AI-Driven Parts Mapping Boosts Aftermarket Revenue
Cross-referenced 60,000+ parts to win over competitors.
Overview
One of the world's leading OEM manufacturers wanted to expand its aftermarket footprint. The challenge? Their field teams had no visibility into part compatibility — especially when servicing competitor equipment. Manual research was time-consuming, and new part demand was going unseen.
The Challenge
Field technicians couldn't offer replacements for competitor parts, leading to missed sales opportunities.
Aftermarket opportunities were consistently lost due to inaccessible or unstructured data across multiple systems and catalogs.
Engineering and product teams lacked a scalable way to map or validate cross-compatibility between their parts and competitor offerings.
Manual research processes were time-consuming and error-prone, making it impossible to scale the aftermarket business effectively.
The Solution
AI-Powered Cross-Referencing
Built an AI-powered engine to cross-reference over 60,000 parts across multiple manufacturers and systems, creating a comprehensive compatibility database.
Data Extraction & Normalization
Extracted and normalized attribute data from internal and competitor catalogs, creating a standardized taxonomy for accurate matching.
Real-Time Compatibility Mapping
Mapped and validated alternate parts, exposing compatibility in real time through APIs that connected to field service applications.
Searchable Interface
Deployed insights through an intuitive searchable interface for field, sales, and service teams, enabling instant part compatibility lookups.
AI-Driven Part Mapping System
AI-driven matching algorithms
Multi-attribute compatibility scoring
Real-time field service integration
Continuous learning from feedback
The Impact
Doubled visibility into addressable aftermarket opportunities
Unlocked new revenue streams without new manufacturing
Field teams could confidently suggest replacement parts instantly
Enabled engineering to prioritize high-demand compatible parts
Why It Worked
Unlike traditional MDM or catalog tools, the AI-driven approach focused on functional equivalence, contextual matching, and multi-system normalization — making it scalable, intelligent, and constantly improvable.
Unlock Efficiencies and Uncover Revenue Growth Opportunities
From kickoff to go-live, experience white-glove implementation—whether by our team or expert partners.