Inside MRO – Blog Series Part 5

In the previous posts, we’ve explored the nuts and bolts of spare parts management—from culture and data to best practices and technology. Now, in this final installment of Inside MRO: Lessons from the Frontlines of Spare Parts Strategy, Andrew Jordan offers his forward-looking perspective on what might be the biggest disruptor in the space: machine learning and AI for Inventory Management.

‍

But while AI for Inventory Management is generating excitement across supply chains, Jordan offers a grounded view. In his experience, the biggest opportunity which is machine learning for inventory optimization isn’t replacing planners or automating decisions—it’s using AI to uncover insights humans wouldn’t find on their own.

‍

‍

AI for inventory Management: Don't Press the Easy Button

‍

Jordan has long been skeptical of how AI is marketed—especially in supply chain contexts where the systems are complex and the stakes are high.

‍

“Historically I’ve been somewhat cynical when it comes to AI...
One of the things I think people want to do too quickly with AI is just again, press that easy button.”

‍

The danger, he argues, is using AI for inventory management to generate recommendations without understanding the rationale behind them. For instance, if an AI tool suggests raising a part’s min/max levels, planners should be able to interrogate the data behind that decision—not just accept it blindly.

‍

“If you can’t tell me why, well, I’m not interested.”

‍

This insistence on transparency when it comes to spare parts management reflects a core belief: machine learning for MRO should augment human decision-making, not bypass it. And for that, the logic behind AI for inventory Management must be traceable, explainable, and based on meaningful inputs.

‍

‍

Where Machine learning for MRO Truly Shines

‍

Jordan does see enormous potential in applying AI and advanced analytics to the right problems—particularly those involving large, complex, or under-analyzed data sets. He highlights several use cases of spare parts management where the technology can be especially impactful:

‍

1. Machine Data Analysis for Failure Patterns

By analyzing equipment runtime and maintenance records, AI can help isolate variables that contribute to asset failure—whether it’s operator error, inconsistent maintenance, or environmental factors. 
‍

“You can take a big data set around an operating asset and understand downtime and why it was down and what happened before and after...
Was it operator error? Maintenance? The part itself?”

In other words, machine learning for inventory optimization can be a huge game-changer. 

‍

2. Material Preservation and Lifecycle Tracking

AI for inventory management can also be used to assess how different storage and preservation practices impact the lifespan of components like motors, belts, and bearings.
‍

“I think one of the big opportunities with spare parts is material preservation.
The more stories I go around, the more I see bearings covered in dust and belts hanging from hooks.”

If AI can track the correlation between how a part is stored and how it performs in service, organizations can shift from reactive replacement to predictive maintenance and proactive longevity.

‍

3. Forecasting for Consumables

While finished goods forecasting receives significant attention, consumables like lubricants, gloves, and filters are often managed through outdated spreadsheets or basic averages. Machine learning for MRO could modernise that process.
‍

“People spend a tremendous amount of time forecasting their finished goods...
But for consumables? They’re doing it with a moving average in a spreadsheet.”

‍

‍

The Real Opportunity: Insight, Not Automation

‍

For Jordan, the true promise of AI in MRO lies in driving better conversations. AI for inventory management should surface signals and patterns humans might miss—then help them make smarter, faster, and more informed decisions.

‍

“There needs to be that understanding...
These things for me, I don’t think they should ever drive the action.
I think they should drive the insight, so that the person can evaluate the outcome.”

‍

By applying Machine learning for MRO this way, companies can close long-standing gaps—between maintenance and spare parts inventory, between spare parts strategy and asset reliability—without losing the human judgment that’s critical in high-risk environments.

‍

‍

Closing the Series: From Siloed Thinking to Systemic Change

‍

Across this six-part Inside MRO series, Andrew Jordan has walked us through the mindset, mechanics, and methods of world-class spare parts management. His perspective underscores one consistent truth: success doesn’t come from tech, tools, or tactics alone—it comes from treating MRO Excellence as an interconnected ecosystem.
‍

“I no longer believe I can fix everything by focusing on inventory alone.
I think about it more in terms of holistic medicine. Everything is connected.”

‍

From master data management and organizational culture to technology and AI for inventory management, spare parts management is due for a reframe. And for companies ready to make that shift, the results aren’t just measurable—they’re transformative.

‍

‍

Have you found this blog post insightful? Don't miss the previous installments:

‍

Part 1: What I Missed About Spare Parts and MRO — Until Everything Pointed Back to Them

‍

Part 2: Why Parts Inventory Management Doesn’t Get the Love—And Why That’s Hurting Your Business

‍

Part 3: The Gold Standard in MRO Optimisation: What Top Performers Do Differently

‍

Part 4: Don’t Blame ERP systems: What MRO Really Needs from Tech

‍

Explore the Sparrow Hub

Whether you're just beginning your spare parts transformation or looking to take it further, the Sparrow Hub brings together everything you need to modernise MRO—starting with data and ending with impact. From Sparrow.CLEAN for master data standardization, to Sparrow.PLAN for smarter maintenance alignment, Sparrow.STOCK for dynamic stocking policies, and Sparrow.POOL for multi-site inventory sharing—Sparrow helps you make better spare parts decisions, faster.
‍

👉 Ready to take the next step? Get in touch with us or book a demo to see how it all works.

‍