MRO Master Data Management for a Multinational Company: Delivered 98%+ Accuracy Across 500,000+ Records, 45% Faster Procurement, 35% Faster Spare Parts Identification

98%+

data accuracy

45%

reduction in procurement cycles

35%

faster spare parts identification

Service

  • MRO Data Management
  • Data Enrichment
  • Web Research
  • Product Data Classification

Platform

  • MS Excel
  • MS SQL

The Client

A Multinational Engineering and Technology Enterprise

Our client operates as a multinational industrial enterprise spanning power and gas, aerospace and defense, automotive, shipping, consumer goods, and healthcare sectors. They manage vast inventories of MRO (Maintenance, Repair, and Operations) spare parts essential for maintenance across global operations, aiming to automate processes from identification to procurement.

Project Requirements

MRO Master Data Management to Enable Faster Procurement and Accurate Part Identification

The client wanted to build an accurate, reliable, and procurement-ready MRO master dataset for seamless ERP integration, faster part identification, and improved sourcing decisions. The project scope included:

  • Product Data Cleansing & Standardization: Processing MRO data coming from multiple sources—supplier feeds, legacy ERP systems, and acquisition databases (approximately 500,000 product records monthly) to extract and standardize critical attributes, including manufacturer part numbers (MPNs), original equipment manufacturer (OEM) names, technical specifications, and supplier names.
  • Web Research & Data Enrichment: Collecting missing information such as manufacturer details, UNSPSC codes, article number, expiration statuses, etc., from manufacturers' or suppliers’ websites and enrich incomplete product records (approx. 1,50,000 records monthly).
  • Product Categorization & Taxonomy Mapping: Implementing standardized taxonomy across the entire catalog by mapping products to industry-standard classification systems such as UNSPSC. This included categorizing 80,000+ uncategorized products and re-categorizing 70,000+ products with incorrect or outdated classifications.
  • Supplier & Manufacturer Data Validation: Researching, validating, and maintaining detailed records for 10,000+ suppliers and manufacturers, including legal entity names, contact information, addresses, authorized distributor status, manufacturer websites, and parent company details. This master supplier database would serve as the foundation for procurement decisions and vendor management.

Project Challenges

Managing Inconsistent and Poor-Quality MRO Data Across Suppliers, Regions, and ERP Systems

The client’s MRO data has several quality issues that made large-scale master data management complicated and time-consuming. Some of the critical issues were:

Massive Data Duplication

Massive Data Duplication

Analysis revealed that approximately 40% of the 85,000 records were duplicates or near-duplicates, created when different teams cataloged the same items. Identifying true duplicates proved complex because variations existed in part-number formats, manufacturer-name spellings, unit-of-measure designations, and descriptive terminology.

Multi-Source Data Complexity

Multi-Source Data Complexity

The consolidated data originated from different ERP systems (SAP, Oracle, legacy custom systems), each with unique field structures, validation rules, and data quality standards. Reconciling these disparate formats while preserving data integrity required sophisticated mapping and transformation logic.

Stringent Accuracy

Stringent Accuracy & Quality Benchmarks

Given the high cost of downtime in mining and heavy manufacturing, the client mandated a data accuracy rate of >97%. A single incorrect attribute (e.g., confusing a high-pressure valve for a low-pressure one) could lead to catastrophic equipment failure or safety hazards. We had to implement a multi-tiered validation process to ensure "Golden Record" quality while processing over 25,000 SKUs per week to meet the deadlines.

Multi-Lingual Data Interpretation

Multi-Lingual Data Interpretation

The client data came from multiple international suppliers and distributors in three languages: English, German, and Spanish, often mixing local slang with technical terminology. To ensure accurate translations, we had to leverage reliable translation tools and subject matter experts (having both language proficiency and technical knowledge of industrial components).

