FMCG Co Achieved 15% Cost Savings in Logistics with Freight Optimization solution
Before
One of India’s largest FMCG company was struggling with an outdated and inefficient logistics plan, leading to escalating costs and suboptimal routing across the country. The company needed a cost-optimized logistics strategy to efficiently manage pan-India freight while considering the unique challenges of India’s diverse transportation infrastructure.
Solution
Our data scientists developed a comprehensive logistics plan using advanced machine learning techniques. A multi-step approach was employed, which included:
- DB-scan clustering to identify key start and end regions for freight, optimizing routes and modes of transport.
- Tree-based gradient boosting regression to provide a detailed costing breakup, helping the company understand and manage logistics expenses better.
- Gradient boosting classification for vendor profiling, allowing the company to select the most reliable and cost-effective vendors based on past performance and risk assessments.
The final logistics plan offered detailed recommendations on category-wise transportation mode, route selection, loading patterns, and risk scores, ensuring a highly efficient and cost-effective logistics operation.
Outcome
Through the implementation of a data-driven, multi-modal logistics model, the company achieved a 15% cost savings, even after accounting for inflation. The optimized logistics plan incorporated road, rail, and water transport, leveraging historical vendor data, geolocation information, and pricing data from all 28 states in India.
