Every technician knows the frustration: you need that one part to get a machine running again, but no one knows exactly what it is, where it’s stored, or even what it’s called. Before you know it, the entire production line is down because someone once ordered something under the name “thingy for the pump.”

In an era where everyone is talking about smart factories, AI, and predictive maintenance, it’s striking how often spare parts management is still a mess. Fortunately, there’s a path to order: spare parts contracts. Not glamorous, but absolutely essential. In this blog, you’ll discover why they make the difference between chaos and control and how they can act as the WD-40 that keeps your maintenance process running smoothly.

Uncoded Spend Creates Chaos in the Chain

When parts are ordered without item numbers or standardised descriptions, it creates chaos, not just in the warehouse and maintenance process, but also in procurement. Operational procurement wastes valuable time searching for the right parts and requesting quotes. This is time lost in the maintenance process.
On a tactical level, tracking contract consumption and leveraging purchasing power becomes nearly impossible. The result? A supply chain that stalls, lacks optimisation and cannot continuously improve.

AI as the Key to Recognisable Spend for Tenders

To establish a spare parts contract, you need a clear understanding of what materials you require from the market. Many organisations base tenders on historical spend, only to find that a significant portion of it remains uncoded. Think of vague descriptions like “filter,” “various parts,” or “repair kit,” with no further specification. With the help of Artificial Intelligence (AI), especially Natural Language Processing (NLP), these vague descriptions can be automatically analysed, categorised, and linked to recognisable product groups or item codes. This enables you to:

  • Cluster spend by function, application, or supplier
  • Identify duplicates
  • Base tender documents on actual needs instead of raw data

AI thus makes it possible to include “hidden” spend in strategic decision-making, bridging the gap between historical chaos and future-proof procurement.

Flexibility in Spare Parts Contracts Is Crucial

While visibility into spare parts demand is essential—and AI can help significantly—there’s more to a good spare parts contract than just competitive pricing. Consider:

  • Service Level Agreements (SLAs): delivery times, availability guarantees
  • Logistics value-added services, such as:
    • Vendor Managed Inventory (VMI)
    • Consignment stock
    • Warehousing near assets
    • Return logistics for defective parts or packaging
    • Digital integration with ERP systems

Spare parts are often characterised by unpredictable demand and high obsolescence. That’s why contracts must not be too rigid. A strong contract includes:

  • Flexible clauses for adding or reclassifying parts
  • Mechanisms for periodic evaluation of data quality and delivery performance
  • Agreements on joint data enrichment and issue resolution
  • Options to mitigate obsolescence risks

This allows the contract to evolve alongside the realities of the maintenance process and new insights.

Spare Parts Contracts as a Driver for Master Data Completion

Spare parts contracts also offer a unique opportunity to actively involve suppliers in codifying items and completing article data, such as descriptions and manufacturer part numbers. Suppliers are often reluctant to invest time and resources in data enrichment without formal agreements. But with a contract in place, explicitly emphasising collaboration and data quality, they’re far more willing to contribute.

This not only strengthens the supplier relationship but also improves data reliability across the entire maintenance process.

Conclusion

Uncoded spend is a silent cost driver and a risk to operational continuity. A flexible spare parts contract, powered by AI-driven spend analysis, is the key to a maintenance process that is reliable, efficient, and future-ready.

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Rutger Vlasblom