Enterprise systems like SAP, Maximo, or Oracle EAM are designed to be modular, configurable, and scalable. They provide structured frameworks for procurement, maintenance, finance, and logistics. However, these systems all rely on a single foundational assumption:
The data entering the system is structured, standardized, and semantically consistent.
This assumption is where most MRO programs fail.
In asset-intensive organizations, Datos MRO is often created under pressure, by technicians, planners, buyers, or contractors, using free-text descriptions, supplier language, or local naming conventions.
Over time, this leads to duplicated materials, bloated inventories, unreliable analytics, and avoidable downtime.
MRO Data Taxonomy exists to solve this problem, transforming unstructured text into structured, semantically meaningful data that supports operational efficiency and analytics.
Introduction to Data Taxonomies
A data taxonomy is a hierarchical classification system used to organize and categorize data assets.
It provides a structured framework for understanding, managing, and leveraging data effectively. In the context of Master Data Management (MDM), taxonomies are crucial for ensuring data consistency, accuracy, and usability across an organization.
They enable efficient data governance, improve data quality, and facilitate data-driven decision-making.
Types of Taxonomies:
Taxonomies can be broadly classified into two categories:
- Standardized Taxonomies: These are pre-defined, industry-recognized classification systems designed for broad applicability. Examples include eClass, PIDx, and UNSPSC.
- Customized Taxonomies: These are tailored to the specific needs and requirements of an organization, often reflecting unique business processes, product offerings, or regulatory environments.
What is MRO Data Taxonomy?
MRO Data Taxonomy is not just a cataloging or naming exercise.
It is an information architecture and semantic modeling discipline that defines how MRO materials data is classified, described, governed, and interpreted across the enterprise.
At scale, it sits at the intersection of:
- Gestión de datos maestros (MDM)
- Enterprise Asset Management (EAM / CMMS)
- ERP material masters (SAP MM, Oracle, IFS)
- Procurement and supply chain analytics
- Reliability engineering and maintenance execution
Without taxonomy, MRO data is a collection of loosely related strings, e.g.,
“Pump seal – big”
“Seal for centrifugal pump”
“Mechanical seal SS”
To a human, these may look related, but to a system, they are separate records. Taxonomy replaces string matching with structured knowledge, enabling deterministic logic, aggregation, and advanced analytics.
It combines three essential elements:
- Taxonomía → Hierarchical classification (Category → Class → Subclass → Item)
- MRO domain knowledge → Engineering, electrical, mechanical, consumables
- Gobernanza de datos maestros → Standardized attributes, naming, and codes
Together, these elements transform unstructured material descriptions into structured entities with relationships.
Core Building Blocks of an MRO Data Taxonomy
Hierarchical Classification
A hierarchy provides context and inheritance.
Por ejemplo:
Mechanical Equipment → Pumps → Centrifugal Pumps → Mechanical Seals
This structure allows materials to inherit characteristics from their class, enabling consistent behavior across reporting, planning, and analysis.
Controlled Vocabulary (Noun–Modifier–Sub-Modifier)
A controlled vocabulary ensures that the same material is always described the same way.
Noun defines what the item is (Pump, Valve, Sensor)
Modifier defines the functional type (Centrifugal, Ball, Pressure)
Sub-Modifier adds technical specificity (Submersible, SS316, Digital)
This structure reduces ambiguity, improves search accuracy, and supports automation.
Attribute Ontology
Attributes are where taxonomy delivers the most operational value.
Instead of relying on free text, key technical details are captured in structured fields, such as:
- Size, pressure rating, voltage
- Material of construction
- Capacity, flow rate, accuracy
- Connection type and mounting
Attributes allow systems to validate compatibility, detect duplicates, and support advanced analytics.
How Taxonomy Touches Day-to-Day MRO Transactions
MRO Data Taxonomy does not operate in isolation, it directly affects daily operational transactions across the MRO lifecycle.
Material Request and Creation
When a new material is requested, taxonomy ensures that the requester selects an appropriate class before describing the item. This enforces consistency, prevents misclassification, and reduces unnecessary material creation.
Over time, this dramatically reduces duplicate materials and improves catalog trust.
Maintenance Planning and Execution
During maintenance planning, taxonomy enables planners and technicians to find the correct spare quickly by filtering on class and attributes rather than guessing from descriptions.
