and how it is accessed (for example, REST API, OPC UA server, database connection, or file drop).

How data sources are used operationally

In integrated manufacturing environments, data sources are identified, cataloged, and connected so that information can be reused consistently across functions. Typical uses include:
- Feeding KPIs and dashboards such as OEE, NPT, or ISO 22400 indicators from defined, traceable origins (for example, a specific historian tag set or MES production records).
- Supporting regulatory and quality records by tying reports and batch documentation back to original source systems and timestamps.
- Enabling MES/ERP integration by mapping master data and transactional data from clearly defined systems of record.
- Driving operations intelligence and analytics, where models and reports are explicitly linked to named, version-controlled data sources.
In validated or audited settings, it is common practice to maintain a data source catalog that documents each source, its owner, data structures, access method, and intended use so that metrics, reports, and decisions can be traced back to origin.

Relation to KPIs and standards

When defining KPIs, including those based on frameworks like ISO 22400 and locally defined indicators, each metric should reference its underlying data sources. For example, a performance KPI might specify that production quantity is taken from a particular MES table, while machine runtime is taken from a specific historian tag set. Clear linkage between KPIs and their data sources supports consistency, reproducibility, and review in regulated environments.

What data sources are not

It is useful to distinguish data sources from related concepts:
- Data sources are not the same as data models. A data model describes how data is structured and related; a data source is where the data is actually obtained.
- Data sources are not necessarily systems of record. A system of record is the authoritative place for a given data element, while a data source may be a copy, aggregation, or transformed view.
- Data sources are not integration tools. Middleware, ETL tools, and message brokers move data between sources and targets but are not, by themselves, the originating sources of the data.
Common confusion

The term “data source” is sometimes used interchangeably with:
- Data set, which usually refers to a specific collection of data extracted from one or more sources at a given time.
- Connector or interface, which refers to the technical mechanism (for example, an OPC client, JDBC driver, or API client) used to access a data source, rather than the source itself.
In manufacturing and compliance discussions, using “data source” to mean the actual originating system, repository, or device helps maintain clear traceability and accountability.
April 11, 2026
Enterprise-Control System Integration
Enterprise-Control System Integration commonly refers to the coordinated connection of business-level IT systems with plant-floor control and automation systems so information, commands, and status can move reliably across the entire manufacturing organization.

What it includes

In regulated and industrial environments, Enterprise-Control System Integration typically covers:
- Enterprise systems: ERP, supply chain, PLM, quality management, LIMS, and other business or compliance applications.
- Operations and manufacturing systems: MES, batch management, historian, maintenance management, and production scheduling systems.
- Control systems: DCS, SCADA, PLCs, safety systems, and related field instrumentation and devices.
The integration focuses on how these layers share data and coordinate workflows, such as orders, recipes, specifications, equipment status, quality results, and production records.

Typical scope and activities

Enterprise-Control System Integration commonly involves:
- Mapping business objects (orders, SKUs, batches) to control objects (units, lines, tags).
- Standardizing interfaces and data models, often guided by frameworks such as ISA-95 or similar reference architectures.
- Setting up data exchange between MES/ERP and control systems for production orders, recipes, and material movements.
- Collecting process and equipment data from control systems into historians, MES, and analytics tools.
- Coordinating event and exception handling, such as alarms that trigger workflows in quality or maintenance systems.
Operational meaning

Operationally, Enterprise-Control System Integration shows up in scenarios such as:
- An ERP system sending a production order to MES, which then downloads setpoints and recipes to DCS or PLCs.
- A control system automatically recording critical process parameters into an historian and making them available to quality systems for review and disposition.
- Equipment state changes from SCADA driving OEE calculations, electronic batch records, or maintenance work orders in higher-level systems.
In regulated settings, the integration is often designed and documented so that data flows, system boundaries, and responsibilities are clear for audit and validation purposes.

