Industrial Equipment Industry, Aftermarket & Service Business
Aftermarket Data: Why OEMs Lose Value Between the Data They Own and the Actions They Take
The Aftermarket Is Where the Margin Is
For industrial OEMs, aftermarket services generate around 40 percent of revenue over an equipment’s useful life, yet roughly 55 percent of gross profit and 62 percent of EBIT. Margins run about twice those of new equipment, and OEMs with a stronger aftermarket focus have delivered roughly twice the total shareholder return of less focused peers.
Capital is following. Around 81 percent of OEMs expect to increase aftermarket investment over the next five years, and about one in five plans to raise it by more than 15 percent. The question is no longer whether to invest in aftermarket, but whether that investment will reach the places where value is actually lost.
Data-Rich Is Not the Same as Insight-Rich
Industrial OEMs sit on one of the richest data assets of any sector. Manufacturing generates around twice as much data as banking, the next most data-rich industry, and a single modern production line can produce about 2,200 terabytes a month. Yet only 28 percent of OEMs derive insight from the data their equipment, processes and systems generate, and as much as 90 percent of manufacturing data goes unused.
The cost of that gap is concrete. Teams working with clean installed base data can lift aftermarket penetration by around 20 percentage points from a typical baseline of 25 to 50 percent. But roughly 90 percent of OEMs report problems with their installed base data.
Tellingly, “too little data” does not appear among the top challenges OEMs cite. Poor data quality, siloed systems, limited integration with dealers and suppliers, accessibility, skills and real-time visibility do. Every one of them sits in the layers that convert raw data into action: capture, integration and insight.
Two Use Cases Hold Most of the Prize, and They Break in Different Places
If OEMs’ data assets were fully utilised, the potential value across the industry is estimated at around €105 billion. Two application domains account for about three quarters of it: asset optimisation and products tracking, each worth roughly €40 billion.
They do not fail in the same way. Asset optimisation breaks at the capture and insight stages: the right data never gets recorded, or the insight arrives in a form no one can act on. Products tracking breaks at integration: the data exists, but in systems that cannot talk to each other. One fix does not fit both.
Asset Optimisation: Lost at Capture, Stalled at Insight
At the capture stage, the most commercially valuable field knowledge rarely reaches the data system. A technician’s diagnostic reasoning is reduced to a single code from a fixed list. Repair photos and scanned invoices are attached as evidence but never parsed. Informal escalations between dealers and OEM engineers leave no record at all. Meanwhile, around 76 percent of equipment fleets still rely on manual logs, introducing inconsistencies in accuracy, timeliness and reliability before the data ever reaches the OEM.
At the insight stage, collecting more data does not solve the problem. Models struggle when sensors do not share a common language, when edge devices cannot run lab-scale models, when a model trained in one setting fails in another, and when rare failures lead to overfitting. Even good analysis often arrives after the window to act has closed: 60 to 70 percent of dashboards sit idle, and analytics teams spend 40 to 60 percent of their time handling follow-up requests. Insight needs to be live, role-specific and prescriptive, not scheduled, generic and descriptive.
Products Tracking: The Data Exists, but Not in One Place
Products tracking fails for a different reason. About two thirds of OEMs hold installed base data across three to four systems, such as ERP, product lifecycle management, field service and asset platforms, with no shared identity key. Each holds part of the picture, and none can say on its own which machines are running at which customer today. Around 90 percent of OEMs find it extremely time-consuming to obtain reliable figures.
Scale makes it worse. Above €250 million in revenue, installed base data accuracy typically falls below 25 percent, and records decay around three times faster than at smaller OEMs. Between 10 and 15 percent of aftermarket sales are lost to poor installed base visibility. The largest blind spot sits outside the OEM: with dealers on multiple management platforms and both trust and technical barriers to sharing, many OEMs know what they sold to their dealers, but not what their dealers sold on.
Fund the Layer Where Value Leaks
The aftermarket data opportunity is not a collection problem. It is a conversion problem. The OEMs that capture its value will be those that structure field knowledge at the source, build a single source of truth for the installed base, and deliver insight to the person who can act on it, at the moment they need it.
The board-level question to end on: as your aftermarket investment rises, will it fund more data, or the capture, integration and insight layers where the value is actually leaking?
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