Aftersales is no longer a support function. For many OEMs, it is one of the most important drivers of margin, customer retention and long-term revenue.
But not all aftermarket operations perform equally. The difference is rarely product quality or installed base size. It is capability: how effectively an OEM can turn engineering data into something that supports fast parts identification, accurate ordering and scalable service delivery.
Most manufacturers already have the data they need. The challenge is making it work operationally.
Many aftermarket operations are still built on processes that were never designed to scale.
Parts catalogs are manually created. Documentation is slow to update. Service teams rely on engineering support to answer routine queries.
These workflows introduce friction at every stage of the aftermarket journey.
And the impact is measurable.
For example, 59% of OEMs say it takes a month or more to produce a parts catalog, limiting how quickly updates can reach customers and dealers.
As product complexity increases, these delays become harder to manage.
Information becomes outdated. Customers struggle to identify parts. Service teams spend more time supporting basic requests.
What worked in lower-volume environments starts to break down.
Modern aftermarket performance requires capabilities that remove this friction.
Parts identification is one of the most common sources of operational drag. In many OEM environments, it is still handled manually:
Customers send emails with screenshots ->
Dealers request confirmation of part numbers ->
Service teams interpret diagrams ->
Engineering teams are pulled in when things become unclear.
This creates bottlenecks and slows down the ordering process.
Modern aftersales teams replace this with self-service parts identification.
This means users can:
This capability directly reduces one of the biggest issues in the aftermarket.
Research shows that 71% of OEMs experience at least one incorrect parts order in every 50 orders, often driven by poor identification processes.
When identification becomes self-service, accuracy improves and support demand decreases.
Even when parts data exists, it is not always available in a usable format. Publishing remains a major constraint.
In many OEMs, creating or updating a parts catalog involves:
This process is time-consuming and difficult to maintain. It also introduces delays between product changes and customer-facing updates.
As a result, parts information is often:
Modern aftersales teams address this by moving toward automated publishing workflows.
Engineering data flows directly into parts catalogs. Updates happen faster. Information remains consistent. And the gap between design and service is reduced.
When publishing becomes scalable, aftermarket operations become more responsive.
In many organisations, engineering teams act as a hidden support layer for the aftermarket.
They help identify parts. They validate orders. They assist with service queries.
While this ensures accuracy, it creates a bottleneck.
Engineering time is expensive and limited. And much of this support is driven by gaps in how parts information is delivered.
The operational impact is significant. Research shows that 64% of OEMs spend at least half a day per week resolving incorrect parts orders, much of which stems from identification issues and manual processes.
Modern aftersales teams reduce this dependency by:
This allows engineering to focus on product development rather than operational support.
Each of these capabilities - self-service identification, fast publishing, and reduced engineering dependency - depends on one underlying factor.
How engineering data is structured and delivered.
Most OEMs already have the data. It exists in CAD models, BOM structures and engineering systems. But that data is typically created for design purposes, not for aftermarket use. This is where the gap appears.
Parts identification becomes manual.
Publishing becomes slow.
Support workload increases.
Modern aftermarket platforms address this by turning engineering data into a live, structured ecosystem. This is where platforms like Partful fit.
Instead of rebuilding parts catalogs manually, engineering data flows directly into an interactive environment where users can:
By transforming CAD and BOM data into interactive 3D parts catalogs, Partful enables OEMs to make parts identification self-service and publishing scalable.
The result is a shift in how aftersales operates:
Because the opportunity is not always about adding more products. It is about making existing engineering data commercially effective.