An average production or manufacturing setup consumes several thousands of spare parts or consumables to maintain undisrupted and incident-free operations.
As per a report published by Data Horrizon Research,
The global spare parts manufacturing market was valued at approximately USD 620 billion in 2023 and is projected to reach around USD 930 billion by 2033, growing at a CAGR of 4.3% from 2024 to 2033
In the US alone, the spare part spends are substantial, estimated at around $89.5 billion in 2023.
However, actual usage is low; organizations typically use just 8 – 10% of their MRO inventory each year, meaning 90% remains unused on shelves. [As Per Terrence Ohanlon at Reliability AI].
To ensure timely procurement, availability, negotiating power, an error-free sourcing process, and to prevent excess stocking, the data and information pertaining to these spare parts are generally meticulously and maintained in an ERP system, generally in a “master data” module that can be referred to as a “Maestro de materiales” or an “Item Master"
As operations scale, additional requests for new spare parts increase, and the data pertaining to these parts can compromise the “data quality” of these spare parts. Here’s how.
New part requests can be duplicates of a part already in the system, which was not known at the time of creating a new part request
The new request could be missing key information points like part specifications, dimensions, category, sub-category, etc
No clean, central standard has been adopted to maintain the integrity of the spare parts, making data cataloguing even trickier
To mitigate this, companies need to invest in a one-time cleanup of their current spare parts data to weed out the duplicates, fill in missing information, structure the dataset, and, in advanced cases, also integrate the data and reference it with other master data domains or other ERP modules.
Companies currently engaged in this process can approach this in a few different ways, each approach having its own varying costs, complexity and requirement of technical bandwidth.
Method 1: Using ETL Data Readiness & Migration Tools
ETL and data readiness/migration tools are designed to collate data from multiple sources by extracting them from various sources like files, folders, software, supplier feeds/catalogues, etc.
After which, the data is assessed before being transformed and standardized based on the accepted taxonomy and standards.
Lastly, the data is then ingested or “loaded” into the source systems
Simply put, here’s what an ETL tool does;
- E – Extract data from multiple sources and compile them together for one master view.
- T – Transform the data into a single standard, with abbreviations, Unit of Measure, Categories etc., written as per accepted taxonomy. This is the stage at which the data is cleaned thoroughly, checked for duplicates, enriched for missing values and merged wherever applicable and validated for rules.
- L – Loading the data back into the source systems after treating it as per original requirements, ensuring it is accurate, standardized, and ready for consistent use across operations.
Pros:
- This approach is relatively cost-effective since ETL tools aren’t prohibitively expensive.
- This approach is scalable, and if the required technical and managerial resources are made available, the cleanup can be done much faster across several thousands (or even millions) of spare part records, assuming the business rules are continuously updated.
Cons:
- The accuracy and use-case coverage are not ideal; in most cases, applying bulk business logic tends to do more harm than good, as there’s little control on the techniques used for data validation and transformation.
- It requires both technical as well as business resources (from teams like procurement, data management), which are often not easily available, and roping in ad-hoc resources can escalate the costs, defeating the most important advantage of this approach in the first place.
Recommendation:
This approach is ideal when the count of spare parts required to be cleaned is closer is much smaller in size (within 3-5K records), the threshold for data accuracy is not too high and when technical resources are made available.
Ejemplos:
A retail company was struggling with significant data inconsistencies across its various branches, which was affecting operational efficiency and decision-making.
To address this challenge, the company implemented an ETL data cleaning solution that standardized and validated data from multiple sources.
As a result, the company achieved a 20% increase in operational efficiency and significantly reduced data-related errors, enabling smoother operations and more reliable reporting.
According to Forbes, dirty data costs businesses up to 12% of total revenue annually.
Method 2: Outsourcing or Offshoring to Specialist teams
Several companies face the issue of spare parts data management, and many aren’t equipped with the technical resources or bandwidth to implement a “Software-based” approach to correct the data pertaining to spare parts material data.
As an alternative, companies facing this issue choose to outsource this data scrubbing to specialist companies that employ a large team of “data stewards”, “analysts”, or “associates” to correct these data quality issues.