What it is not
- It is not limited to a single protocol or vendor product; it is a multi-layer integration concept.
- It is not just network connectivity; it includes data structure alignment, semantics, and workflow coordination.
- It is not the same as basic device commissioning or PLC programming, which sit at a lower level.
Common confusion
- Versus MES integration: MES integration usually focuses on connecting MES to nearby systems. Enterprise-Control System Integration is broader and explicitly spans enterprise, operations, and control layers.
- Versus IT/OT convergence: IT/OT convergence is a strategic concept about aligning information technology and operational technology. Enterprise-Control System Integration is the practical implementation of data flows and interfaces across those layers.
April 11, 2026
Comparability
Comparability commonly refers to the ability to reliably compare data, processes, or results across different times, locations, products, or systems because they have been generated and recorded under sufficiently similar and well understood conditions.

In manufacturing and regulated operations

In industrial and regulated environments, comparability is often discussed in relation to:
- Measurement data: ensuring inspection, test, or sensor data from different lines, plants, or labs can be compared because the same methods, gages, calibrations, and sampling plans are used.
- Processes and equipment: confirming that two processes, machines, or sites produce outputs that are similar enough (within specified criteria) to be evaluated side by side.
- Batches and lots: assessing whether different lots or batches of material, product, or repairs can be evaluated against each other using consistent specifications and data structures.
- Systems and reports: ensuring MES, ERP, QMS, or LIMS data is structured, timestamped, and defined consistently so KPIs, yield, or quality trends are comparable over time.
Comparability does not mean the data or processes are identical. It means they are generated under conditions that are controlled and documented well enough that observed differences can be interpreted meaningfully, rather than being artifacts of different methods, tools, or definitions.

Operational aspects

On the shop floor and in supporting systems, comparability typically involves:
- Standardized methods for measurement, test, and inspection (for example, shared work instructions, test procedures, and MSA or gage R&R studies).
- Consistent data definitions such as common units, tolerances, part characteristics, and defect codes across sites and systems.
- Controlled change management so that changes to methods, tools, or specifications are documented and time-bounded, allowing before/after comparisons.
- Structured data capture in MES, QMS, or ERP, with clear traceability to product, revision, route, equipment, and operator.
When comparability is established, organizations can more credibly benchmark plants, evaluate process changes, track continuous improvement, and investigate nonconformances across different contexts.

Common confusion
- Comparability vs. repeatability: Repeatability is about getting similar results when the same person, method, and equipment are used under the same conditions. Comparability focuses on whether results from different places, people, or systems can be meaningfully compared.
- Comparability vs. consistency: Consistency describes how stable a process or dataset is over time. Comparability describes whether two sets of data or processes are defined and documented well enough to be evaluated against each other.
April 11, 2026
Rolled Throughput Yield (RTY)
Rolled Throughput Yield (RTY) is a quality and performance metric that quantifies the probability that a unit passes through an entire process, or value stream, without a defect at any step. It is commonly used in Lean, Six Sigma, and manufacturing operations to understand the combined effect of defects across multiple operations, work centers, or process stages.

What Rolled Throughput Yield measures

RTY focuses on the cumulative, step-by-step yield of a process:
- Unit of measure: the fraction or percentage of units that would make it from the start to the end of the process without requiring any rework or repair.
- Scope: spans multiple sequential steps (e.g., machining, assembly, test, packaging), rather than a single operation.
- Defect treatment: counts all defects at each step, including those that are later reworked, because they represent process inefficiency and potential risk.
RTY is typically calculated by multiplying the individual step yields, where each step yield is often expressed as the proportion of units that pass that step with zero defects. In regulated manufacturing, the underlying defect counts and yields are usually drawn from validated systems such as MES, LIMS, or quality management systems.

Operational use in manufacturing

In industrial and regulated environments, RTY commonly appears in:
- Continuous improvement and Lean/Six Sigma projects: to identify where defects accumulate across a process and to compare different process routes.
- Manufacturing dashboards and KPIs: alongside metrics such as First Pass Yield (FPY), OEE, and throughput, to give a more complete view of process robustness.
- Process validation and technology transfer: to assess how changes in equipment, methods, or materials affect overall defect-free flow, even when final product conformance remains acceptable.
- Risk and compliance discussions: to highlight hidden complexity and rework that may affect data integrity, traceability, or batch record completeness.
Because RTY multiplies yields across steps, it tends to be lower than any single-step yield and can be sensitive to data quality. In multi-site or multi-system environments, organizations typically define RTY calculation rules, step boundaries, and data sources explicitly to ensure consistent reporting.

