Service Transformation & After-Sales Analytics
85% reduction in service TAT for a 2-Wheeler Automotive OEM.
Service OperationsPython AnalyticsSpare PartsAutomotiveSARIMAForecasting
About the Project
This engagement addressed a critical service crisis at a 2-Wheeler Automotive OEM where service Turn-Around Time (TAT) had ballooned to 45 days with 5,000+ pending vehicles, causing massive brand erosion.
We led a 3-month "war-room" transformation initiative to completely overhaul service operations. The approach combined Python-based analytics for issue prioritization with optimization of the spare parts distribution network to eliminate bottlenecks.
The team developed custom SARIMA/VARMAX forecasting models for 1,000+ automotive SKUs, achieving 8% accuracy uplift in SKU-level forecasting. This enabled better inventory planning and reduced stockouts across the spare parts network.
Key Features
War-room transformation approach over 3 months
Python-based analytics for issue prioritization
Spare parts distribution network optimization
SARIMA/VARMAX forecasting for 1,000+ SKUs
8% accuracy uplift in SKU-level forecasting
Inventory planning and stockout reduction
Challenges & Learnings
Addressing 5,000+ vehicle backlog causing brand erosion
Compressing 45-day TAT to acceptable levels
Optimizing complex spare parts distribution network