Capital equipment often remains in use for decades, and for OEM manufacturers, this long lifecycle presents a lucrative opportunity: a long-term business in wear parts and spare parts. But to capitalize on this, companies need detailed knowledge of product designs that may date back 20 years or more.
And that’s where the challenge begins—where is the data?
Spare parts may not have been a priority during the original product sale decades ago. Since then, PLM systems with structured BOMs may have been introduced, CAD platforms may have changed, and data may now reside in outdated formats or buried in legacy drawings.
Too often, selling spares becomes a manual, time-consuming effort—hunting down information across disconnected systems and formats. The leap from this fragmented reality to a seamless, omnichannel self-service e-commerce experience is steep, even if it’s been featured on executive slides for years.
This is precisely where AI can be transformative.
AI can act as a powerful engine to read, convert, harmonize, and cleanse legacy data—from old drawings and BOMs embedded in assembly documents or from manually maintained Excel lists. With the right training, AI can automatically populate customer assets in the installed base with accurate, structured spares data.
Once the data is cleansed, itemized, and harmonized, it becomes e-commerce ready—unlocking true self-service ordering and turning legacy data into a modern revenue stream.