Why Parts Catalogs in Heavy-Duty Are Broken
This is Part 1 of a two-part series on why parts management and dirty catalogs remains one of the most underestimated sources of operational costs in the industry. This piece examines how catalog data became so compromised, why it has stayed that way, and why it matters far beyond the moment a technician orders or pulls a part off the shelf.
The Catalog Problem Nobody Treats as a System Failure
Ask any fleet maintenance director or shop owner about their parts catalog and you will hear a familiar answer: “It’s okay." Long pause. "We’ve gotten it to a decent place.” That answer rarely holds up under a few follow-up questions.
You start to hear about placeholder codes that never got resolved, duplicate entries that require constant workarounds, cross-references that lead nowhere, and descriptions that could apply to multiple components. You hear about manual clean-up that has quietly become part of the daily workflow, cycle-counts without which the shop or fleet could not function. You hear about fleets with no shops, waiting days or weeks for a repair at a provider, only for the wrong part to arrive.
You hear a more telling point from parts managers themselves; a significant portion of their day is not spent managing inventory or improving availability, but reshuffling parts across repair orders, correcting mistakes, and working around gaps in the catalog. Many believe they could double their productivity if that layer of friction did not exist.
The industry shorthand for this is familiar to anyone who has worked in a parts department: XXXX. It appears when a supplier does not have a clean manufacturer, description, a confirmed part number, or a validated cross-reference. In a controlled system, XXXX would signal an incomplete record that must be resolved before it is used. In practice, it has become embedded in production catalogs and thousands of entries exist with unresolved data and remain in circulation basically forever.
This is a system failure, although is masquerades as a nuisance. Parts catalogs are not just reference tools, they are decision systems that determine what gets ordered, what gets installed, and how a repair is recorded. When the underlying data is ambiguous, incorrect, or flooded with XXXX's every downstream decision becomes harder and less reliable.
How Bad Data Gets Created
The sources of catalog degradation are structural and relatively easy to trace. Heavy-duty parts data spans dozens of OEMs, hundreds of suppliers, and hundreds of thousands of SKUs that change across model years, configurations, and aftermarket substitutions. Every supersession, spec change, or new supplier introduction requires the catalog to update. In practice, many of these updates are partial or delayed.
The teams responsible for maintaining this data are typically embedded within supplier or distributor operations and are not measured on long-term data quality. Their work is invisible when it is correct and only surfaces when something fails downstream. When there are competing priorities such as order fulfillment or sales targets, catalog maintenance is deprioritized.
As a result, data quality issues accumulate rather than resolve. Outdated part numbers remain active long after they should be retired. Fitment data is incomplete or tied to VIN ranges that were never validated. Descriptions are reused across similar parts without verification. When new suppliers are onboarded, their data is often ingested as-is because the alternative is a large-scale clean up effort that no one has the time or incentive to undertake. Over time, the catalog becomes a layered hodgepodge of partially correct information, usable enough to operate, but unreliable enough to create consistent friction and drive up costs and downtime.
The Scale of the Problem Is Underestimated
Internal audits across fleet and shop systems routinely show that up to 15% of active part records contain degraded data, whether that is missing attributes, invalid cross-references, or unresolved placeholders. In large catalogs, that translates to tens of thousands of records that cannot be trusted in a time-sensitive repair context.
This is not confined to obscure parts. The gaps frequently show up in high-frequency categories such as filters, belts, brake components, lighting, and electrical systems. These are the parts technicians rely on every day, often under time pressure, and they are also the parts with the most complex fitment requirements.
A useful way to think about this is not as a data quality percentage, but as a decision failure rate. If one out of every ten parts lookups returns ambiguous or incomplete information, then one out of every ten repair decisions is being made on degraded inputs. At scale, this touches nearly every vehicle in a fleet over the course of normal operations.
What Happens When a Truck Goes Down
A Class 8 truck arrives with an intermittent power issue. Diagnostics point to a likely component. The technician searches the catalog and finds multiple possible part numbers for the engine configuration. Two have no application data, one appears available but does not clearly match the component being inspected.
At this point, the technician is not selecting a part so much as making a judgment call. The most plausible option is ordered. It arrives the next day and turns out to be incorrect for that specific configuration. The part is returned, a second order is placed, and the truck remains down for several additional days.
The direct cost shows up as downtime, additional labor, and parts handling. The indirect cost is more subtle but more important. The repair record now reflects an attempted fix that did not resolve the issue. If that record is not carefully corrected, it becomes part of the vehicle’s maintenance history. Over time, these inaccuracies accumulate and begin to distort failure patterns, warranty claims, and inventory decisions.
What starts as a catalog ambiguity does not stop at a delayed repair. It flows into the data that fleets rely on to make operational and financial decisions.
Why the Problem Persists
The reason this has not been fixed is not that the data does not exist but that ownership and incentives are misaligned.
OEMs produce the original data but do not control how it is maintained downstream. Distributors aggregate and distribute catalogs but often inherit issues from multiple sources. Data providers sit in between but rely on inputs that are already inconsistent. Each party can point to another when issues arise.
The cost of failure is also distributed. Fleets absorb it as increased downtime and maintenance spend. Shops absorb it as margin erosion on individual repair orders. There is no explicit line item for “catalog failure,” which makes the impact difficult to isolate and act on.
At the same time, the cost of fixing the problem is concentrated. Cleaning and maintaining a high-quality catalog requires ongoing effort, clear ownership, and feedback loops that most organizations are not set up to support. Without a way to directly connect bad data to measurable operational loss, the status quo remains acceptable.
Not Solved by Better Catalogs Alone
The industry has historically approached this as a catalog quality problem, which implies that the solution is a cleaner static dataset, however this framing is incomplete.
Parts data is not static. It changes continuously as vehicles evolve, suppliers update components, and new edge cases appear in the field. Any system that relies on periodic updates or one-time cleansing efforts will degrade again over time. Strong fleet and shop operators already know this, but in the absence of systemic solutions, keep focusing on the one-time fixes.
What is missing is a continuous feedback loop between the point of failure and the source of truth. Every incorrect part order, every return, and every technician correction is a signal that the catalog is wrong or incomplete. Today, that signal is rarely captured in a structured way and almost never fed back into the system at scale.
Until catalogs are treated as living systems that learn from real-world usage, rather than static references that are periodically updated, the same errors will continue to repeat.
Garbage Out: What Comes Next
The catalog is where the problem begins and not where it ends. In Part 2, we examine what happens after bad catalog data enters the purchase order workflow. Once an incorrect or ambiguous record is used in a transaction, it does not stay isolated but seeps across inventory, purchasing, and financial systems, compounding cost and creating new errors with each step.
Axle Mobility is the system of execution for fleet repair and maintenance, so techs, fleet managers and fleet executives can focus on rolling trucks and making money, not mindless admin.