The processes in Maintenance Repairs and Operations [MRO], especially at large manufacturing or production-intensive organizations requires managing, generating and treating and synchronizing quite a lot of data across several organizational systems.
This data itself can pertain to spare parts, consumables, fixed assets, inventories, suppliers, work orders, equipment BOMs etc.
These data sets are typically managed in ERP systems and due to their sheer volume, scale of usage and lack of data management protocols, over-time, their data quality erodes.
Data quality issues with spare parts are generally the most common, followed by MRO suppliers and equipment.
Key MRO Data Quality Issues & Their Consequences
Duplicate Data
Due to poor data stewardship and a near-absence of data governance standards for spare parts, over time, multiple records are created for what is essentially the same part, vendor or equipment.
The challenge, however, is that Master data systems are built on a “Golden Record” standard, meaning that every record created references to a unique product, part, equipment or supplier.
Duplication of records results in the MRO inventory management system to believe that the two spares are distinct, leading to multiple procurement orders, overstocking of spare parts and ultimately, higher procurement and inventory costs.
Based on research that we carried out at Verdantis on anonymized company data found that procurement costs can be inflated by anywhere between 20% - 35% in MRO datasets that are riddled with duplicates
Data records that are updated in haste to procure an item without updating the right information, attributes, specifications or details are a fairly common problem.
This is a severe data quality issue and is also one of the reasons that exacerbates issue #1 highlighted above.
For instance, Missing information specific to a spare part of a vendor can lead to misinformed decisions, leading to wastage, incorrect procurements, and in case of critical maintenance operations, this can directly result in Production downtime.
Absence of key information like manufacturer name or part number makes it more difficult to ascertain if the given item is “Active” or “Obsolete”
Based on another internal research at Verdantis, instances linked to production downtime and maverick spending can be reduced by as much as 30% by ensuring clean and up to date MRO records.
Smooth operations of an MRO process depends heavily on data synchronized between different data domains and even between master datasets and other digital modules in the ERP.
This data is synchronized through “linkages” which are basically unique IDs that link a data record across different datasets.
Linkages from the Digital BOM to Spare Parts establishes references from spare part data records to fixed assets
Spare Parts to the inventory management system establishes linkages with the Inventory management system
Data from the spare parts criticality assessment should also be synchronized in the MRO master to ascertain spare parts that are critical vs non-critical ones
Although out of the purview of most MRO data management solutions, best practices also dictate that the very nature of spare parts should be classified into slow moving, fast moving, obsolete etc
This synchronization of data then paves the way for software-based MRO inventory management solutions to have all the right data to optimized inventory levels seamlessly.
MRO Data Management is critical for ensuring equipment uptime and operational efficiency. Clean, consistent data helps maintenance teams find the right parts faster, enables accurate inventory planning, and reduces procurement delays – ultimately cutting costs and avoiding downtime.
Rely on accurate part descriptions and specifications to minimize equipment downtime and streamline repairs.
Depend on clean, classified MRO data to avoid duplicate purchases, reduce supplier risk, and optimize sourcing decisions.
Use trustworthy MRO data to improve asset reliability, plan preventive maintenance, and reduce operational disruptions.
Need standardized data to manage stock levels effectively, avoid overstocking, and eliminate dead inventory.
Ensure system-wide consistency by integrating MRO data across ERP, EAM, and CMMS platforms.
Leverage high-quality MRO data to enable AI, analytics, and predictive maintenance initiatives.
How to Solve for these Data Quality Issues?
The solution to this ongoing challenge can be bifurcated into 2 parts;
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Normalization of Existing Data
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Creation of Future Entries/Records
This entails a total standardization of past legacy data that has accumulated over the years and scrubbing it thoroughly and entails weeding out the duplicate entries, enriching the required information, extracting attributes from the data records and synchronizing information with other data sources.
Beyond standard Depuración de datos MRO, software solutions can also establish links between different domains and establish a cross-reference between the two.
Up until now, companies were largely reliant on large teams that manually cleaned these data records, while the accuracy of such processes were arguably manageable, the turn-around time for executing such a cleansing exercise for master data records was quite high.
In recent times, however, with the rise of purpose-built AI models, MRO data cleansing today are far more accurate and can be done in a fraction of the time with AI agents that are trained on millions of vendors, spare parts or equipment records.
The video below showcases how Harmonize, a software solution that deploys such AI agents, can normalize legacy data with >95% accuracy.
El siguiente vídeo detalla el proceso de enriquecimiento de datos de nuestra solución de armonización de datos:
While cleansing of legacy data protects the sanity of MRO data pertaining to the past, there is very little that prevents these data quality issues from surfacing again.
