Why Industrial OEMs are sitting on valuable data they cannot use
A maintenance technician at a food plant has a packaging machine down. A belt tensioner is in pieces on the floor, and the technician has a photo of the machine’s serial plate on a phone. What he needs is a part number. What should be simple spare parts search quickly becomes a time-consuming process.
That part number already exists. It was printed in a 340-page parts manual in 2011, on page 212, beside callout 14 in an exploded view of the drive assembly. The manual is stored somewhere in the dealer’s shared drive. Another copy may be in an email inbox or on a USB stick in a service van. To find the part, someone at the parts desk must open the PDF, locate the relevant assembly, confirm the serial range, identify the callout, and send the answer back. The process can take 40 minutes. If the wrong manual revision is selected, the wrong part may be shipped, and the production line could remain stopped for several more days.
This is not an isolated problem. It is a common symptom of the industrial aftersales data challenges: the information exists, but it is not structured for the way customers, dealers, and service teams need to use it.
The aftermarket data “junkyard”
Industrial OEMs have accumulated decades of valuable product information, yet spare parts searches remain unnecessarily complex and time consuming. Parts manuals, engineering drawings, CAD files, ERP records, service notes, and technical publications all contain information needed to identify and sell replacement parts. However, that information is often spread across disconnected systems and stored in inconsistent formats. A typical industrial OEM may have:
- 3D models distributed across several CAD platforms such as Pro/E, SolidWorks and Inventor, or even a CAD tool the vendor stopped supporting years ago
- Scanned PDF catalogs created decades ago
- Different part numbers for the same component in different regions
- Serial number rules documented only in older manuals
- Experienced employees who know critical fitment relationships from memory
- ERP records containing part numbers and prices, but no images
Each file and system may have been created for a valid reason. The problem is that the information was not designed to work together. A dealer may need an answer in less than a minute, while the business process still depends on manually combining information from several sources.
Why the delay matters
The cost of a slow parts search extends beyond the time spent on the phone. Industrial equipment may be sold once, but replacement parts and services can generate revenue for twenty years or more. When customers can quickly identify and order the correct part from the original manufacturer, they are more likely to remain loyal to that brand. When the process requires repeated calls, long waits, or manual clarification, customers may turn to third-party suppliers instead. The result can include:
- Longer machine downtime
- More pressure on parts and service teams
- Increased risk of incorrect shipments
- Lost aftermarket revenue
- Greater dependence on individual employees
- A weaker customer experience
What AI can do with aftersales data
AI is often presented as a universal solution for industrial data. In practice, the most valuable applications are usually more focused. AI can help read complex documents and convert their contents into structured information that can be used by search tools, quoting systems, commerce platforms, and customer service teams. For aftermarket operations, the benefits appear in four main areas.
- Extracting information from older documents: Document extraction tools can identify part numbers, descriptions, quantities, specifications, and fitment relationships from PDFs and scanned drawings. Instead of treating a manual as a collection of pages, the system can identify the relationships between: machines, models, serial number ranges, assemblies, exploded-view drawings, callouts, parts tables, and part numbers. This creates structured data without requiring the entire document library to be manually recreated first.
- Supporting natural-language searches: Customers do not always know the formal catalog name for a part. A technician may search for “the gasket for the rear door on the large washer” rather than entering an exact part number. When product information has been structured correctly, search can connect the customer’s description to the relevant catalog record. This makes the system more useful for dealers, technicians, and customers who know the equipment but not the company’s internal terminology.
- Identifying related parts: Parts are often purchased or replaced together. For example, a pump seal may commonly be replaced with an impeller. An experienced applications engineer may know this immediately, but that knowledge is difficult to scale if it remains in someone’s memory.
- Preserving employee knowledge: Industrial aftersales teams often depend on employees with decades of experience. These employees may know which pump fits a particular frame or which revision applies after a certain serial number. Document analysis and structured interviews can help capture this knowledge before it is lost through retirement or employee turnover.
AI still needs structure and oversight
A general-purpose chatbot cannot safely solve the aftersales data problem by simply being connected to a folder full of CAD files and PDFs. Without a controlled process, the system may produce incorrect answers with confidence. That is especially risky when a mistake could result in an incorrect part shipment, equipment damage, or a safety issue. A more reliable approach includes three components:
- A disciplined extraction pipeline
- Human review for safety-critical and fitment-related information
- Integration with tools employees and customers already use
The result should flow into systems such as:
- Quoting tools
- CRM platforms
- Dealer portals
- E-commerce systems
- Customer service applications
- Parts catalogs
A new AI interface is unlikely to deliver value if it exists outside the workflows people already follow.
Why the parts manual is the best place to start
Among an OEM’s various information sources, the parts manual often provides the most comprehensive view of what a customer purchased. A useful parts manual connects four essential elements:
- the machine model and the serial number
- the assembly
- the exploded-view drawing
- the parts list, including quantities
An ERP system may contain the part number and price, but the manual usually shows where the part fits, which machine it belongs to, and what it looks like.
The challenge is that a PDF is designed for human viewing, not necessarily for software interpretation. A person can look at a page and immediately understand that a drawing belongs to a table below it. Software must determine:
- Which drawing belongs to which parts list
- Which callout corresponds to which table row
- Which assembly belongs to which section
- Which serial numbers apply to the content
- Whether the document represents the correct revision
This is where document extraction and catalog platforms such as CDS Partable® can help.
From documents to searchable catalogs
CDS Partable® processes manuals by identifying images, parts tables, hotspots, serial numbers, and assembly relationships. It then uses those elements to build a structured catalog. The amount of human effort required depends on the quality and structure of the source PDF. Some manuals can move from upload to catalog creation with no manual intervention. Others need a few corrections. Some documents are too incomplete or poor quality to process reliably without better source files.
Classifying manuals before beginning a migration helps OEMs avoid spending weeks treating every document as if it requires the same level of work.
A practical starting point
OEMs do not need to begin with a multi-year data transformation program. A more practical approach is to select twenty manuals from different product lines and decades. Review them for:
- Clearly identified model and serial information
- A usable table of contents
- Named assemblies
- Consistent drawings and parts tables
- Readable callouts
- Clean, machine-readable pages
The strongest manuals can be loaded first to deliver early value and create usage data. More complex manuals can then be cleaned up in stages, while documents with missing or unreadable source content can be sent back for better files. The goal is not to clean every document before launching a catalog. The goal is not to clean every document before launching a catalog. The goal is to identify where value can be delivered quickly and build from there.
The opportunity in the aftermarket
Industrial OEMs already possess much of the information needed to improve spare parts identification and ordering. The challenge is making that information accessible, accurate, and connected to the customer experience. A forty-minute parts search can become a self-service interaction. A retiring employee’s knowledge can become structured catalog data. A static PDF can become an interactive parts experience. The aftermarket opportunity is not hidden because the information is unavailable. It is hidden because the information has not yet been organized for use.
In the next article, we will examine the six characteristics of a catalog-ready parts manual, the four most common PDF problems, and how OEMs can prioritize their document libraries.
Want to know how much of your parts library is catalog-ready? Contact us to set up a review of a sample of your manuals.
Back to Blog