When a part is issued during maintenance execution, the system records not just “a part,” but what type of part was used. This distinction is critical for reliability analysis and failure pattern identification.
Inventory and Replenishment
Inventory decisions rely heavily on historical consumption.
Without taxonomy, similar items are scattered across multiple records, making demand appear lower or more volatile than it actually is.
Taxonomy allows demand to be aggregated at the appropriate class or subclass level, improving replenishment decisions and reducing emergency procurement.
Adquisiciones y contratación
For procurement teams, taxonomy enables:
Better visibility into what is being purchased
Identification of equivalent or interchangeable items
Supplier consolidation and spend analysis
Instead of negotiating part by part, procurement can negotiate at a category or class level, supported by accurate data.
Standardized vs Customized MRO Taxonomies
Industry standards such as UNSPSC, eClass, or PIDx provide broadly applicable classification structures. They are useful for supplier alignment, procurement reporting, and external interoperability.
However, they are often too generic for maintenance execution and asset-specific decision-making.
eClass
eClass is a comprehensive, internationally recognized product classification standard used primarily in the manufacturing, engineering, and procurement sectors.
It provides a hierarchical structure for classifying products and services, enabling efficient electronic data exchange and improved supply chain management.
Structure: eClass utilizes a four-level hierarchy:
- Segmento: The highest level, representing broad categories (e.g., Electrical Engineering).
- Family: A more specific grouping within a segment (e.g., Cables and Wires).
- Clase: A further refinement of the family (e.g., Power Cables).
- Block: The most granular level, describing specific product characteristics (e.g., Voltage Rating, Conductor Material).
PIDx (Process Industry Data Exchange)
PIDx is a data exchange standard specifically designed for the process industries, including oil and gas, chemicals, and pharmaceuticals.
It focuses on standardizing the exchange of engineering, procurement, and construction (EPC) data.
Structure: PIDx defines a common data model and vocabulary for describing process plant equipment, materials, and services. It uses a combination of classes, properties, and relationships to represent data in a structured manner.
Por ejemplo:
- Clase: Pump
- Property 1: Caudal
- Property 2: Discharge Pressure
- Relationship: Connected to Piping System
Industry Application: Oil and Gas, Chemicals, Pharmaceuticals
Stats: PIDx is supported by major EPC companies and owner-operators in the process industries.
UNSPSC (Código Uniforme de Productos y Servicios de las Naciones Unidas)
UNSPSC is a widely used classification system for products and services, maintained by GS1 US. It provides a hierarchical structure for categorizing goods and services, facilitating procurement, spend analysis, and supply chain management.
Structure: UNSPSC uses an eight-digit code, divided into four levels:
- 1st two digits → Segment (broad industry group)
- 2nd two digits → Family (related product group)
- 3rd two digits → Class (items with similar use)
- 4th two digits → Commodity (exact product or service)
Por ejemplo:
Segment level: Segmento 40000000
Meaning: Industrial & Construction products in general.
Family level: Familia 40150000
Meaning: Fluid & Gas Distribution.
Class level: Clase 40151500
Meaning: Pumps.
Commodity level: Materias primas 40151503
Meaning: Centrifugal Pumps.
Industry Application: Procurement, Spend Analysis, Supply Chain Management
In practice, taxonomy-driven classification frameworks, including those implemented by vendors such as Verdantis, are used to apply standardized and customized taxonomies consistently across MRO master data.
Customized Taxonomies
Customized taxonomies, like MESC, reflect how assets are actually built, maintained, and fail within a specific organization or industry.
They incorporate:
- Asset context
- Maintenance strategies
- Failure modes
- Regulatory and operational constraints
Most mature organizations use standardized taxonomies for external alignment and customized taxonomies for internal execution.
Saudi Aramco (for Example), being one of the world’s largest integrated energy and chemicals companies, requires a highly customized taxonomy to manage its vast and complex asset base.
Their taxonomy is tailored to their specific operational needs, equipment types, and different maintenance practices.
Key Features of Taxonomy:
- Asset-Centric: The taxonomy is primarily organized around physical assets, such as pumps, compressors, pipelines, and vessels.
- Hierarchical Structure: It employs a multi-level hierarchy to classify assets based on their function, type, and characteristics.
- Technical Specifications: The taxonomy incorporates detailed technical specifications, including material types, dimensions, performance ratings, and maintenance requirements.
