Every maintenance team knows that equipment reliability depends on having the right part, at the right time. But in practice, that often isn’t what happens. When spare parts data is incomplete, duplicated, or inconsistent, it creates a hidden layer of inefficiency that slows maintenance, increases downtime, and drives up costs.
In industries such as mining, oil & gas, utilities, and manufacturing, where every hour of downtime can cost millions, poor MRO (Maintenance, Repair, and Operations) data can quietly become one of the biggest operational risks.
The Hidden Cost of Dirty MRO Data
ERP and EAM system were never designed to handle inconsistent data across sites and suppliers. Over time, product data from multiple vendors, mergers, or regions accumulates inconsistencies:
- Duplicate part numbers that inflate stock levels.
- Missing attributes such as material, size, or manufacturer.
- Vague descriptions like “pump assembly” or “valve kit” that make identification impossible.
- Inconsistent naming conventions across plants and teams.
When technicians or planners search for a part, they often face thousands of similar results, leading to procurement confusion, wrong part orders, and unnecessary rework.
A McKinsey study found that maintenance teams spend up to 30% of their time searching for information, time lost due to unstructured or inaccurate data.
How Poor Data Causes Downtime
When MRO data is unreliable, every step of the maintenance process slows down:
- Identification Delays
Technicians can’t quickly confirm the correct spare part, resulting in extended troubleshooting and longer mean time to repair (MTTR). - Procurement Inefficiency
Buyers issue duplicate RFQs for the same item under different names, extending lead times and fragmenting supplier contracts. - Inventory Inaccuracy
Duplicate records distort visibility, leading to excess inventory of some parts and shortages of others. - Maintenance Planning Breakdowns
Inconsistent data undermines preventive maintenance scheduling and predictive analytics, reducing asset uptime.
The result: longer downtime, higher maintenance costs, and reduced asset reliability, all driven by poor data quality.
The Solution: Clean, Enriched, and Classified MRO Data
To restore reliability, maintenance operations need structured, accurate MRO data that gives complete visibility into every part, asset, and supplier. That requires:
- Cleansing — Removing duplicates and obsolete entries.
- Enrichment — Filling missing fields such as dimensions, OEM details, and hazardous classifications.
- Classification — Applying taxonomies like UNSPSC or ISO 14224 to create structure and interoperability.
Clean MRO data allows technicians to find the right part instantly, procurement to source effectively, and maintenance teams to plan with confidence.
How AICA Helps Improve Maintenance Reliability
AICA specialises in transforming MRO and spare parts data into structured, reliable datasets that directly improve maintenance performance.
Our Agentic AI platform delivers:
- Automation at scale: Millions of records cleansed, enriched, and classified in weeks.
- High accuracy: Over 90% precision in classification, supported by human QA/QC for critical data.
- Standardisation: Alignment to UNSPSC, GS1, and ISO 14224 for global interoperability.
- Integration: Seamless deployment into ERP, EAM, and CMMS systems for continuous data governance.
By combining automation with subject matter expertise, AICA helps organisations reduce downtime, control inventory costs, and strengthen maintenance reliability across their operations.
Conclusion
Reliable maintenance starts with reliable data. Poor MRO data leads to confusion, delays, and downtime, but with clean, enriched, and classified datasets, maintenance teams can operate proactively rather than reactively.
We’re here to help organisations build that foundation by transforming messy MRO data into a trusted, structured asset that supports uptime, efficiency, and long-term operational excellence.
Visit our website to learn how AICA helps maintenance teams eliminate downtime caused by poor data quality.
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