Conrad Greer & The AI Future of Spare Parts Data (Part 4)

Throughout the first three parts of our series with MRO data specialist Conrad Greer, we examined the fundamental challenges of spare parts management—from the hidden costs of inconsistent item identities to practical methods for quantifying their operational and financial impact. We then moved towards solutions, focusing on how structured taxonomies, sound governance, and rationalised catalogues lay the foundation for long-term success.

Now, in Part 4, we shift our attention to the future. Greer explains why the era of uncontrolled MRO data is drawing to a close—and how artificial intelligence (AI) and machine learning (ML) are poised to become the transformative forces behind sustainable, scalable MRO data cleansing and management. Whether your organisation is grappling with legacy data or seeking to embed smarter practices into daily operations, this final chapter offers a clear perspective on what lies ahead—and how to prepare.

The Real Barrier Has Never Been Technology

Looking ahead, Greer remains optimistic: the days of uncontrolled spare parts data are numbered.

"I believe the problem of uncontrolled item identities will be solved in 10 years."

Greer argues that the core obstacle has never been purely technological—it has been conceptual. For decades, organisations have responded to MRO data challenges with isolated MRO data cleansing exercises. While well-intentioned, these initiatives seldom addressed the structural relationships between items, focusing instead on formatting or standardising descriptions at the record level.

"The real issue hasn’t been a lack of effort—it’s been the absence of the right framework for understanding how items are interconnected."

A Turning Point: AI and Machine Learning for MRO Data Cleansing and Management

What has changed? According to Greer, the industry has now clarified the conceptual basis of the MRO identity problem. The emergence of scalable, intelligent tools—powered by machine learning—has made lasting solutions for MRO data cleansing and management both possible and inevitable.

Although AI dominates headlines, Greer emphasises that the real breakthrough lies in Machine Learning algorithms that can:

• Detect patterns within vast datasets
  
  
• Interpret contextual relationships between attributes
  
  
• Identify duplicates and enrich incomplete records
  
  
• Learn continuously from new inputs and operational feedback
  
  

"This is fundamentally a machine learning problem—and we now have the tools to solve it at scale."

When applied correctly, these technologies enable organisations to build, maintain, and evolve clean, structured MRO data catalogues without relying solely on manual interventions.

Greer foresees a near future in which uncontrolled spare parts data becomes a rarity, and the benefits of MRO data cleansing extend beyond a few best-in-class enterprises.

"Companies with highly uncontrolled identities will become rare."

Solutions like SPARROW.Clean already exemplify this approach, combining machine learning with operational expertise to systematically cleanse and harmonise MRO data at scale.

From Incremental Fixes to Unified Standards

In his consulting work, Greer observes many organisations taking tentative steps to improve spare parts data quality, often by tightening controls around new material requests or individual entries.

"Most teams are attempting to introduce some consistency to new data. But the real value lies in establishing a forward-looking discipline."

While such efforts are commendable, they frequently fall short of tackling the larger issue: legacy spare parts data. With catalogues containing upwards of 150,000 unstructured records, many organisations view full-scale MRO data cleansing efforts as unmanageable.

Greer recounts a familiar refrain from clients: "We call it what the requester wanted to call it."

Although frontline staff possess valuable knowledge, the absence of a shared naming convention results in subjective, inconsistent descriptions.

"If a technician calls it a right-handed wing nut, it may make sense to them, but it doesn’t make it searchable or comparable elsewhere."

Embedding Structure into Everyday Processes

Greer advocates a pragmatic yet powerful approach: embedding structured taxonomy into every step of the materials request process. While a full data overhaul may not be feasible immediately, organisations can:

• Apply standard classification methods to all new entries
  
  
• Normalise data intake processes, regardless of who submits the request
  
  
• Educate requesters on the benefits of standardisation
  

"It’s not about rejecting technicians’ expertise. It’s about giving their knowledge a structure that works across the organisation."

