Conrad Greer & The Real Cost of Bad Data (Part 2)
In Part 1 of this series, we uncovered how inconsistent spare parts data undermines ERP performance, causes maintenance delays, and creates operational inefficiencies—from inventory bloat to safety risks. We explored the essential role of standardisation and data integrity in restoring control over spare parts management.
Now, in Part 2, we take a practical step forward. Conrad Greer shares how organisations can quantify the hidden costs of poor MRO data, providing both quick estimation methods and detailed analysis approaches. By putting a monetary value on inefficiencies, companies can build a strong business case for investing in spare parts data improvement.
A growing body of research highlights the importance of data quality in industrial operations. A McKinsey report on predictive maintenance stresses that clean data is the foundation for digital transformation initiatives, and without it, advanced analytics and automation projects often fail to deliver expected value [1].
Master Data as the Foundation for MRO Excellence
Greer emphasises that spare parts master data is the bedrock of effective MRO operations. Everything from procurement and inventory management to maintenance planning relies on accurate, searchable spare parts data.
"I've worked with databases between 150,000 and 700,000 records."
Managing such large inventories poses unique challenges. According to the Aberdeen Group, up to 30% of MRO parts in typical industrial catalogues are redundant or obsolete [2]. Without rigorous spare parts data management, duplicate entries and missing records proliferate, clogging systems and reducing operational efficiency.
Greer stresses that many companies fine-tune reorder points and stock levels without addressing the underlying spare parts data quality issues. He warns that such efforts are unlikely to deliver meaningful results unless organisations first focus on correcting item identities and catalogue structures.
The Disconnect Between Projects and Operations
Another frequent point of failure occurs at the interface between capital projects and operations. Large projects often include significant investment in spare parts provisioning, with suppliers delivering documentation and stock forecasts.
However, as Greer explains, this MRO data frequently fails to transition effectively into operational systems:
"A project's first few years of spare parts might be funded, but the data handover is often incomplete. It’s like a relay where the baton never gets passed."
This handover challenge is common in asset-intensive industries. The International Energy Agency (IEA) highlights that many organisations still lack formal processes for digital handover between project and operations teams, leading to costly inefficiencies [3].
Maintenance Strategy and the Procurement Perspective
Greer emphasises that maintenance and procurement strategies must be aligned. Every decision regarding spare parts—from stocking policies to supplier selection—should be grounded in a clear maintenance strategy.
Drawing on his experience in the Canadian oil sands, he illustrates how operational realities evolve over time:
"Years ago, they needed to stock everything on-site. Now, local suppliers can get parts delivered in 40 minutes."
In the past, remote locations necessitated large on-site inventories. Today, improved logistics and supplier networks enable leaner inventory strategies—but only when accurate spare parts data enables confident sourcing decisions.
A 2022 PwC study shows that companies with advanced MRO strategies reduce working capital tied up in inventory by up to 20% [4]. This underscores the importance of treating MRO data as a strategic asset.
Solutions like SPARROW.Plan are designed precisely for this purpose—bridging the gap between maintenance strategy and data-driven decision-making. By enriching and structuring MRO master data with operational context, SPARROW.Plan helps teams align procurement with evolving maintenance needs, enabling smarter stocking decisions and reducing working capital tied up in inventory.
Good Data Doesn’t Need to Be Perfect—But It Must Be Usable
Perfection isn't necessary for MRO data to be effective. Instead, Greer advocates for a pragmatic focus on usability:
"You don't need perfect data. You need good enough data that supports informed decisions."
Critical spare parts data fields—such as part number, manufacturer, material type, and lead time—must be consistently populated to enable effective decision-making.
According to Accenture, focusing on "fit-for-purpose" MRO data quality can deliver faster results and higher ROI than perfection-focused initiatives [5]. Organisations that prioritise usability often outperform those stuck in pursuit of unattainable accuracy.
Quantifying the Operational Loss of Poor Spare Parts Data
To drive action, Greer encourages companies to quantify the operational and financial impact of poor spare parts data:
“Companies need to understand the pain and waste they live with for ignoring poor MRO item identities. Too often the waste is accepted as a cost of doing business.”
He proposes two complementary approaches:
- Parametric Estimation: A quick, approximate method based on multiplying estimated annual inefficiency per employee by the number of staff involved in MRO processes.
- Detailed Impact Assessment: A structured approach that lists all operational failures linked to poor data—duplicate orders, emergency buys, delayed jobs, etc.—and estimates the frequency and cost of each.
"There’s significant opportunity in addressing these inefficiencies."
Studies by LNS Research indicate that MRO data improvement projects can deliver ROI ratios between 5:1 and 15:1, demonstrating the substantial savings available from better parts data [6].
Conclusion: Turning Recognition into ROI
As Conrad Greer's insights reveal, poor spare parts data is far more than a technical inconvenience—it's a serious operational risk and a financial drain.
By quantifying the cost of inaction, organisations can build a compelling business case for spare parts data improvement. While perfection is not required, achieving a usable, structured, and consistent dataset unlocks wide-ranging benefits—from cost reductions to improved maintenance performance.
===> Coming Up in Part 3: We’ll explore practical methods for cleansing, rationalising, and maintaining spare parts catalogues over time.
===> Missed Part 1? Read Conrad Greer & The Risks of Broken Data
Sources:
[1] McKinsey & Company. “The Future of Maintenance Is Predictive.” 2021.
[2] Aberdeen Group. “Best-in-Class MRO Strategies.” 2019.
[3] International Energy Agency (IEA). “Digitalisation and Energy: A Review.” 2021.
[4] PwC. “Spare Parts Inventory Management Benchmark Study.” 2022.
[5] Accenture. “Intelligent Asset Management: Unlocking Value through Data.” 2022.
[6] LNS Research. “Optimizing MRO Strategies for Asset Performance.” 2020.
