How a multi-refinery fuels producer built an MRO case its CFO could audit line by line, grounded in its own reported inventory figures, not vendor estimates.
A North American fuels refiner operating multiple complex refineries across several states, each with its own storeroom, its own material master records, its own reorder parameters, and its own institutional knowledge of which parts matter.
A turnaround-intensive estate, a disciplined shareholder-return framework, and portfolio actions ahead that will split systems and item masters. Like most refiners, its recent operating history includes publicly reported unplanned events, a reminder of what is always at stake in an industry that logs unplanned incidents in the public record most years.
MRO360 was proposed as a governed intelligence layer on the operator's existing SAP landscape, nine interconnected agents reading extracts continuously and writing approved values back into the same SAP fields the planners already use. No migration, no new system of record, no change to how SAP operates. Every write-back requires explicit human approval and carries its derivation.
All customer figures in this case study are expressed as ratios to protect confidentiality. The base of the case is the repair-and-maintenance-supplies inventory line in the operator's own audited filings, a figure its finance team could verify independently.
This operator runs SAP, and runs it well. The problem was never what SAP does, it is what SAP MRP was never designed to do. MRP calculates replenishment from consumption history, lead time, and a service level a planner typed in. Nothing in that model reads equipment criticality, redundancy, failure consequence, RCA findings, or supplier reliability.
The download covers all six modules, the reasoning behind the reduction target, and why the base number came from the operator's own filings rather than a vendor estimate.
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Every assumption was set deliberately below published benchmarks, and the base number came from the operator's own audited filings.
| Metric | Result |
|---|---|
| One-time working capital release | Eight-figure, at a ~10% reduction target against 30–45% benchmarks |
| Recurring benefit | ~30% of the release, every year |
| Carrying-cost saving | ~22% per year on capital freed |
| Downtime cost | $500,000+ per hour, published industry benchmark |
| Missing-spare share of downtime | 23% of unplanned downtime, published benchmark |
| Downside case | Still decisively positive at half the assumed capture |
Avoided downtime was deliberately excluded from the headline case and positioned as upside, to be measured against the operator's own downtime records rather than promised in advance.
Accountable for a nine-figure capitalised turnaround balance and long-lead spares staged years ahead.
Closing the plant-by-plant silo that sends emergency orders past parts already sitting on a shelf elsewhere.
Needing a base number the CFO's office can verify independently, not a vendor estimate.
Running MRP well, but needing a computation layer rather than a replacement, through an ECC-to-S/4HANA transition.
If your MRP runs on parameters last reviewed years ago, or your refineries buy externally what the network already holds, the same case can be built on your own filings and a single SAP extract, in weeks.
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.
