{"id":37572,"date":"2025-09-09T16:32:48","date_gmt":"2025-09-09T11:02:48","guid":{"rendered":"https:\/\/www.verdantis.com\/?p=37572"},"modified":"2026-02-02T13:17:08","modified_gmt":"2026-02-02T07:47:08","slug":"how-to-clean-spare-parts-data","status":"publish","type":"post","link":"https:\/\/www.verdantis.com\/how-to-clean-spare-parts-data\/","title":{"rendered":"How to Cleanup Spare Parts Data"},"content":{"rendered":"\t\t
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An average production or manufacturing setup consumes several thousands of spare parts or consumables to maintain undisrupted and incident-free operations.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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As per a report published by Data Horrizon Research<\/strong><\/a>,<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\tThe 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\t\t\t<\/p>\n\t\t\t\t\t<\/blockquote>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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In the US alone, the spare part spends are substantial, estimated at around $89.5 billion in 2023.<\/p>

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<\/a>].<\/p>

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 \u00a0meticulously<\/a> maintained in an ERP system, generally in a \u201cmaster data\u201d module that can be referred to as a \u201cMaterial Master<\/a>\u201d or an \u201cItem Master<\/a>\u201d<\/p>

As operations scale, additional requests for new spare parts increase, and the data pertaining to these parts can compromise the \u201cdata quality\u201d of these spare parts. Here\u2019s how.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\tNew 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\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\tThe new request could be missing key information points like part specifications, dimensions, category, sub-category, etc\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\tNo clean, central standard has been adopted to maintain the integrity of the spare parts, making data cataloguing even trickier\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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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<\/a> or other ERP modules.<\/p>

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. \u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMethod 1: Using ETL Data Readiness & Migration Tools\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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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.<\/p>

After which, the data is assessed before being transformed and standardized based on the accepted taxonomy and standards.<\/p>

Lastly, the data is then ingested or \u201cloaded\u201d into the source systems<\/p>

Simply put, here\u2019s what an ETL tool does;<\/p>

  1. E – Extract data from multiple sources and compile them together for one master view.<\/li>
  2. 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.<\/li>
  3. 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.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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    Pros:<\/strong><\/p>

    1. This approach is relatively cost-effective since ETL tools aren\u2019t prohibitively expensive.<\/li>
    2. 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.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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      Cons:<\/strong><\/p>

      1. 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\u2019s little control on the techniques used for data validation and transformation.<\/li>
      2. 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.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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        Recommendation:<\/strong><\/p>

        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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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        Examples:<\/strong><\/p>

        A retail company was struggling with significant data inconsistencies across its various branches, which was affecting operational efficiency and decision-making.<\/p>

        To address this challenge, the company implemented an ETL data cleaning solution that standardized and validated data from multiple sources.<\/p>

        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.<\/p>

        According to Forbes<\/a>, dirty data costs businesses up to 12% of total revenue annually.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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        \n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMethod 2: Outsourcing or Offshoring to Specialist teams\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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        Several companies face the issue of managing the spare parts<\/a> data, and many aren\u2019t equipped with the technical resources or bandwidth to implement a \u201cSoftware-based\u201d approach to correct the data pertaining to spare parts material data.<\/p>

        As an alternative, companies facing this issue choose to outsource this data scrubbing to specialist companies that employ a large team of \u201cdata stewards\u201d, \u201canalysts\u201d, or \u201cassociates\u201d to correct these data quality issues.<\/p>

        A typical data management analyst uses a manual or semi-automated approach to;<\/p>

        1. Identify missing information in every data record<\/li>
        2. Standardize the data based on the \u201cdata sheet\u201d definition of the spare part category<\/li>
        3. Extract the key details\/information from the description into the correct \u201cproperties\u201d, \u201cheaders\u201d or \u201ccolumns\u201d<\/li>
        4. 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<\/li>
        5. Enrich missing values, categories, attributes, Manufacturer Part Numbers, Manufacturer Name, etc<\/li>
        6. Run another duplicate check L2. This time, with additional data points, the duplicate check will be much more thorough and \u201ccomplete\u201d<\/li><\/ol>