A typical data management analyst uses a manual or semi-automated approach to;
- Identify missing information in every data record
- Standardize the data based on the “data sheet” definition of the spare part category
- Extract the key details/information from the description into the correct “properties”, “headers” or “columns”
- Perform an L1 duplicate check on the entire dataset to weed out duplicate entries. Even at this point, the chances of duplicate entries being in the system are high
- Enrich missing values, categories, attributes, Manufacturer Part Numbers, Manufacturer Name, etc
- Run another duplicate check L2. This time, with additional data points, the duplicate check will be much more thorough and “complete”
The process, as one would assume, is laborious, and these outsourcing companies typically employ a large chunk of their workforce in a country with a large English-speaking population who are adept at computer skills to keep the cost low.
The steps outlined above are the basic tasks and deliverables in a spare part data cleanup exercise.
However, larger enterprises with much more complex data management requirements typically have much more comprehensive data cleanup and augmentation needs.
This includes
- Full-Scale Spare Part Data Enrichments, including supplier name, attributions, and data enrichment
- Enrichment, cleanup and deduplication of Supplier and equipment Data as well
- Integrations between these data domains, so cross-referencing spare parts with equipment by leveraging the Equipment BOM
- Integrations between spare parts and vendors by leveraging supplier catalogues, in-house data, etc.
To fuel excellence in inventory management for MRO teams, these outsourcing teams also augment not only the spare parts data (also referred to as Material spares) but also every other piece of data linked to maintenance processes, and this is typically referred to as MRO Master Data Cleansing.
This is a much more comprehensive and advanced data cleanup solution, and more information can be found on the MRO Data Cleansing aquí.
To fuel excellence in inventory management for MRO teams, these outsourcing teams also augment not only the spare parts data (also referred to as Material spares) but also every other piece of data linked to maintenance processes, and this is typically referred to as MRO Master Data Cleansing.
This is a much more comprehensive and advanced data cleanup solution, and more information can be found on the MRO Data Cleansing aquí.
Pros:
- For enterprises with high data accuracy thresholds, this is a clean approach with the highest accuracy across metrics linked to data standardization, data enrichment, extraction and de-duplication.
- Custom requirements linked to the extraction, enrichment or standardization of data can be easily accommodated since the approach is quite flexible.
- It’s an ideal choice for cleaning spare part master data records that are >20K records, since there are fixed costs associated with finding, onboarding and managing the outsourced vendor.
Cons:
- Despite the cost savings achieved by leveraging low-cost offshore teams, since the process is manual and requires some “technical” understanding to a certain extent, this approach can turn out to be quite expensive in the long run, especially for recurring data cleansing efforts
- Depending on the count of spare parts and records, this approach can take the longest to turnaround and projects with over 50k part records can easily take over 2 months to deliver
- Although accuracy scores are generally much higher when compared to previous approaches, it really depends on the quality of the offshore team and regular coordination, progress updates and reviews are required to ensure quality control is in check.
- For less than 20K spare part material records, this is not an ideal approach due to the fixed costs associated with finding, onboarding, contracting and managing the offshore partner.
Recommendation:
This is an ideal approach for companies with high data quality thresholds, budgets, a high count of spare part records and relatively longer timelines for implementation.
Por ejemplo:
A global medical technology company, required efficient management of spare parts and service items to support its operations.
The company outsourced the management of spares and service items, including the creation of 200 spares kits and sub-assemblies, to streamline their logistics and inventory processes.
The outsourcing initiative led to improved inventory control and service efficiency, enhancing overall operational performance.
According to a report published by PSC Global, outsourcing data management activities led to a reduction in lead times from 12 weeks to just 1 day, reduced working capital by centralizing stock, minimized spare requests, and improved item availability.
Method 3: Purpose-Built-Software
In addition to the ETL/Data Readiness software, several purpose-built, specialist tools claim to completely automate the process of cleaning spare part data with built-in validation rules, duplicate identification.
