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Verdantis MDM Suite
Standardizes, enriches, and consolidates master data across systems
Continuous governance, validation, and lifecycle control of enterprise master data
Specialist Master Data Solutions for Production-Intensive Enterprise Operations & Data Domains
AutoEnrich AI
Autonomous Data Enrichment of Equipment and Spare parts from verified OEMs
SpareSeek AI
Identify Obsolete Spares & Assets with Alternate Recommendations
AutoSpecs
AI-based Normalization & duplicate detection based on Taxonomy
AutoDoc
Converts complex technical documents into structured data tables
AutoTrans
AI Agent for multi-lingual data management through one central system
Patented System for autonomously normalizing, enriching and integrating disparate industrial master data
MRO 360
Criticality
Multi-plant, advanced criticality analysis with AI-driven risk prioritization
Maintenance Demand Forecasting
Proprietary algorithms for forecasting planned & unplanned demand with >95% accuracy
Work Order Planning
AI-based Normalization & duplicate detection based on Taxonomy
Inventory Intelligence
Auto-detect obsolete items, find alternates, and multi-tier classification of parts and assets
Demand Planning & Optimization
Safety stock optimization, vendor lead time intelligence and autonomous procurement recommendations
Guaranteed Savings
Contractually Guaranteed, depending on operational maturity
Patented AI-based Algorithms
Heavylifting by AI Agents, Perfected by Human Experts
Institutionalize Inventory Knowledge
Reinforcement training from multiple-plants for near perfect analysis
Master Data Management
By Data Domains
Material Master Data Supplier Master Data Customer Master Data Equipment Master Data
By Functional Requirement
Manufacturing Master Data Procurement Master Data ERP Master Data Management Supply Chain Master Data
MRO Master Data Management
MRO Master Cleansing MRO Data Enrichment MRO Data Governance MRO Classification MRO Cataloguing Spare Parts Classification
Miscellaneous
MDM in SAP MDM in Retail Item Master Data Master Data Cleansing
Enterprise Asset Management
MRO Inventory Management
Parts Inventory Management System Parts Criticality Asset Criticality MRO Demand Forecasting
By Industry
Oil & Gas Inventory Management Inventory Management in Mining Inventory Management in Utilities
Preventive Maintenance
Asset BOM Management Work Order Software Solutions
Predictive Maintenance
Condition Monitoring Vibration Analysis Digital Twin
Reliability Centered Maintenance
FMECA Mean Time to Failure Root Cause Analysis PM Optimization
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Asset BOM Management MRO Procurement Strategies Spare Parts Management MRO Inventory Management in SAP
Basics of Master Data Management
Alternatives to SAP MDG Material Management in SAP Multi Domain MDM Challenges in MDM Basics of Master Data Governance
Basics of Asset Management
Preventive Maintenance Strategies
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4 Types of Maintenance Strategies: A Complete Guide for Industrial Operations

Explore the four core maintenance strategies — reactive, preventive, predictive, and prescriptive — and learn how to choose the right mix for your industrial operations.
  • Updated On: April 22, 2026

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We offer unparalleled scalability and multi-lingual capabilities,
proven to optimize business processes and drive bottom-line improvements.

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MRO Master Data Management for a leading $12Bn Oil and Gas company

Oil and Gas organizations face huge unwanted costs in the form of losses, penalties and compensation due to industrial accidents, taking a sizable chunk of their revenues. One of the important reasons for failures and accidents is the breakdown of key industrial infrastructure arising from ineffective management of the material base and inefficiencies in the MRO supply chain that are highly critical for the production and maintenance cycles.

More and more organizations are now looking to establish Master Data Management (MDM) implementations in order to manage their assets through informed decision-making based on reliable data and ensure optimal industrial safety across the enterprise.

About the company:

This US based company is a global leader in providing essential mechanical components for both land and offshore drilling rigs, complete drilling and well servicing rigs, as well as a comprehensive range of tubular inspection, internal coatings, drill string equipment, lifting and handling gear, and downhole drilling tools. It also offers supply chain services through distribution centers strategically located near major drilling and production sites worldwide. With over 800 manufacturing, sales, and service centers globally and annual revenues exceeding $12 billion, the company delivers customer-focused solutions tailored to meet the energy industry’s quality, productivity, and environmental needs.

Material Master Data Challenges

The company faced significant challenges with their master data.
• Lack of consistency and standards for the master data
• Incorrect, incomplete, unclassified and outdated attribute fields of master
data High volumes of data in multiple languages
• Multiple ERP and other legacy instances across the company
• Poorly structured material descriptions
• Lack of data governance and collaboration across cross-functional divisions and locations

Solution & Results Achieved

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Solution: Material Master Data Harmonization & Ongoing Maintenance

The company implemented Verdantis Material Harmonize to cleanse and enrich its historical material master data. Using AI-powered tools and the Verdantis Content Repository, the solution classified, standardized, and eliminated duplicate materials.

Once the data was harmonized, Verdantis Material Integrity was deployed for ongoing data governance. It ensured continuous data accuracy through real-time validation, AI-driven classification, and duplication prevention, maintaining high data quality across enterprise systems.

Key Actions Taken:

  • Classified 150,000 materials into 1,210 UNSPSC and 500+ HTS codes
  • Parsed and normalized unstructured material attributes
  • Eliminated duplicates and enriched material descriptions
  • Established real-time data governance and approval workflows
  • Created a centralized repository for cross-functional data access

Results Achieved:

  • Improved Data Accuracy – Eliminated duplicate and inconsistent material records
  • Enhanced Classification – Standardized materials with UNSPSC & HTS codes
    Better Inventory Management – Optimized stock visibility and usage
  • Faster Procurement & Compliance – Enabled seamless material search & regulatory adherence
  • Sustainable Data Governance – Ensured ongoing accuracy with AI-driven maintenance
  • Informed Decision-Making – Centralized data repository for improved operational efficiency

By integrating Material Harmonize & Material Integrity, the company achieved a scalable, AI-powered Master Data Management framework, ensuring long-term data consistency, efficiency, and compliance.