Siloed processes, absent protocols and data management across different systems inevitably lead to data quality concerns, among which, “missing” or “Absent” data is one of the most critical challenges in any given maintenance operation.
This absent data can pertain to spare parts, fixed assets, MRO vendors and have implications across maintenance processes including work order management, maintenance scheduling, inventory management and so on.
Fixing these problems isn’t as simple as patching a few fields or adjusting a format here and there.
To really get the data into usable shape, you need a deeper approach, one that blends smart data cleansing techniques for MRO with solid enrichment work.
When done properly, this kind of effort helps fill in missing technical details, smooth out inconsistencies, and bring everything into a cleaner, more uniform structure.
Only then can maintenance teams rely on the data for planning and day-to-day operations.
Types of MRO Enrichment
MRO data enrichment can take several different forms. The exact type depends on where the information is coming from, the structure of the data model you’re enhancing, and the specific attributes you’re adding or correcting.
These categories often overlap, and most organizations end up using a mix rather than just one.
We list the most common data enrichment methods that we employ here at Verdantis
Spare Parts Data Enrichment
The spare parts data, typically resides in the MRO materials master in any given SAP ERP system or an Item master data in any given Oracle ERP system.
MRO Spare parts, consumables and ancillaries make up a bulk of this data set.
Over time, due to a substandard data management practice or a total absence of data stewardship, especially during urgent procurement requirements, this data is not uploaded in the system comprehensively.
This results in data records that are missing key details like the parts’ attributes, units of measure, features, specifications, category and various other key information that are required for decision making throughout the maintenance process.
In some cases, a procurement request cannot be made in the absence of these details.
In other cases, exceptions are made, and a procurement request is processed even though the MRO records don’t exist in the ERP system in the first place.
Enrichment from Public Sources
Thanks to innovations in Agentic AI and purpose-built AI agents, software solutions can now autonomously enrich MRO data in bulk – this includes data specific to spare parts.
Verdantis’ purpose-built MRO Data Enrichment agent Auto Enrich AI surfs the web, identifies verified supplier catalogues, looks up the incomplete part record and fetches key information like Manufacturer Part Number, Manufacturer Name and related information like specifications, attributes, units of measure etc.
Moreover, the agent autonomously updates the information directly into the MRO master Data, with a human in the loop for review and approvals
Here’s a video showcasing how AutoEnrich works in real time. We’ve attached a demo of the AI agent in action at the end of this article. This same enrichment can be processed for multiple records in bulk across 1000s of records.
Enrichment from First Party Sources
Another common data source used for enriching MRO data sets are first party sources, enterprises with complex production operations already store this data in sources like;
A digital record of all the parts and components required for upkeep of a given piece of machinery
These include engineering specifications, equipment drawings etc
What exactly is a work order? A document detailing what is to be done and how is it to be done.
In any manufacturing organization with plant sites and production facilities, a large number of work orders are issued.
This work order data is heavily used by maintenance technicians or the front-line workers to record every task they perform, whether it’s a part replacement or an operation executed.
This information becomes a vital historical log, detailing which spare parts were used, their specifications, the conditions under which they were used, and the assets or equipment they were associated with.
As mentioned above, it’s not uncommon, especially in urgent procurement requirements, to make procurement requests outside of standard processes.
In such cases, one may want to create or update the MRO database with spare parts data from invoices.
Much of this critical information is often trapped in unstructured documents, sometimes with gaps or outdated details, and extracting it manually is both time-consuming and resource-intensive, especially when using traditional methods.
However, purpose-built AI agents that are built for advanced document processing, can now understand the “context” within a complex document and can extract structured data from them.
This can then be used to update in the MRO materials database.
The video below showcases Auto Doc AI, an AI agent that can process hundreds of documents at once and extract structured information in the form of an excel table, JSON file or any other format and be updated directly within the MRO master data
Enrichment of Fixed Assets
Fixed assets are at the heart of any maintenance operation, be it any equipment like a centrifugal pump used in a chemical plant, or any machinery like an air compressors or boilers, or a site infrastructure that make production and service delivery possible.
