In the complex world of spare part inventory management, we all know that decision-makers are constantly striving to optimize stock levels, reduce costs, and ensure operational reliability.
However, what many don’t realize is that a subtle and dangerous cognitive bias often creeps into these decisions: survivorship bias.
This is a logical error: focusing only on the assets, components, or practices that “survived” operational stresses while ignoring the lessons from those that failed or even the risks inherent in those that don’t even get ‘tested’ due to infrequent events. These issues can lead to misleading conclusions, flawed strategies, and increased risk.
Survivorship bias occurs when we concentrate on the entities that made it through a process and mistakenly assume their success tells the whole story.
A classic example comes from World War II: analysts looked at damage on returning aircraft to reinforce the most hit areas, ignoring the fact that planes shot down (and thus unavailable for inspection) likely revealed the actual critical areas that needed reinforcement.
In spare parts management, this bias manifests when companies optimize inventory based only on frequently replaced or readily available parts, neglecting rare or critical failures that led to downtime or catastrophic losses.
- Overemphasis on Common Failures
- Ignoring Infrequent but Critical Failures
- Misreading “Success Stories”
It’s natural to focus on parts that are frequently requested or replaced. These parts are well-documented, and their demand patterns are clear. The problem is that this can result in high ‘fill rate’ values (say, 98%) and with that a false sense of security in how the management system is working. This, in turn, can lead to neglecting low-frequency, high-impact components – those that fail rarely but can cripple operations when they do.
Parts associated with one-off failures or past disasters often fade from memory, especially if those failures led to asset retirement or were managed ad hoc. Since these parts don’t generate a recurring demand signal, they’re often excluded from standard inventory models – until another failure brings operations to a halt.
Observing operations that have run smoothly and assuming their inventory strategies are universally applicable ignores the luck or unique conditions that allowed them to succeed. Copying the spare parts approach of another facility without understanding its context can also lead to vulnerability.
Here’s an example. A refinery may, for instance, track and stock high-turnover items like seals, gaskets, or filters. But what about the specialized control module for a legacy compressor that failed once five years ago and took eight weeks to replace? That failure may not appear in trend analyses, and the event may not even be known to the current management team, but its impact was far greater than a hundred gasket replacements.
Without accounting for such “silent signals”—the missing aircraft in our WWII analogy—companies fall into a trap of false optimization, where inventory appears lean and efficient on paper but is functionally brittle under real stress.
To counteract this bias in spare parts management, organizations should:
- Analyze Failures, Not Just Replacements: Build failure databases that include all past part failures, especially catastrophic or infrequent ones. Understand what was needed but unavailable. The key to managing spare parts that exist to support uptime is to understand the failure modes that may lead to the demand for the spare part.
- Learn from Decommissioned Assets: One idea is to study retired equipment or scrapped assets to understand what parts were prone to failure and which were hard to procure.
- Include Expert and Tribal Knowledge: Understanding failure modes requires the engagement of maintenance engineers and technicians who understand the technical side of the operating equipment and who remember “that one time we didn’t have X part.” This anecdotal memory fills the gaps left by data.
- Risk-Based Inventory Modeling: Classify parts not by frequency of use but by their criticality to operations. Stocking a rarely-used $20 relay might prevent a million-dollar shutdown.
- Simulate Failure Scenarios: Run simulations or reviews to identify parts that would halt operations if they failed, regardless of past replacement frequency.
Survivorship bias is a hidden trap in spare part inventory management, leading to a false sense of security based on visible, frequent events while neglecting the invisible, infrequent but often catastrophic ones.
A truly resilient inventory strategy must look beyond the “survivors” and seek lessons in what failed, was forgotten, or never made it into the data. Only by doing so can organizations ensure true operational readiness—not just apparent efficiency.
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Posted by Phillip Slater