Our Solution

Structured Multi-Phase Approach to Streamline MRO Master Data Management

To address these multifaceted challenges, we deployed a dedicated team of 30 MRO data specialists and provided them with initial training on the client’s product line and guidelines. Our large-scale MRO data management approach involved:

MRO Data Cleansing and Standardization

We implemented systematic data cleansing workflows to transform fragmented raw data into a unified, accurate master database:

  • Automated Data Scraping: Developed custom Excel macros and Python scripts to parse unstructured product descriptions, extracting key attributes (MPNs, manufacturer names, product types, specifications) from unstructured data. This reduced manual parsing time by 60% while maintaining accuracy.
  • Manufacturer Data Standardization: Created a master manufacturer reference database with 2,000+ verified manufacturer names, brand variations, legal entities, and aliases. Implemented fuzzy matching algorithms to standardize manufacturer names across multiple spelling variations and abbreviations. For example, variations like VolksWagen, VolksWagen, and VW were standardized to the uniform term “VolksWagen”.
  • Data Deduplication: Developed multi-criteria matching logic based on manufacturer name + MPN + product type combinations to identify and resolve over 85,000 duplicate records.

MRO Data Research and Enrichment

We established comprehensive research protocols to enrich product records with accurate, detailed technical information:

  • Technical Specification Extraction: Systematically extracted and validated detailed technical specifications across various attributes such as electrical characteristics (voltage, current, frequency), physical dimensions, environmental ratings (IP ratings, temperature ranges), certifications (UL, CE, CSA, RoHS), and material composition from manufacturer and supplier websites, datasheets, and other credible sources.
  • Rich Content Development: Created comprehensive product descriptions (150-300 words) highlighting key features, typical applications, benefits, and technical differentiators. Sourced and validated high-resolution product images, technical diagrams, dimensional drawings, and installation guides.
  • Multi-Source Data Verification: Implemented cross-validation protocols to check data against at least two authoritative sources, ensuring complete accuracy.

Taxonomy Development and MRO Data Classification

We developed a 5-level category taxonomy aligned with the UNSPSC classification framework, spanning Division > Group > Class > Subclass > Product Type, to ensure intuitive navigation and search.

  • Product Data Classification: Developed custom classification rules tailored to the client's industrial equipment. For example, hydraulic components were categorized not just by function but also by pressure ratings, connection types, and application areas (mobile equipment vs. stationary systems), enabling procurement teams to quickly locate precisely the items they needed.
  • Cross-Reference Mapping: Mapped the client's existing internal category codes to the standardized UNSPSC framework, creating translation tables that preserved institutional knowledge while enabling industry-standard reporting and benchmarking.
  • Category-Specific Attribute Development: Defined category-specific attribute sets, ensuring each product data includes relevant technical details.

Supplier Data Validation and Enhancement

To validate, enrich, and manage supplier data for 10,000+ vendors, we created a streamlined process that involved:

  • Data Verification: Verified legal entity names, tax identification numbers, registered addresses, and contact information against official business registries and databases (D&B, company registries). If the critical details around aspects like legal names, websites, parent company details, synonymous names, and company logos or URLs are missing, we appended them to ensure a comprehensive database.
  • Compliance Documentation: Confirmed current status of certifications, licenses, and compliance documents (ISO certifications, safety standards, industry-specific qualifications).
  • Data Quality Assessment: Reviewed each supplier record for completeness and usability, marking entries as "Valid" or "Unusable" based on data accuracy and business requirements. Invalid entries were flagged with specific reasons (duplicate, incorrect information, insufficient data) to support data cleanup decisions.
  • Risk Assessment Enhancement: Appended risk indicators, including financial stability ratings, geographic risk factors, supply chain concentration metrics, and past performance scorecards.
  • Supplier Classification: Categorized vendors by strategic importance, spend levels, and capability assessments to support differentiated supplier relationship management strategies.

Quality Assurance and Governance

We established rigorous quality control processes to achieve data accuracy of over 97%. The approach involved:

  • Four-stage data verification: Initial MRO data processing, peer review, technical expert validation, and final client approval.
  • Statistical sampling: Regular quality audits examining random samples to ensure sustained accuracy levels above 97%.

Project Outcomes

Leveraging our domain knowledge, scalable workflows, and human-in-the-loop approach, we not only successfully handled MRO data processing for over 500,000 records monthly but also helped the client achieve transformative results, such as:

98%+ data accuracy across cleansed and enriched MRO records

40% reduction in duplicate and obsolete spare parts records

35% faster spare part identification for maintenance teams

45% reduction in average procurement cycles with accurate and non-conflicting data

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