If this data in inaccurate or incomplete, with huge information gaps, it can cause real headaches.
- Missing specifications? Like a CP-200X pump model recorded, with an operating power of 8 bar, but missing flow rate, and motor power.
- Inconsistent Naming? An asset recorded as “Pump-01” in one system, “PMP_1_Main” in another, and “Main Feed Pump” in a third.
- Unclear Classifications? A diesel generator listed under “Electrical Systems,” even though it should fall under “Power Generation – Generators.
These can disrupt preventive maintenance, skew asset-lifecycle tracking, and lead to unnecessary spending.
To avoid these problems, organizations need a solid approach to managing asset master data, one that keeps information accurate, consistent, and dependable.
Enrichment from Public Sources
To strengthen the quality of fixed-asset data, Verdantis’ agent, Auto Enrich, supplements the internal records with information sourced from reliable external repositories.
These include OEM databases, public engineering catalogues, industry standards libraries, and other authoritative reference sources.
By tapping into these repositories, the enrichment engine can auto-populate the missing or incomplete details, such as manufacturer and model information, technical specifications of any part, industry classification codes (UNSPSC, ECLASS, etc.), power ratings, dimensions, and other essential attributes.
Each asset record is then validated and aligned with standardized descriptors taken from these trusted sources. This ensures that the asset master is complete, consistent, and technically accurate.
With a clearer and more uniform dataset, it becomes easier for organizations to support preventive maintenance planning, reliability modelling, asset lifecycle management, financial tracking, and other critical operational processes.
Enrichment from First Party Sources
In most organizations, asset information already exists within their own systems, but it is often scattered across multiple formats and repositories.
Verdantis’ AutoDoc agent extracts this information by reading technical documents, engineering drawings, scanned files, PDFs, and other unstructured materials.
Using advanced language processing, it identifies key fields such as asset type, performance limits, installation details, and historical usage data, and then converts them into clean, structured records.
The agent also maps material and spare-part data, establishing accurate parent–child hierarchies along with the vendor details through which these parts were procured.
Here is a video that shows how the agent extracts the data from different pdfs and maps it to the BOM data:
What makes fixed asset enrichment truly impactful is its integration across other domains. Verdantis synchronizes enriched asset data with:
- Spare parts repositories (e.g., linking assets to required spares and alternates)
- Maintenance planning systems (e.g., aligning assets with preventive schedules)
- Financial systems (e.g., for depreciation tracking, asset valuation, or audits)
This multi-system synchronization ensures that enriched asset data is usable across departments, delivering cross-functional value from engineering to finance.
Including historical maintenance logs and usage trends.
That link specific assets with their required parts
That document historical performance
These can be mined for original procurement data including CAPEX classification, asset valuation, and depreciation schedules.
That hold configuration and dimensional details
These can reveal original asset specs, warranties, and vendor data.
RFID or barcode-based data repositories often hold important identifiers.
Enrichment of Supplier Data
A reliable supplier master is essential for reducing procurement risk, maintaining compliance, and making informed sourcing decisions.
Yet many MRO-heavy organizations struggle with issues such as duplicate supplier entries, missing contact details, and inconsistent naming formats, all of which weaken purchasing efficiency.
Enrichment from Public Sources
To strengthen the quality of supplier records, Verdantis uses its AI-driven agents to reference trusted external sources, including public business registries, official supplier websites, compliance databases, and product catalogues.
By cross-checking information against these repositories, the system can automatically capture key details such as:
- Legal entity names: General Electric Company
- Supplier type (OEM, distributor, service provider, etc.): OEM – Siemens, Distributor – Fastenal, Service Provider – Emerson
- Country of registration: Germany (for Siemens AG)
- Contact information (email, phone, address): [email protected]
- Quality certifications and relevant industry memberships: ISO 9001, API membership
These checks also help confirm whether a supplier is active, allowing the system to flag obsolete, inactive, or potentially duplicate vendor entries.
