Explore how Agentic AI is transforming procurement data quality and enabling automated, system-driven operations.
Procurement and supply chain operations in production-intensive industries are notoriously complex. Legacy systems, disconnected processes, and manual interventions make it difficult for enterprises to modernize especially when poor data quality prevents them from deploying AI agents that could automate and transform day-to-day workflows.
In supply chain dynamics, even minor data inconsistencies can cascade into major operational disruption. Poor material and procurement data amplifies the Bullwhip Effect – where small shifts in demand lead to excessive inventory swings, stockouts, expediting costs, and reduced trust in planning systems. This is the same pattern often demonstrated through the Beer Game concept – showing how inaccurate signals create chaos across the value chain.
Yet the same AI technologies that rely on high-quality data can now also be used to improve it. With the right training data, domain-specific prompts, and purpose-built models, AI agents can resolve data quality gaps at scale removing one of the biggest barriers to automation in ERP and procurement environments.
Watch this insightful session with David Inbar, Founder of Minet Technologies and a renowned leader in supply chain and procurement technology innovation. David will break down how enterprises can practically adopt these new capabilities – and what steps to take today to:
• Automate repetitive, time-consuming processes
• Improve data quality at the source
• Enable intelligent, system-driven procurement operations
• Remove key barriers holding back AI transformation
Who Should Watch:
If you’re exploring how AI agents can transform procurement performance and data governance – this conversation is for you.
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.