Relationship to other quality metrics

RTY is often used together with related metrics:
- First Pass Yield (FPY): the percentage of units that pass a single step, or sometimes the whole process, on the first attempt without rework. RTY extends this concept across multiple steps and accounts for compounding effects.
- Throughput: the rate at which good units are produced. RTY does not directly measure speed or volume but influences effective throughput by indicating how much work is lost to defects and rework.
- Cost of Poor Quality (COPQ): RTY does not express cost directly, but low RTY typically corresponds to higher internal failure and rework costs.
Common confusion

RTY is commonly confused with:
- Final yield or shipment yield: which counts only units that meet specifications at the end of the process. Final yield can appear high even when RTY is low, because rework and scrap are hidden.
- Average step yield: taking a simple average of individual step yields. RTY is not an average; it is the product of step yields, which captures the compounding impact of defects.
Tie to KPI context

In discussions of core manufacturing KPIs, RTY is often considered a more detailed or advanced quality metric compared to basic defect rates or FPY. It helps explain why operations with apparently strong local yields or OEE can still experience constrained throughput, high rework, or complex deviation and CAPA workloads.
April 10, 2026
P&L
P&L stands for profit and loss. It commonly refers to the financial statement that summarizes an organization’s revenues, costs, and expenses over a defined period (such as a month, quarter, or year) to show whether it operated at a profit or a loss.

What P&L means in industrial and manufacturing contexts

In industrial operations and regulated manufacturing environments, P&L typically refers to one or both of the following:
- The profit and loss statement: A formal financial report used by finance and leadership to track revenue, cost of goods sold (COGS), operating expenses, and net income for a plant, business unit, or company.
- P&L responsibility or accountability: A description of a role’s scope, indicating that the person is accountable for both the cost and revenue impact of decisions, not only for operational metrics. For example, a VP of Supply Chain with end-to-end P&L responsibility is expected to manage inventory, service levels, and capacity in a way that affects overall profitability.
On the operations side, P&L impact links manufacturing and supply chain decisions to financial outcomes. Examples include:
- Changes in yield, scrap, or rework affecting COGS and gross margin.
- Production scheduling, lead times, and on-time delivery affecting recognized revenue.
- Inventory and purchasing decisions affecting working capital and write-offs.
What P&L is not
- It is not a detailed cost accounting system or MES; it summarizes financial results rather than tracking every transaction in real time.
- It is not the same as a balance sheet (which shows assets, liabilities, and equity at a point in time).
- It is not the same as a cash flow statement (which focuses on cash movements rather than accrual-based profit).
Common confusion

P&L vs. budget: A budget is a financial plan or target; the P&L is the actual recorded performance over a period. Operations leaders are often measured on variance between budgeted and actual P&L results.

P&L vs. cost center management: Managing a cost center focuses mainly on controlling expenses within a function (for example, maintenance). P&L responsibility usually covers both revenue and cost decisions across a broader scope, such as a plant, product line, or full supply chain.

Link to the derived context

In discussions of senior supply chain or operations roles, “end-to-end P&L impact” means the role influences both revenue (for example, service levels, capacity to ship) and major cost drivers (for example, manufacturing efficiency, logistics, and inventory). Such roles are often described as having P&L accountability for a site, region, or business unit.
April 10, 2026
COO
COO stands for Chief Operating Officer. In industrial and manufacturing organizations, the COO is a senior executive responsible for day-to-day operations across plants, supply chain, and supporting functions such as quality, maintenance, and logistics, depending on the company structure.

Core responsibilities

In a regulated manufacturing or industrial environment, a COO commonly oversees:
- End-to-end operations performance, including throughput, cost, service, and reliability
- Manufacturing sites and production systems, often including OT, MES, and integration with ERP
- Supply chain operations such as planning, sourcing, production scheduling, and distribution
- Operational aspects of quality and compliance, working with quality and regulatory leaders
- Risk management related to operational continuity, safety, and regulated processes
- Cross-functional coordination between operations, engineering, IT/OT, finance, and commercial teams
The COO role typically focuses on execution and operational strategy implementation, while working alongside the CEO and other executives who may own product, finance, or commercial strategy.