So, enterprises typically resort to a software-based solution that integrates with you’re the ERP systems and the MRO master to prevent duplicates from being created in the first place using a combination of AI models and powerful fuzzy match systems.
Some also refer to this as an MRO data governance solution.
En el siguiente vídeo se detallan las principales características de nuestra solución de gestión de datos:
Beyond Master Data
So far, we have broadly covered what are considered standard offerings in MRO data management.
However, enterprises looking to truly build a competitive advantage with their MRO data management strategies augment the quality of the data with novel approaches as well as by adopting new techniques for this approach.
Advanced MRO data management solutions, today, can scour through your entire existing MRO spare parts data and can ascertain the “status” of any given part, that is, it can flag a spare part as “Obsolete” or “Active” – enabling more effective obsolesence data management for spare parts.
This is now possible with the use of AI agents that can autonomously develop a contextual understanding by accessing supplier catalogue, websites and information from open and proprietary sources.
Agentic AI solutions can also scour through the parts data and identify alternate spare parts and suppliers for those spare parts.
This management of spare parts is critical for building excellence in MRO management, optimizing and analysing MRO spends and plays into an overall MRO sourcing strategy.
Once the Manufacturer Name or Part Number for spare parts are ascertained, best practices dictate that this data can be referenced back to the vendor master, thus building a holistic multi-domain MDM system
Along with assessment of critical MRO equipment and parts, nature of the spare parts and the linkages between them, a reliable MRO dataset can augment the capabilities of an MRO inventory management software
Agentic AI tools can significantly simplify and strengthen the process of Bills of Materials data management by extracting data from digital Bills of Materials (BOMs). These are often found in formats like engineering PDFs, Excel files, ERP exports, or CAD drawings.
These documents are difficult to process at scale due to their unstructured nature, and manual handling often introduces errors, delays, and inconsistencies.
AI-powered extraction tools can read these digital BOMs and automatically pull key information such as part numbers, equipment IDs, part descriptions, and quantities.
Once extracted, this structured data is matched against the spare parts master and equipment master records to establish linkages between parts and the specific machines or assets they belong to.
This automated mapping eliminates ambiguity by clearly defining which parts are used in which equipment, their locations in the asset structure, and how many units are required.
It helps prevent the creation of duplicate part records, ensures consistency across data sources, and supports the creation of accurate, complete maintenance BOMs.
Moreover, this extraction process feeds directly into improving inventory accuracy, optimizing spare part stocking strategies, and reducing instances of over- or under-ordering.
It also enables the classification of spares by their usage context within the BOM – whether as fast-moving, critical, or low-use parts – which is key for prioritizing procurement and maintenance planning.
This supports data harmonization, builds integrity across the asset and material masters, and creates the groundwork for more advanced automation and analytics. It’s a critical step toward enabling proactive, data-driven MRO operations.
In the past, enterprises largely relied on an army of humans who manually corrected the compromised data in MRO datasets, as mentioned earlier, the turnaround times, cost and accuracy in such an approach was sub-optimal.
MRO Data Management software, like the one we have developed at Verdantis, leverages industry-trained AI models to automate most of the data normalization tasks that are then looped back to a human-reviewer for quality-control.
Here are a few industries that we specialize in
Types of MRO Data Management Services
The goal of this service entails building “completeness” in MRO data records by fetching information from third party sources, either manually or programmatically and updating the information back to the MRO data set.
At Verdantis, we primarily rely on industry-trained Agentic AI models that autonomously enriches data records across spars, fixed assets and suppliers
- For MRO spare parts, this can be data points like manufacturer name, manufacturer part number, part attributes, specs, Units of Measure, equipments where they are used
- For equipment or fixed assets, this can be Equipment manufacturer, equipment ID, plant where it is used, equipment category
- For suppliers (or vendors), this can be manufacturer/distributor name, HQ address, Tax Identification Number and similar details
This primarily entails cataloguing of spare parts, although for large-scale operations, cataloguing of fixed assets and vendors is also common.
Every organization adopts a specific taxonomy for classification of MRO data and fixed assets, some of the popular ones are UNSPC, NAICS, PIDEX.
Based on the standards prescribed earlier, data records are structured and standardized by processing huge volumes of raw data, extracting key attributes, enriching missing values and de-duplicating the database for potential L1 & L2 duplicates.
In some cases, companies need to create data records in bulk either from supplier catalogues, or internal MRO Bill of Materials.
Using solutions like Auto-Doc AI, this can be consolidated and created in bulk with a few clicks.
Verdantis as a Specialist in MRO Data Management
Verdantis has been a specialist in MRO Master Data Management for companies with asset-intensive production operations and heavy maintenance operations.
Our focus on Agentic AI models for the full suite of MRO data services have helped us solve crippling data quality concerns for mid-market and enterprise companies globally.
