Why Maintenance Stores Hold Millions in Idle Cash — and How AI Sets It Free

Walk into almost any engineering storeroom and you’ll find shelves stacked high with parts that haven’t moved in years. Bearings for machines long gone. Motors still boxed in their original wrapping. Seals, sensors, and gearboxes “kept just in case.”

On paper, those shelves represent safety. In reality, they represent cash — often millions tied up in inventory that will never turn.

Analysts estimate that 40–60% of all MRO inventory is excess, obsolete, or rarely used. That’s not just inefficient — it’s value frozen on the balance sheet.

The Hidden Cost of ‘Safety’

Every maintenance team fears the line-stopping breakdown. The natural response is to hold more stock “just in case.” But that safety blanket has a cost — and it’s not just the capital sitting on the shelf.

Over-stocking drives a cascade of hidden expenses:

• Labour cost: hours lost hunting through cluttered stores or raising duplicate orders.
• Lost productivity: fitters waiting while planners search for the right part.
• Space cost: obsolete stock filling aisles instead of value-adding material.
• Utilities and overheads: every extra square metre needs lighting, heating, and cleaning.
• Handling cost: each time a part is counted, moved, or checked, time and risk are added.

Add it all up, and the total cost of holding inventory typically runs at 20–30 % of its value every year. A £5 million MRO store could be quietly consuming more than £1 million annually in carrying cost alone — before a single part is fitted to an asset.

Why the Problem Persists

Maintenance and procurement leaders aren’t hoarders by choice. They’re working within systems built on fragmented data and limited visibility:

• Obsolescence: parts remain in the catalogue long after assets are retired.
• Over-stocking: fear of downtime outweighs confidence in the data.
• Poor naming conventions: the same bearing appears under multiple descriptions.
• Limited equivalence mapping: identical or substitute parts aren’t linked.

ERP and CMMS systems record this information faithfully — but they don’t understand it. To a planner,

“6205-ZZ SKF Bearing,” “Deep Groove 6205-2Z,” and “Bearing FAG 6205-ZZ”

are obviously the same item. To the system, they’re three unique SKUs.

Multiply that across tens of thousands of records and you end up with the modern MRO dilemma: no one really knows what’s on the shelf, what’s needed, or what’s redundant.

Where AI Changes the Game

Artificial Intelligence can now do what traditional data tools never could: understand context.

AI models trained on industrial data can:

• Read and interpret free-text descriptions like a human.
• Identify identical, equivalent, and alternative parts across different systems.
• Detect duplicates automatically.
• Map relationships between parts, assets, and suppliers.
• Recommend rationalisation or substitution options before new stock is ordered.

The result is a shift from “data chaos” to intelligent inventory — where every item can be traced, compared, and optimised across the enterprise.

From Counting Stock to Creating Insight

AI doesn’t just tidy up master data; it transforms how we make stocking decisions.

Traditional Approach AI-Enabled Approach Static min/max based on historical usage Dynamic levels based on predicted demand and lead-time variability Manual duplicate reviews Automated equivalence detection across thousands of SKUs Reactive obsolescence checks Predictive alerts using OEM and supplier data Disconnected ERP, CMMS, procurement Connected view linking assets, parts, and usage history

This moves MRO from being a cost centre to a source of intelligence. Instead of quarterly spreadsheet reviews, planners gain live visibility into where stock sits, what’s moving, and what’s wasting money.

Releasing Trapped Capital

When applied systematically, AI helps identify and eliminate inefficiency:

• Duplicates: merge or retire redundant stock codes.
• Obsolete stock: flag items linked to retired equipment.
• Excess stock: quantify the financial impact of over-stocking.
• Inter-site sharing: enable one site’s surplus to become another’s spare.

Organisations applying AI-driven MRO optimisation are realising 20–40 % reductions in stock value while improving availability and service levels.

The Path to an Intelligent Store

AI is an accelerator — but only if the foundations are solid.

1. Standardise naming conventions Use a noun–modifier–attribute structure and validate against a master data library.
2. Connect your systems Link ERP, CMMS, and procurement data so every part ties back to the equipment it serves.
3. Apply intelligent models Detect duplicates, equivalents, and alternatives. Calculate optimal stock based on demand, lead time, and criticality.
4. Turn insight into action Dashboards that expose stock value, obsolescence, and working-capital opportunities make improvement visible — and accountable.

Beyond the Store — Predictive Supply

The next evolution is where AI connects reliability and inventory.

As predictive maintenance models forecast the probability of failure, MRO systems can check whether the right spare is available — and if not, trigger replenishment proactively.

Imagine a maintenance plan that doesn’t just predict failure, but ensures the replacement is already on the shelf. That’s where connected data across engineering, maintenance, and procurement starts to deliver genuine reliability and resilience.

A Mindset Shift — From Hoarding to Learning

The storeroom is a mirror of maintenance culture. In reactive environments it’s a safety net; in proactive ones it’s a strategic asset.

AI doesn’t replace human judgement — it enhances it. It gives engineers, planners, and procurement teams the confidence to make evidence-based decisions and to challenge the old belief that more stock equals more safety.

The Payback

AI-enabled spare parts management delivers benefits that are both financial and cultural:

• 20–40 % reduction in working capital
• Inventory turns doubled (from 0.5 → 2.0+)
• 15–30 % fewer emergency purchases
• Improved maintenance productivity from faster part identification and retrieval
• Reduced overheads — smaller stores, less lighting, heating, and labour
• Higher asset uptime and planning accuracy

And perhaps most importantly, it reconnects Finance, Engineering, and Procurement around a shared objective: Reliability at the lowest lifecycle cost.

Closing Thought

For years, manufacturers have focused on digitalising assets and production data. Now it’s time to bring the same intelligence to the stores — to build a connected, searchable, and optimised view of every part and its purpose.

That’s the promise of AI in MRO: Turning dusty shelves into data, data into insight, and insight into cash, confidence, and reliability.

#Maintenance #AssetManagement #MRO #AI #Reliability #DigitalTransformation #WorkingCapital Two6 Services Ltd