Operational meaning in manufacturing systems

From a systems and workflow perspective, the COO often:
- Sets priorities for investments in manufacturing systems (e.g., MES, automation, data integration)
- Defines or approves high-level operations policies and performance metrics (e.g., OEE, on-time delivery)
- Sponsors programs to standardize processes across sites for consistency and compliance
- Receives consolidated reporting from plant managers, supply chain leaders, and quality leadership
Common confusion
- COO vs. CSCO (Chief Supply Chain Officer): A COO usually owns a broader scope, including manufacturing and sometimes other functions. A CSCO typically focuses on supply chain planning, sourcing, and logistics. In some companies, these roles are combined.
- COO vs. Operations Director/VP: Directors or VPs of Operations usually report into the COO and are responsible for specific regions, plants, or functions rather than company-wide operations.
Context from supply chain leadership

In the context of supply chain and industrial operations, the COO is often the senior executive with end-to-end accountability for operational performance and risk, including how supply chain, manufacturing, and quality execution support overall business objectives.
April 10, 2026
Overall Equipment Effectiveness (OEE)
Overall Equipment Effectiveness (OEE) is a composite metric used to quantify how effectively a piece of equipment, a production line, or a manufacturing area is utilized. It combines three underlying factors: availability, performance, and quality, to express actual productive output as a percentage of the theoretical maximum.

Core definition

In most manufacturing and industrial operations, OEE is commonly defined as:
- Availability: The percentage of planned production time in which the equipment is actually running (accounts for unplanned downtime, changeovers if treated as loss, and certain scheduled stops).
- Performance: The speed at which the equipment runs as a percentage of its ideal or rated speed (accounts for speed losses, minor stops, and slow cycles).
- Quality: The proportion of good units produced versus total units produced (accounts for scrap, rework, and process-related defects).
These are typically combined as:

OEE = Availability × Performance × Quality

The result is usually expressed as a percentage that represents the share of total scheduled time that is truly productive, producing good units at the ideal rate.

Operational meaning in manufacturing systems

In industrial and regulated environments, OEE is often implemented as a key performance indicator across shop floor systems and business systems. It can be:
- Calculated in MES or production monitoring systems using machine signals, production counts, and downtime events.
- Reported at different levels (asset, line, cell, area, or site) for operational review and benchmarking.
- Goverened by documented definitions of “good part,” “ideal cycle time,” and “planned time” to ensure consistent and auditable calculations.
- Integrated with ERP, quality management, and data historian systems to align production performance with scheduling, cost, and compliance records.
Because of its composite nature, OEE is sensitive to how data is modeled. Clear rules are usually needed for classifying downtime, product changeovers, maintenance windows, and quality dispositions so that OEE values are comparable across shifts, products, and sites.

What OEE includes and excludes

OEE focuses on the effective use of equipment time and does not, by itself, fully describe all aspects of manufacturing performance. For example:
- Includes: Losses related to equipment time, speed, and quality output on that equipment.
- May or may not include: Planned downtime such as preventive maintenance or certain scheduled breaks, depending on local definition of planned production time.
- Excludes: Broader factors like material availability, upstream scheduling, logistics delays, or safety performance, unless modeled indirectly via availability losses.
Different plants and industries may adjust the treatment of changeovers, trials, and engineering runs, so written, version-controlled definitions are important, especially in regulated environments.

Use in regulated and validated environments

In regulated manufacturing, OEE calculations often need to be consistent, traceable, and, where required, supported by validated systems. Common practices include:
- Documenting the OEE calculation formula, component definitions, and data sources in standard operating procedures or system specifications.
- Ensuring time stamps, production counts, and quality decisions are traceable to source records.
- Configuring MES, historians, and reporting tools so that OEE logic is applied consistently across equipment and sites.
OEE may appear alongside other core KPIs, such as throughput, on-time delivery, cost measures, and quality indicators, as part of an operational performance metric set.