- Integration with Maintenance Systems: It is tightly integrated with their Computerized Maintenance Management System (CMMS) to facilitate efficient maintenance planning and execution.
Por ejemplo:
- Asset Type: Pump
- Pump Type: Centrifugal Pump
- Application: Water Injection
- Material: Stainless Steel
- Capacity: 500 m3/hr
Noun, Modifier, and Sub-Modifier Example:
- Noun: Pump
- Modifier: Centrifugal
- Sub-Modifier/Type: Submersible
Industry Application: Petróleo y gas
Benefits of Data Attributes in Customized Taxonomy
A customized taxonomy provides significant advantages for data extraction and enrichment, especially in MRO environments where item descriptions are often unstructured, inconsistent, or supplier specific.
By defining organization-specific classes, attributes, and value rules, customized taxonomies enable accurate identification, normalization, and enrichment of critical data attributes such as type, material, size, pressure, and manufacturer.
For example, when extracting data for Compressors or Sensors, a customized taxonomy ensures that attributes like “Capacity, Pressure, Power Rating, Voltage, Measurement Range, Accuracy and connection type” are captured in standardized fields rather than free text.
This improves searchability, duplicate detection, and catalog quality, while also supporting automation through AI/ML extraction tools.
Enriched, attribute-level data allows maintenance teams to select the correct spare parts quickly, improves compatibility checks, reduces incorrect purchases, and strengthens analytics for inventory optimization and reliability engineering.
Artículo | Atributos de los datos | Importancia |
Compressors | Compressor Type: Reciprocating, Rotary Screw, Centrifugal | – Compressor type determines application suitability and performance. – Capacity and pressure ensure the compressor meets system demand. – Power and voltage are critical for electrical compatibility. – Cooling and lubrication types affect maintenance and reliability. – Manufacturer and model number help in sourcing spares and replacements. |
Sensors | – Sensor Type: Pressure, Temperature, Flow, Level | – Sensor type and range define measurement capability. |
Benefits of a Customized Taxonomy:
- Improved Asset Management: Enables better tracking, monitoring, and maintenance of assets.
- Enhanced Data Quality: Ensures consistent and accurate data across the organization.
- Streamlined Procurement: Facilitates efficient sourcing and procurement of spare parts and equipment.
- Reduced Downtime: Minimizes equipment downtime through proactive maintenance and efficient troubleshooting.
The Importance of Nouns, Modifiers, and Sub-Modifiers
The use of nouns, modifiers, and sub-modifiers is fundamental to building effective taxonomies. This approach allows for a granular and descriptive classification of data, enabling precise identification and retrieval of information.
- Noun: The core object or concept being classified (e.g., Pump, Valve, Cable).
- Modifier: A characteristic or attribute that further defines the noun (e.g., Centrifugal, Ball, Power).
- Sub-Modifier: An even more specific attribute that refines the modifier (e.g., Submersible, Stainless Steel, XLPE Insulated).
By combining these elements, a taxonomy can provide a highly detailed and nuanced representation of data, facilitating accurate search, filtering, and analysis.
Comparative Analysis of Taxonomy Systems & Summary
This comparison highlights how UNSPSC, eClass, PIDx, and Customized taxonomies support different master data and business needs.
- UNSPSC is best suited for cross-industry procurement and spend analysis with moderate classification depth.
- eClass offers a more detailed, property-based structure, making it ideal for engineering and product data exchange.
- PIDx focuses on standardized, transaction-driven data specifically for the Oil & Gas industry.
- Customized taxonomies provide maximum flexibility, allowing organizations to tailor data structures to their unique operational and compliance requirements, though with limited external standardization.
Standardized taxonomies provide external alignment, while customized taxonomies ensure operational relevance and precision at the asset and attribute level.
MRO Data Taxonomy is more than a classification system, it is the backbone of reliable, efficient, and data-driven maintenance and operational processes.
By providing a structured, hierarchical, and semantically consistent framework, it turns unstructured material data into actionable knowledge, forming the foundation for effective MRO data cataloguing.
Ultimately, a robust MRO Data Taxonomy empowers organizations to make informed decisions, minimize downtime, enhance asset reliability, and fully leverage advanced analytics and AI-driven insights, forming a foundation for world-class maintenance, procurement, and supply chain excellence.
