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.
Addressing these issues requires more than just surface-level fixes.
A robust approach that combines data enrichment with intelligent data cleansing techniques for MRO can uncover and populate missing technical specifications, correct inconsistencies, and standardize formats. This lays the foundation for more reliable maintenance planning and execution.
Types of MRO Enrichment
Enrichment of MRO data can broadly be classified to different non-exclusive buckets depending on the type of data sources, type of data model that is enriched, and the specific data points that are being populated.
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
Maintenance technicians or front-line workers rely heavily on accurate work order data to record every task performed, part replaced, and operation executed. This information forms a vital historical log, detailing which spare parts were used, under what conditions, and for which assets.
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.
This critical information and data are often trapped within unstructured documents and are both time-consuming and resource-intensive to extract, especially with 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 form the backbone of maintenance operations and include equipment, machines, and infrastructure required to carry out production and service delivery.
However, poor visibility into fixed asset data, ranging from incomplete specifications to inconsistent classification, can significantly impair preventive maintenance, asset lifecycle tracking, and cost optimization.
Implementing effective asset master data management strategies is essential to ensure accuracy, consistency, and operational reliability.
Enrichment from Public Sources
Just as with spare parts, Verdantis’ purpose-built enrichment agents leverage public repositories, OEM databases, and engineering catalogues to populate missing information around fixed assets.
This includes key attributes such as model numbers, manufacturer details, asset classification codes (e.g., UNSPSC, ECLASS), power rating, physical dimensions, and more.
By identifying standard descriptors and mapping incomplete asset data records with verified public sources, the AI agent ensures that each asset profile is complete and consistent, ready to be used in reliability modeling, maintenance scheduling, and cost attribution frameworks.
Enrichment from First Party Sources
Much of the critical asset-level data is already embedded within enterprise systems, which is spread across:
Verdantis’ AutoDoc AI agent is designed to extract structured asset information trapped within technical PDFs, scanned documents, and unstructured inputs.
Using advanced NLP and semantic modeling, it identifies and extracts relevant fields such as asset type, operational limits, installation details, and usage history.
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 well-maintained supplier master ensures compliance, reduces procurement risk, and enables better sourcing decisions.
However, duplicate supplier records, missing contact information, and inconsistent naming conventions are common challenges across MRO-intensive enterprises.
Enrichment from Public Sources
Verdantis’ AI agents are trained to identify, match, and verify supplier records using public business registries, supplier websites, compliance databases, and product catalogues. These agents autonomously pull critical supplier details such as:
Legal entity name
Supplier type (OEM, distributor, service provider, etc.)
Country of registration
Contact information (email, phone, address)
Quality certifications and industry affiliations
In addition, they can identify whether a supplier is still active, which helps in flagging obsolete or duplicate vendors.
Enrichment from First Party Sources
Enterprises already store relevant supplier data in procurement records, POs, invoices, and contracts, but often in siloed or unstructured formats.
Our AI agent extracts key supplier information from these sources and populate it within the Supplier Master. This includes PO frequency, spend category, payment terms, supplier performance scores, etc.
Contain references to vendor terms, part codes, and delivery history.
Indicate real-world fulfillment, discrepancies, and payment terms.
Include negotiated terms, compliance requirements, and SLAs.
Offer data on vendor performance, availability, and pricing.
AI agents analyze and structure this data from across systems and formats to create a complete, unified supplier master. This can include mapping alternative vendor records to a single global vendor ID and enriching vendor-product relationships.
Verdantis also supports full synchronization of supplier data across procurement platforms (like SRM), ERP systems (like SAP, Oracle), and sourcing portals.
Through AI-led data matching and de-duplication, organizations can achieve a clean, accurate, and consolidated supplier master. This directly supports better sourcing decisions, contract compliance, and supplier rationalization efforts.
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?
Enrichment goes far beyond surface-level corrections. It includes auto-completion of missing attributes, synonym resolution, taxonomy alignment, deduplication, and linking parts to equipment or BOMs. It ensures materials data is structured, searchable, and business-ready.
How does MRO data enrichment support better procurement and inventory management?
Clean, enriched data eliminates duplicate parts, flags obsolete items, and ensures accurate part descriptions. This leads to smarter sourcing decisions, reduced maverick spend, better vendor negotiations, and optimized stock levels across locations.
How does Verdantis handle multilingual MRO data for global operations?
Our platform supports multilingual data cleansing and enrichment, including translation, localization, and mapping of equivalent terms across languages. This ensures consistency across global plants, reducing part proliferation and miscommunication.
Does Verdantis help establish governance processes post enrichment?
Yes. Beyond the one-time enrichment project, Verdantis supports ongoing governance through its AI-led platform, ensuring that new entries conform to standards and duplicates or obsolete items are prevented at the source.
Why is MRO data enrichment critical for asset-intensive industries?
In asset-heavy environments, spare parts data directly influences procurement efficiency, maintenance execution, and inventory planning. Enriching this data ensures accurate part descriptions, eliminates duplicates, and links spares to the right equipment, reducing downtime, overstock, and indirect spend.
