Tackling Obsolete Parts in Heavy Machinery with AI

Industrial OEMs face a persistent and increasingly critical challenge: managing part obsolescence within long-life assets. Equipment such as mining rigs, industrial presses, cranes, and turbines are engineered for operational lifespans exceeding 30 years. However, the electronic and control components embedded in these systems—motor drives, PLCs, HMIs, circuit breakers, and I/O modules—typically have support lifecycles of less than five years. 

This creates a severe lifecycle mismatch, where critical parts become obsolete long before the end-of-life of the host machine. The consequences are large: 

• Annual Component Discontinuation: Thousands of industrial components are phased out each year, rendering legacy replacements harder to obtain. 
• Accelerating Obsolescence Risk: Once a part is declared end-of-life,- its supply chain shrinks rapidly. Industry analysts warn that it’s only a matter of time before replacement options disappear entirely. 
• Global Supply Chain Constraints: Ongoing disruptions - worsened by semiconductor shortages - have intensified sourcing challenges for legacy parts. 
• Operational and Financial Fallout: In the absence of compatible components, OEMs must choose between: 
  • Absorbing the cost of system redesigns and retrofits, or 
  • Risking unplanned downtime and associated production losses. 

To remain resilient, OEMs must adopt initiative-taking lifecycle intelligence, use real-time parts traceability, and build strategic sourcing ecosystems that mitigate the risks associated with part obsolescence.  

Managing Vast Parts Catalogs and SKU Complexity 

1. High SKU Volume: 

Each heavy-duty machine includes hundreds of unique spare parts. OEMs managing multiple models often manage thousands of SKUs, creating a vast and intricate catalog. 

2. Data Inconsistency: 

Part descriptions and numbers vary across: 

• Production eras 
• Geographic regions 
• Equipment configurations 

3. Fragmented Information Sources: 

Critical parts data is often: 

• Locked in static PDFs. 

• Scattered across disconnected systems. 
• Hard to access in real-time. 

4. Manual Tracking Still Prevalent: 
Despite digital advancements, 52% of maintenance teams still rely on manual spreadsheets for parts and repair tracking. 

5. Common Issues in Manual Systems: 

• Inconsistent naming conventions 
• Duplicate part entries 
• Missing or outdated cross-references 
• Difficulty finding replacement parts. 

6. Technician Time Drain: 

Technicians can spend hours searching legacy documents, trying various search terms and SKU variants to find the correct part. 

7. Scalability Challenges: 

As inventories grow more complex, manual processes become unsustainable, risking delays, inefficiencies, and service errors.  

The Hidden Costs of Downtime and Delays  

A Gen AI-Powered Parts Management Platform 

The solution lies in automating and accelerating the parts lookup workflow with Generative AI. A Gen-AI-powered parts management platform ingests an OEM’s entire spare parts database (manuals, drawings, SKU tables), along with competitor catalogs and aftermarket data, and acts as an intelligent search assistant. Its capabilities include: 

• Interpreting incomplete requests: The AI can manage vague or partial inputs – even a SKU number, or hand-drawn diagram or a brief symptom description  – and still pinpoint the correct part. 
• Cross-referencing catalogs: The platform simultaneously searches internal SKUs and known equivalents from other brands. It matches competitor part numbers and alternative sources automatically, doing in seconds what once took manual cross-database queries. 
• Automated compatibility analysis: When a part is selected, the system verifies fit and configuration rules (e.g., voltage, mounting, interface) behind the scenes. This “smart” check dramatically reduces mismatches – one solution report notes AI “dramatically reduces errors in part selection” – minimizing the need for costly reorders or fitment issues. 
• Continuous learning: Over time, the AI refines its knowledge from every service case. Its part suggestions improve as it “learns” from confirmed repairs and inventory outcomes, making each later search faster and more accurate.  

Manual vs AI-Enabled Parts Workflow Comparison 

The impact is best seen side-by-side. The table below contrasts a typical manual replacement process with an AI-accelerated workflow: 

Each row reflects documented improvements. For example, a Gen-AI advisor claims it can cut lookup time from hours to seconds, increase technician throughput by 35%, and reduce parts errors that trigger repeat jobs. In aggregate, this means far fewer unplanned stops and faster repairs.  

Quantifying the Benefits 

For example, consider a mining OEM with 500 service calls per year. If AI cuts the average part search from 4 hours to 1 hour, the team saves 1500 technician-hours. If each avoided downtime hour saves $260K in lost output, even preventing 5 extra stoppages per year yields millions back to the business. These kinds of calculations can be compelling when presented to stakeholders.  

The Right Solution: Mastek Intelligent Parts Management 

By offering real-time access to both new and legacy components and integrating seamlessly with predictive maintenance systems,- the platform helps minimize downtime, reduce costs, and keep operations running at peak efficiency. As Mastek puts it, it’s about “slashing downtime, cutting costs, and keeping your operations at peak performance.”  

Call to Action: If your organization is grappling with the complexities of obsolete parts, now is the time to explore AI-powered parts search solutions. Platforms like Mastek’s are transforming parts management into a strategic advantage. We encourage OEMs to consider a pilot or demo—empower your service teams with intelligent search capabilities and turn legacy parts challenges into a competitive edge.