          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.<\/p>

          The steps outlined above are the basic tasks and deliverables in a spare part data cleanup exercise.<\/p>

          However, larger enterprises with much more complex data management requirements typically have much more comprehensive data cleanup and augmentation needs.<\/p>

          This includes<\/p>

          1. Full-Scale Spare Part Data Enrichments, including supplier name, attributions, and data enrichment<\/li>
          2. Enrichment, cleanup and deduplication of Supplier and equipment Data as well<\/li>
          3. Integrations between these data domains, so cross-referencing spare parts with equipment by leveraging the Equipment BOM<\/li>
          4. Integrations between spare parts and vendors by leveraging supplier catalogues, in-house data, etc.<\/li><\/ol>

            To fuel excellence in inventory management for MRO teams<\/a>, 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.<\/p>

            This is a much more comprehensive and advanced data cleanup solution, and more information can be found on the MRO Data Cleansing here<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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            Pros:<\/strong><\/p>

            1. 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.<\/li>
            2. Custom requirements linked to the extraction, enrichment or standardization of data can be easily accommodated since the approach is quite flexible.<\/li>
            3. It\u2019s 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.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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              Cons: <\/strong><\/p>

              1. Despite the cost savings achieved by leveraging low-cost offshore teams, since the process is manual and requires some \u201ctechnical\u201d understanding to a certain extent, this approach can turn out to be quite expensive in the long run, especially for recurring data cleansing efforts<\/li>
              2. 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<\/li>
              3. 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.<\/li>
              4. 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.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                Recommendation: <\/strong><\/p>

                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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                Example:<\/strong><\/p>

                A global medical technology company, required efficient management of spare parts and service items to support its operations.<\/p>

                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.<\/p>

                The outsourcing initiative led to improved inventory control and service efficiency, enhancing overall operational performance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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                According to a report published by PSC Global<\/a>,<\/strong> 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                \n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMethod 3: Purpose-Built-Software\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                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.<\/p>

                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\u2019t exist OR require numerous rule-based logic that is not practical to set up.<\/p>

                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.<\/p>

                Verdantis MDM Suite<\/a>, can now significantly automate these tasks.<\/p>

                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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                Here is a walkthrough of how our solution automates data cleanup and enrichment<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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                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.<\/a><\/p>

                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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                Pros:<\/strong><\/p>

                1. One of the most accurate ways of cleaning up, enriching and standardizing the MRO spare parts data<\/a> on adopted taxonomies at scale \u2013 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.<\/li>
                2. One of the fastest ways to execute a parts cleanup activity in a very short period<\/li>
                3. 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.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                  Cons: <\/strong><\/p>

                  1. While this approach is far cheaper than deploying offshore teams managed by the outsourcing companies, it\u2019s a more expensive option when compared to ETL\/MDM tools.<\/li>
                  2. 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.<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                    Example:<\/strong><\/p>

                    A global energy company faced challenges with inconsistent and fragmented spare parts data across its operations, leading to procurement inefficiencies and increased downtime.<\/p>

                    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.<\/p>

                    This approach not only improved data accuracy but also streamlined procurement processes and reduced operational costs.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                    Recommendation<\/strong><\/p>

                    In 90% + of the projects at Verdantis, we deploy the MDM suite\u2019s 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.<\/p>

                    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.<\/p>

                    In this, industry-trained AI models clean the parts data in a 5-step data scrubbing and data standardization operation.<\/p>

                    The cleaned data is also tagged with a \u201cconfidence score\u201d, and the records that lacked enough context for a cleanup are then tagged for manual human review. These records can then be \u201cdeleted\u201d or \u201cmerged\u201d<\/p>

                    At Verdantis, our product roadmap is built on Agentic AI foundations and its ability to add significant value in the data cleansing process.<\/p>

                    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.<\/p>

                    This is the most well-suited approach for parts data cleansing projects with a count anywhere between 20K \u2013 2 million data records.<\/p>

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