Common confusion
- OEE vs. utilization: Utilization often refers only to how much time equipment runs relative to total time, without accounting for speed or quality. OEE explicitly includes speed and quality losses.
- OEE vs. availability: Availability is only one factor within OEE. High availability does not imply high OEE if there are speed or quality losses.
- OEE vs. line efficiency or yield: Line efficiency might consider throughput against a plan, and yield focuses on quality. OEE combines time, speed, and quality into one measure, but it is not a replacement for detailed diagnostic metrics.
Relation to performance improvement

OEE is frequently used as a high-level indicator to identify and categorize production losses. While the metric itself does not prescribe actions, organizations often analyze its components (availability, performance, quality) and their underlying loss categories to prioritize improvement projects, maintenance strategies, or process changes.
April 10, 2026
bottleneck

Core meaning

In industrial operations, a **bottleneck** is the resource, operation, or process step with the lowest effective capacity relative to demand, which therefore limits the overall throughput of the entire system.

A bottleneck can be:
– A machine or work center (e.g., a specialized heat-treat furnace)
– A labor-constrained station (e.g., inspection requiring certified personnel)
– A material or component constraint (e.g., a part that is frequently short)
– An information or systems constraint (e.g., slow engineering release or approvals)

The defining property is that increasing capacity or reliability at the bottleneck increases the maximum output of the end-to-end process, while improving non‑bottleneck steps does not raise overall throughput.

How bottlenecks appear in manufacturing workflows

In regulated and complex manufacturing environments, bottlenecks commonly arise at:
– **Special processes**: plating, heat treatment, composite curing, or other limited-capacity operations.
– **Critical inspections and tests**: NDT, first article inspection, or final quality checks with limited qualified staff or equipment.
– **Approvals and documentation steps**: engineering sign‑off, deviation approvals, or batch record review.
– **Shared resources**: tools, fixtures, or test stands used by multiple product families.

Operational signals that a step is a bottleneck often include:
– Persistent queues or high work-in-process (WIP) in front of the step.
– High utilization rates compared to other resources.
– Schedule slippage when this operation is down or delayed.

In many plants, systems such as MES, APS, and operations-intelligence tools are used to identify bottlenecks by analyzing cycle times, WIP accumulation, and resource utilization data.

Boundaries and what it is not

A bottleneck is:
– **About system throughput**, not just local inefficiency.
– **Relative to demand and routing**, not an absolute measure of speed.

It is **not** necessarily:
– The slowest theoretical machine on its own, if that machine still has excess capacity relative to upstream and downstream demand.
– The step with the highest defect rate, unless those defects restrict usable output.
– A one-time disruption (e.g., a short breakdown) if it does not consistently constrain throughput.

Common confusion and related terms

– **Constraint vs. bottleneck**: In many operations and Theory of Constraints literature, a bottleneck is a type of constraint. A constraint is anything limiting the system’s performance (market demand, regulations, or supplier capacity), while a bottleneck usually refers to a specific process step or resource inside the plant.
– **Chokepoint**: Often used informally as a synonym for bottleneck in production discussions.
– **Local efficiency issues**: A step can be poorly run without being a bottleneck if other parts of the process limit throughput first.

Site context: WIP status and bottlenecks

In environments such as aerospace manufacturing, bottlenecks often drive:
– **WIP update cadence**: High-risk or constraint operations may have near-real-time tracking of WIP, machine state, and queue lengths.
– **Scheduling focus**: Sequencing rules and priorities are frequently built around protecting bottleneck utilization and minimizing waits at that operation.
– **Visibility requirements**: MES and shop-floor visibility tools are configured to highlight WIP accumulation and delays at known bottlenecks so that planners and supervisors can respond quickly.

In this context, accurately identifying and monitoring bottlenecks is central to understanding true system capacity and making reliable commitment dates.

April 10, 2026
KPI taxonomy
A KPI taxonomy is a structured classification system for key performance indicators (KPIs). It commonly defines how KPIs are grouped, named, described, and related to each other so that teams use performance measures consistently across departments, systems, and reports.

In manufacturing and regulated operations, a KPI taxonomy often covers categories such as production, quality, maintenance, supply chain, compliance-related monitoring, and financial performance. It may also define attributes for each KPI, such as calculation logic, unit of measure, data source, reporting frequency, ownership, and whether the indicator is leading or lagging.

A KPI taxonomy is not the KPI itself, and it is not the same as a dashboard. The taxonomy provides the organizing structure behind the metrics. Dashboards, scorecards, MES reports, ERP reports, and analytics tools may all use the taxonomy to present data in a more consistent way.