This makes it easier to review and compare past data from the procurement master, such as historical pricing, delivery timelines, and quality performance, so that organizations can make informed decisions about which suppliers to continue working with.
Enrichment from First Party Sources
Most enterprises already possess valuable supplier information, but it is typically spread across POs, invoices, contracts, sourcing portals, and various procurement tools. Verdantis’ AI agent brings this scattered information together by reading and structuring data from:
Reveal vendor terms, referenced part numbers, order patterns, and delivery timelines.
Show fulfilment status, discrepancies, and applied payment terms.
Contain negotiated conditions, compliance clauses, and service-level expectations.
Provide visibility into vendor performance, pricing behaviour, and availability.
By compiling and standardizing this information, the AI creates a unified supplier profile that reflects real-world transactional behaviour as well as contractual relationships.
This can include linking duplicate or alternate supplier entries to a single global vendor ID and organizing product-to-vendor mappings.
Verdantis also supports end-to-end synchronization of the enriched supplier data across procurement systems (such as SRM), major ERPs (SAP, Oracle), and sourcing platforms, ensuring consistency across the entire landscape.
Through intelligent matching, de-duplication, and data harmonization, organizations gain a supplier master that is accurate, consolidated, and ready for strategic use. The outcome is improved sourcing decisions, stronger contract compliance, and more effective supplier rationalization.
Impact & Value Drivers
While data enrichment may seem trivial as a process, the benefits of “complete” and reliable data are manifold.
Total Clarity on Part Availability – With no missing information in the database, procurement does not need to guess whether the record for any given spare is missing in the system.
Based on the specifications and categories outlined, the procurement process is streamlined and incorrect procurement requests are minimized, directly resulting in controlled overheads
Synchronization of Maintenance Processes – With digital BOMs in sync and data from several sources compiled into one central repository, the requirement and demand for specific types of MRO spares can be clearly forecasted.
This is a pre-requisite for optimizing MRO Inventory levels and prevents instances of downtime occurring due to stockouts completely.
Verdantis’ MRO Data Enrichment Solution
Watch how our AI agents intelligently map, enrich, and manage spare parts data to ensure accuracy across your MRO operations
What People Ask
How is Verdantis different from other MRO data enrichment providers?
Verdantis uses AI-trained models built on over 100 million parts and 1 billion+ data points to deliver unmatched accuracy in enriching and classifying MRO data. Unlike rule-based or manual services, our agentic AI continuously learns from industry patterns, improving over time while minimizing internal effort.
What does MRO data enrichment involve beyond just fixing spelling or formatting errors?
MRO data enrichment goes well beyond simple corrections. It includes completing missing attributes, resolving synonyms, aligning parts to standardized taxonomies, removing duplicates, and linking materials to the correct equipment or BOMs. The result is structured, searchable, and reliable data that is ready for operational use.
How does MRO data enrichment support better procurement and inventory management?
Accurate and enriched data helps eliminate duplicate parts, identify obsolete items, and ensure consistent part descriptions. This enables smarter sourcing decisions, reduces maverick spend, improves vendor negotiations, and helps maintain optimal stock levels across multiple locations.
How does Verdantis handle multilingual MRO data for global operations?
Verdantis supports data cleansing and enrichment across multiple languages. The platform translates, localizes, and maps equivalent terms across regions, ensuring consistency across global plants while reducing part proliferation and miscommunication.
Does Verdantis help establish governance processes post enrichment?
Yes. Beyond completing a one-time enrichment project, Verdantis provides ongoing governance. Its AI-driven platform ensures that new data entries adhere to standards, preventing duplicates and minimizing the entry of obsolete items at the source.
Why is MRO data enrichment critical for asset-intensive industries?
In industries with heavy asset reliance, spare parts data is directly linked to procurement efficiency, maintenance execution, and inventory planning. Enriched data ensures correct part descriptions, eliminates duplicates, and associates spares with the right equipment, helping reduce downtime, avoid overstock, and lower indirect spend.
