Learn how poor asset and BOM data is slowing down your maintenance and how AI-powered solutions can fix it for good.
Is your maintenance team constantly playing catch-up with delayed work orders, missing BOMs, and reactive fixes? You’re not alone. In this episode of the Verdantis Insights Podcast, host Priyanka Agarwal, Global Marketing Specialist at Verdantis, sits down with Mohit Mittal, Product Manager at Verdantis, to uncover the hidden culprits behind work order inefficiencies, and how to overcome them.
From fragmented asset records to unlinked BOMs and duplicated part orders, we unpack the data challenges plaguing industries like manufacturing, utilities, energy, and F&B. Mohit shares real-world examples and explains how AI-powered solutions and a data-first approach can transform your maintenance operations and cut downtime.
Why poor asset and BOM data is the root cause of most maintenance inefficiencies
How fragmented records lead to costly delays and unplanned downtime
Common red flags: inconsistent descriptions, free-text work orders, and missing asset links
How leading companies are fixing the problem using AI-powered classification, enrichment, and governance tools
The Verdantis approach to modernizing asset data and automating work orders
Actionable steps to audit and improve your current maintenance data landscape
This episode is a must-listen for:
Maintenance & Reliability Engineers
Plant Operations Managers
Asset Management Leads
CMMS and SAP PM Users
MRO Procurement Specialists
Digital Transformation Leaders
Anyone responsible for uptime, compliance, or maintenance performance
Extract, Enrich & Sync Technical Data to ERP Systems
AI led enrichment tool for data harmonization
AI-powered parts and inventory management software
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.
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: 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:
Results Achieved:
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.