That’s why SPARROW.Clean, for example, not only enriches datasets with official manufacturer information but also preserves legacy nomenclature—ensuring consistency between historical and newly added spare parts data.

By consistently applying these practices, organisations can gradually stabilise their catalogue, improve searchability, and reduce duplication—without overwhelming operational teams.

Conclusion: Small Shifts, Lasting Impact

As Greer highlights, improving spare parts data quality does not always require sweeping reform. The journey towards a rationalised, high-integrity catalogue often begins with small, everyday shifts in how organisations handle new entries.

By formalising how spare parts are named, classified, and entered—irrespective of who initiates the request—enterprises can begin to stabilise their master data, minimise operational friction, and foster collaboration across maintenance, supply chain, and procurement.

It is not about replacing engineering or maintenance expertise, but about translating that expertise into a shared, system-friendly language that drives efficiency at scale.

Through steady, structured improvements, organisations can work towards a future in which clean, consistent MRO data is not the exception, but the standard.

Series Conclusion: The Future of Spare Parts Management Begins with Better Data

Throughout this four-part series, we have explored Conrad Greer’s expert perspective on one of the most persistent challenges facing asset-intensive industries: uncontrolled spare parts data.

Greer’s message is clear: poor MRO data is not merely an administrative issue—it is a systemic vulnerability with significant operational, financial, and safety implications. More importantly, MRO data cleansing is a solvable problem.

Key Takeaways:

• Part 1 revealed how inconsistent item identities undermine systems and operations.
  
  
• Part 2 highlighted the financial and operational costs of poor MRO data, alongside methods for quantification.
  
  
• Part 3 provided practical approaches to MRO data cleansing and standardisation implementing structured taxonomies and scalable governance.
  
  
• Part 4 explored the transformative potential of AI and ML in automating and maintaining MRO data integrity.
  

Looking Ahead

As Greer notes, the foundational principles for solving the MRO identity crisis already exist. Over the next decade, clean and scalable materials data will move from a competitive differentiator to an industry norm.

Yet, while machine learning and automation offer exciting possibilities, true progress depends on establishing a solid foundation:

• Embedding structured taxonomies at every stage
  
  
• Empowering operations, procurement, and maintenance to jointly steward data quality
  
  
• Developing catalogues that evolve alongside operational needs
  
  

How SPARROW Supports This Journey

At SPARROW, we have built solutions to support this transformation:

• SPARROW.Clean leverages AI-driven classification and operational expertise to help organisations cleanse, harmonise, and maintain spare parts data with precision.
• SPARROW.Plan enables teams to forecast and align MRO planning with maintenance schedules, reducing downtime and ensuring critical parts are always available while avoiding overstock. 
• SPARROW.Stock features a user-friendly interface designed to ease the transition from legacy systems, making it simple for teams to locate and manage items across storerooms. By supporting intuitive daily use, it encourages long-term adoption among staff and ensures spare parts data and inventory levels remain accurate and up to date.
• And SPARROW.Pool facilitates secure, strategic parts sharing across sites or partners, accelerating access to rarely used or long-lead components while avoiding duplication and unnecessary purchases.

Whether you are integrating operations following a merger, rationalising legacy catalogues, or preparing for ERP migrations like SAP S/4HANA, Sparrow ensures your MRO data works for you—not against you.

Let’s Build a Smarter Spare Parts Strategy Together

MRO data is more than an IT asset; it is fundamental to operational reliability, regulatory compliance, and cost optimisation. With the right tools, structure, and strategy, spare parts data can be transformed from a hidden liability into a strategic advantage.

Ready to take the next step? Connect with us to schedule a discovery call or request a tailored demonstration.

===> Missed Part 1: Conrad Greer & The Risks of Broken Data

===> Missed Part 2: Conrad Greer & The Real Cost of Bad Data

===> Missed Part 3: Conrad Greer & Building Sustainable Data Frameworks

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