How it appears in operations

Operationally, a KPI taxonomy is often used to align reporting between systems such as MES, ERP, QMS, historian, or BI platforms. For example, one organization may define a common structure for metrics like OEE, scrap rate, first pass yield, schedule adherence, on-time delivery, and nonconformance rate so that the same terms are used across plants or business units.

This helps distinguish:
- the metric name from its formula
- the business category from the data source
- site-specific labels from enterprise-standard definitions
- leading indicators from lagging outcome measures
What a KPI taxonomy usually includes
- standard KPI names and definitions
- groupings or hierarchies of related metrics
- calculation and interpretation notes
- data ownership and system of record
- reporting context, such as line, cell, site, supplier, or enterprise level
- tags for themes such as quality, throughput, downtime, compliance, or risk
Common confusion

KPI taxonomy is commonly confused with a metric catalog, scorecard, or data model.
- A metric catalog is usually a list or register of metrics.
- A scorecard is a reporting view that presents selected metrics for review.
- A data model defines how data is stored and related technically.
- A KPI taxonomy focuses on classification, naming, and semantic consistency.
It is also different from a business process taxonomy. A process taxonomy classifies activities or workflows, while a KPI taxonomy classifies the measures used to evaluate them.

Why the term matters

When the same KPI name is used to mean different things across sites, reports can become difficult to compare. A KPI taxonomy commonly provides the controlled vocabulary needed for clearer governance of performance reporting, especially where multiple systems and regulated records must remain aligned.
April 10, 2026
ISO 22400-2
ISO 22400-2 is an international standard that specifies a set of standardized key performance indicators (KPIs) for manufacturing operations and production management. It belongs to the ISO 22400 family, which focuses on automation systems and integration, particularly performance evaluation in manufacturing environments.

The standard formally defines a catalog of manufacturing KPIs with common terminology, structures, and formulas. These KPIs typically cover areas such as utilization, availability, production time, production output, resource efficiency, and related performance aspects. ISO 22400-2 also describes inputs, calculation logic, and expected measurement boundaries so that different plants, systems, and vendors can interpret and implement the KPIs in a consistent way.

How ISO 22400-2 is used in operations

In industrial and regulated environments, ISO 22400-2 is commonly used to:
- Provide a reference set of manufacturing KPIs when designing MES, operations intelligence, and performance dashboards.
- Align OT and IT stakeholders on KPI definitions for production performance, such as OEE-related measures, utilization, and throughput.
- Support more consistent comparisons across lines, plants, or suppliers by referencing standardized KPI definitions.
- Guide integration work between MES, ERP, historians, and quality or compliance systems, by clarifying required data elements and aggregation rules.
Organizations rarely implement every KPI exactly as described in the standard. Instead, they typically select and adapt a subset of KPIs that fit their products, data availability, validation constraints, and existing OEE or performance frameworks.

Scope and boundaries

ISO 22400-2 focuses on:
- Definitions and calculation structures for manufacturing KPIs.
- Use in discrete and hybrid manufacturing environments, often in conjunction with MES and automation systems.
- Performance measurement at the equipment, line, area, or plant level.
It does not prescribe specific targets, business rules, or management practices, and it is not a quality management system or cybersecurity standard. Instead, it is a technical reference that can be combined with standards such as ISO 9001 or ISA-95 to build coherent performance and reporting frameworks.

Common confusion
- ISO 22400-2 vs. OEE as a concept: OEE (Overall Equipment Effectiveness) is a specific performance metric or family of metrics. ISO 22400-2 is a broader catalog of KPIs, some of which relate to OEE components, but it is not limited to OEE.
- ISO 22400-2 vs. MES standards: ISO 22400-2 defines KPIs, not MES functional requirements. MES standards or models (such as those aligned with ISA-95) may reference these KPIs but cover a wider range of execution functions.
Link to the derived context

In many plants, ISO 22400-2 is used as an authoritative source for a standardized set of manufacturing KPIs. While the standard defines a formal list of KPIs, practical implementations often tailor these definitions to existing MES, ERP, and OEE solutions, especially in brownfield and regulated environments.
April 10, 2026