Spare Parts Rationalization Helped Reduce Inventory Costs


The client’s operations were spread across different geographies and IT systems. It resulted in duplicate information across systems, sub-optimal inventory positions, and escalating inventory costs. Sancus helped harmonize the data from different systems and create an AI/ML-based layer of demand forecasting and machine failure prediction that helped reduce inventory cost.


  • De-duplication of parts data using Sancus:
    • Identify parts with different names that are the same/similar in terms of description and usage
    • BoM intervention – by redesigning BoM to minimize parts (principles of concurrent engineering)
  • Data enrichment – using supplier/manufacturing/other databases to provide holistic information about items, thus enabling Spare parts consolidation/rationalization
  • Category wise Demand forecasting of spare parts
  • Prediction of Machine failures to optimize spare part inventory levels and minimize downtime
  • Integration with existing IT systems for seamless adoption

Key Benefits

  • Wholistic view of spare parts inventory from 41 plants across the globe, recorded in multiple languages
  • Enabled utilization of spares across plants


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Savings to the tune of $1.2 M in inventory costs

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