Right-Sizing Spare-Parts Inventory Across 30 Service Locations
Location-wise AI forecasting across roughly 10,000 spare-part SKUs balanced excess inventory against shortages, location by location.
ITChamps built an AI-enabled location-wise spare-parts demand forecasting and inventory planning solution for a medical equipment manufacturer servicing approximately 30 locations with around 10,000 unique spare-part SKUs.
The Challenge
One-size-fits-all stocking couldn't match location-by-location demand
Managing spare-parts inventory across multiple service locations was challenging because demand varied significantly by location, installed base, and historical service requirements, creating excess inventory at some sites and shortages at others.
- Spare-parts demand varied significantly across roughly 30 service locations.
- Excess spare-parts inventory built up where demand was lower than anticipated.
- Shortages occurred at locations with unexpected service requirements.
- The team needed location-wise inventory planning while maintaining service levels.
The Solution
Forecast spare-parts demand location by location
The solution consolidated historical consumption and service data across ~10,000 SKUs and ~30 locations, analyzed location-wise demand patterns, and produced AI-based, location-specific spare-parts forecasts and inventory recommendations.
Consolidated historical consumption and service data for approximately 10,000 SKUs.
Analyzed spare-parts requirements independently across approximately 30 service locations.
Analyzed historical consumption patterns, replacement frequency, and service requirements.
Engineered equipment, location, service, and consumption features for forecasting.
Forecast the likely spare-parts requirement for each location.
Converted forecasts into recommended inventory levels, identifying where stock could be reduced without risking availability.
Results
Measured business impact
Unique SKUs included in the forecasting model.
Locations planned individually rather than uniformly.
Reduced through location-wise optimization.
Improved Service Level Agreement compliance.
Highlights
- Moved from a largely historical/rule-based stocking approach to forecast-driven planning.
- Improved working-capital utilization by right-sizing inventory per location.
- Improved availability of required spare parts and SLA compliance.
- Reduced risk of service delays caused by spare-parts shortages.
Services and technology
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