Before the advent of AI models, one could argue that it was pretty much impossible to automate this cleanup since clean and clear rules for cleaning or standardizing the spare part data simply don’t exist OR require numerous rule-based logic that is not practical to set up.
Since 2025, however, the application of AI agents and their ability to be context-aware by getting trained on the right data have opened new doors.
Verdantis MDM Suite, can now significantly automate these tasks. The investment in developing AI agents for standardizing part descriptions, autonomously creating data sheets based on taxonomies and enriching data from verified sources has resulted in spare parts data cleanup being much faster, productive and more affordable.
Here is a walkthrough of how our solution automates data cleanup and enrichment
Moreover, the same AI models can be used to ensure the part data quality remains intact on a going basis; this is typically referred to as governance of MRO Data.
While there have been several advancements in AI and our team at Verdantis has shipped several AI models embedded into various spare part data cleaning workflows. In 90%+ cases, these data cleansing workflows cannot be fully automated and require humans in a reviewing capacity.
Pros:
- One of the most accurate ways of cleaning up, enriching and standardizing spare part data on adopted taxonomies at scale – especially if the software leverages AI models trained on industry-specific parts data. In fact, since this approach leverages trained AI models, the accuracy can be better than Method #2 detailed above.
- One of the fastest ways to execute a parts cleanup activity in a very short period
- The idea of this approach is to train AI agents on a large horde of standardized data and build processes that enable AI agents to do the heavy lifting while maximizing accuracy. Human reviews can be tightened to achieve the desired accuracy level by making tactical use of AI-based confidence scores etc.
Cons:
- While this approach is far cheaper than deploying offshore teams managed by the outsourcing companies, it’s a more expensive option when compared to ETL/MDM tools.
- Many of these software platforms that are powered by AI agents are novel and require thorough onboarding and training to be used, which can be a bit overwhelming and a comfort zone for enterprises and their procurement teams. On the contrary, ETL & MDM tools are familiar territory.
Por ejemplo:
A global energy company faced challenges with inconsistent and fragmented spare parts data across its operations, leading to procurement inefficiencies and increased downtime.
By implementing a purpose-built AI-enabled Master Data Management (MDM) solution, the company automated the standardization, enrichment, and validation of over 50K+ spare parts records.
This approach not only improved data accuracy but also streamlined procurement processes and reduced operational costs.
Recommendation
In 90% + of the projects at Verdantis, we deploy the MDM suite’s spare parts data cleansing module along with a team of 2-3 data analysts and a project manager for the final review, edits and approvals and QC checks.
We find that this hybrid approach of leveraging powerful, industry-trained AI agents along with expert subject matter experts is an ideal way to minimize turnaround time and maximize accuracy.
In this, industry-trained AI models clean the parts data in a 5-step data scrubbing and data standardization operation.
The cleaned data is also tagged with a “confidence score”, and the records that lacked enough context for a cleanup are then tagged for manual human review. These records can then be “deleted” or “merged”
At Verdantis, our product roadmap is built on Agentic AI foundations and its ability to add significant value in the data cleansing process.
With that said, while a fully automated solution is right around the corner, as it stands, a parts data cleansing process requires humans in the loop that can review, approve and override some of the data records.
This is the most well-suited approach for parts data cleansing projects with a count anywhere between 20K – 2 million data records.
Software in Action:
Method 4: Generic MDM Software
While generic MDM platforms aren’t purpose-built for spare parts scrubbing, they are flexible and configurable enough to handle industry-specific use cases for cleansing, but this also requires a specialist in data stewardship or data management to configure and implement these rules and migrate the corrected data into the source systems.
Here’s how these platforms can be used for cleansing material and spare data
1. Data Collection
Similar to ETL tools, these MDM tools identify the sources of data from multiple source systems like ERP/EAM, CMMS, Folders, catalogues, legacy systems or manual records.
The MDM platforms use connectors or integration tools to pull the data from these disparate systems, and the goal is to consolidate all this data into the MDM platform for centralized data management.
2. Data Standardization
MDM platforms typically have a very intuitive interface for data standardization through “rule configuration”, classification schemas for spare parts, naming conventions, units of measurement, etc.
