Who We Serve → Maintenance
Your Maintenance team is only as good
as the data behind it.
Asset-intensive organizations rely on EAM and CMMS data to drive every maintenance decision. When that data is in disrepair, your team pays for it in downtime, overtime, and lost wrench time.
The Problem
You've Got a Maintenance Data Problem That's Getting Harder to Ignore
As a maintenance leader, your goal is to keep equipment running reliably, minimize unplanned downtime, and run a cost-effective operation. However, most organizations are working against themselves. They are managing maintenance on data they don't fully trust and in systems that are harder to change than they should be. The result is reactive maintenance, inflated costs, and a team spending 30–40% of their shift finding information instead of fixing things.
Your current options aren’t working
Most maintenance teams try one of these approaches. None of them solve the root problem.
"We'll fix it directly in SAP or Maximo"
Making changes to a live EAM system is risky and slow. A single strategy change in SAP requires a precise multi-step process across task lists, maintenance items, and maintenance plans in a specific order. Done manually, a single project can take over 12 months of man-time, and one error in production can cascade across thousands of records.
"We'll manage it in spreadsheets"
Spreadsheets can't model complex relational EAM data. They don't reflect the true inter dependencies between maintenance strategies, task lists, and maintenance plans. They provide poor visualization, can't validate data before it goes live, and break down entirely at scale leaving you with a false sense of control and data you still don't trust.
"We'll let IT build something custom"
Custom solutions take months to build, require specialized knowledge of your EAM's data model, and rarely stay current as your systems evolve. You end up with a rigid tool that handles yesterday's problem, forces workarounds for today's, and leaves your team dependent on a small group of people who understand how it works.
NRX ASSETHUB
offers a better path
Purpose-built for asset-intensive organizations running SAP, Maximo, Oracle, and more .
NRX AssetHub gives maintenance teams the tools to build, govern, and trust their EAM data without the risk, the rework, or the wait.
40%
Average wrench time in most industries. The rest is spent finding and verifying data
5X
More expensive to run reactive maintenance than a planned and predictive approach
10%
Of annual revenue spent on maintenance at asset-intensive organizations
Where it Hurts
Three ways poor EAM data hits maintenance teams hardest
Poor data quality doesn't just slow maintenance down, it compounds across every team, every shift, and every asset class.
Work Management
Planners and technicians need accurate asset records, well-defined work tasks, and reliable maintenance plans to do their jobs. Without clean EAM data, they spend 30–40% of every shift locating and verifying information before a single wrench is turned.
→ Decreased wrench time across all shifts
→ Incomplete or incorrect work tasks and maintenance plans
→ Poor allocation of human capital
→ Missing or inefficient use of specialized skills
→ Sub-optimal maintenance spend vs Replacement Asset Value
Knowledge Management
As experienced maintenance workers retire, the knowledge they carry needs to live in your systems and not in their heads. When EAM data is incomplete or inaccurate, that institutional knowledge disappears and the next generation inherits a broken foundation.
→ Inability to build comprehensive maintenance plans
→ Inability to set best practices across the enterprise
→ Increased environmental, health, and safety risk
→ Over-reliance on individual expertise vs documented systems
Maintenance Costs
Asset-intensive companies spend 5–10% of annual revenues on maintenance. Reactive maintenance is two to five times more expensive than a planned approach and unreliable EAM data is what forces teams into reactive mode in the first place.
→ Critical spare parts sourced urgently at premium cost
→ Incomplete preventive maintenance plans and task descriptions
→ Obsolete parts catalogs causing procurement errors
→ Downtime waiting for technicians with specific skill sets
→ Under or over utilization of maintenance personnel
How NRX ASSETHUB Helps
Built for the complexity maintenance teams actually face
NRX AssetHub gives maintenance teams the tools to build, govern, and trust their EAM and CMMS data so the work gets done right, the first time.
01
Build a Clean Data Foundation
Identify gaps, duplicates, and inconsistencies across your EAM or CMMS. Build accurate asset hierarchies, equipment records, and maintenance plans to the right standards before errors compound across shifts and sites.
02
Transition from time-based to condition-based PMs. Consolidate overlapping strategies. Automate the complex sequencing required in SAP or Maximo without manual re keying or the risk of breaking a live production environment.
03
Trust Your CMMS Data
Give planners and technicians data they can act on without verification. Increase wrench time. Reduce reactive maintenance. And build the clean foundation your AI and IIoT investments depend on to deliver real results.
Real World Results
From 12 months of manual work to 2 months with NRX
See how NRX AssetHub helped a global Oil & Gas company restructure thousands of SAP maintenance plans automatically, accurately, and without production risk.
Oil & Gas — SAP PM
The Challenge
Over years of using SAP PM, the company had accumulated dozens of redundant maintenance strategies, each created because the right time unit or package wasn't available in the existing strategy. They needed to consolidate everything into one standardized approach. The process required removing strategy packages from task lists, deleting maintenance items, flagging plans for deletion, updating the strategy, and rebuilding the structure in a precise order. In SAP or Excel, this would have taken over 12 months of man-time for the dataset involved.
Completed in 2 months. ~90% automated.
NRX AssetHub automated the required changes in the correct sequence, generated SAP load files at each step, validated in the Quality environment, and deployed to Production with business involvement limited to approval sign-off only.
767 maintenance plans changed | 2,034 task lists updated | 30,878 operations processed | 12 months to 2 months | ~90% automated
%
of industrial maintenance teams are already using AI tools
%
report measurable ROI within six months of adoption
AI doesn't fix a broken data foundation. It runs on top of one.
Those numbers are remarkable. So why are most organizations still struggling to improve asset reliability despite broader AI investment?
Because AI learns from your data. When that data is broken, AI learns the wrong patterns and generates predictions nobody trusts. Maintenance teams go back to doing things the way they always have.
- Duplicate asset records feeding bad predictions
- Incomplete maintenance histories skewing failure models
- OEM manuals nobody can search or reference
- Spare parts data spread across spreadsheets and invoices
Getting Started
How we work with your maintenance team
From first conversation to live deployment. Here's what working with NRX looks like.
01
We learn your process
We start with a conversation and not a pitch. Our team digs into your current EAM setup, your biggest data pain points, and where you're losing the most time. We prepare a tailored demo based on your actual environment.
02
We show you what's possible
Bring your key stakeholders. We'll walk through your specific challenges, show how NRX has solved them for similar organizations, and give you a clear picture of what improvement looks like for your team.
03
We build the business case
Our team models the ROI based on your data volumes, your team size, and industry benchmarks. We work directly with your IT team on security, compliance, and integration with your existing EAM and ERP systems.
04
We stay with you
NRX doesn't disappear after go-live. Our services team supports you through configuration, training, and ongoing optimization so your maintenance data stays clean and your team stays confident in what they're working with.