For Example, a rule for standardizing “Kilograms” to “Kg”, or a rule that disallows special characters
Or another rule to validate or standardize numerical values (eg: dimensions, cost)
Or another rule for naming conventions, for example, standardizing part descriptions to avoid different variations (e.g., “Steel Bolt” vs. “Bolt, Steel”).
3. Duplicate Detection
MDM Platforms typically have various capabilities for duplicate detection. The platform uses algorithms to find exact matches across part numbers, descriptions, categories, Units of Measure, etc.
For advanced de-duplication, the MDM systems support fuzzy matching, which can identify records that are similar but not identical at a character level. (e.g., “12mm Bolt” vs. “12mm-Bolt”).
These records are generally tagged with a confidence score, because allowing merging of the records automatically can be risky and lead to errors.
4. Data Cleansing & Enrichment
This largely entails automatically or manually correcting inaccurate data fields like part numbers, descriptions, manufacturers, or classifications. For example, if a part description is incomplete or misspelled, it can be updated to match the correct information.
Where data is missing (e.g., critical specifications or part numbers), the MDM platform can use predefined rules to either pull data from external sources (like supplier catalogs) or flag these gaps for manual entry.
Most MDM tools have built-in integrations with external data sources for enrichment. Digital copies of supplier catalogues and in-house data within ERP/EAM systems or third-party databases can be used to build out enrichment workflows and to plug the gaps.
5. Data Governance
As discussed in point #1, central management of parts data is the goal of any MDM initiative for material spare parts. The key aspect here is to ensure reliable data on a going basis.
A cleanup exercise on spare parts master data will only remain effective for a few weeks before the data quality erodes again. This is why a data governance plan for MRO should be the immediate next step to ensure high-quality spare parts data on a going basis.
Some of the leading master data management software vendors are listed on this page along with their USPs, industry use-cases, integration options and user reviews.
Pros:
- The approach is flexible and can be configured to meet the specific requirements of an organization’s spare parts data, making it suitable for a variety of industries and data complexities.
- It enables centralized management of spare parts data by consolidating information from multiple sources like ERP, EAM, CMMS, or manual records into a single platform for easier control and access.
- With rule-based standardization and validation, the platform helps clean data by automating common tasks like converting units of measurement, applying naming conventions, and correcting errors based on predefined logic.
- Duplicate detection is supported through algorithms that find exact matches and similar records, helping reduce redundancy and ensuring data accuracy while providing confidence scores for safer merges.
Cons:
Setting up the platform requires data stewardship or IT expertise to configure rules, workflows, and integrations, which can make initial deployment resource-intensive.
Since generic MDM software is not purpose-built for spare parts data, organizations often need to invest additional time and effort in customizing workflows and logic to fit industry-specific needs.
Without structured governance and regular reviews, cleaned data can quickly degrade, requiring ongoing effort to maintain quality and reliability.
Por ejemplo:
A global manufacturing company dealing with spare parts data inconsistencies across regional ERPs implemented a generic MDM platform.
They standardized naming conventions, units of measure, part classifications, and ran duplicate detection via fuzzy matching.
As a result, they reduced data errors, improved procurement accuracy, and enhanced maintenance planning, while establishing ongoing data governance to sustain these quality improvements.
As per a research report by IJSRP, companies that implemented an MDM solution to harmonize their data reported up to a 25% reduction in data errors and a 15% improvement in production efficiency.
Conclusión
Cleaning spare parts and consumables data is a critical step in enabling operational efficiency, reducing costs, and improving decision-making for organizations in production-heavy industries.
While there isn’t a one-size-fits-all solution, each approach outlined here offers its own strengths depending on the scale of the data, the required accuracy, available resources, and the specific business context.
Ultimately, the right approach depends on the unique needs of the organization. Whichever method you choose, investing in a structured cleanup process and ongoing governance will ensure your spare parts data remains clean, reliable, and ready to support strategic operations.
Clean data is not just about maintaining records; it’s about enabling smarter procurement, better maintenance planning, and stronger inventory control across your entire operation.
