US based consulting firm
Rate discovery analysis for Supply Chain Shippers
Problem
The client required a method to analyze the freight market and benchmark freight rates, enabling shippers and carriers to optimize pricing strategies.
Solution
A linear regression model was implemented to estimate freight rates, which were compared to actual rates to establish a benchmark. An LP model for lane matching was also introduced to identify and reduce dead miles during transactions.
Outcome
Lane-by-lane cost prediction allowed the client to assess shipper performance and use the insights for contract negotiations. Lane matching fostered collaboration, reducing dead miles and improving cost savings.
Optimizing Freight Pricing with Data-Driven Insights
The client needed a solution to analyze freight rates and optimize pricing for shippers and carriers. The dataset was cleansed and validated to remove outliers before being transferred to SPSS for regression analysis at a 95% confidence interval. A linear regression model estimated freight rates, establishing a benchmark, while an LP model for lane matching identified dead miles and revealed opportunities for collaboration with non-competitive shippers to reduce inefficiencies.
The analysis revealed that distance was the most significant factor influencing overall costs, followed by fuel and geography. Lane-by-lane cost predictions provided the client with insights into shipper performance for each transaction, which were used as benchmarks in contract negotiations. The lane matching model encouraged shipper collaboration, leading to reduced dead miles and improved cost savings for the client.
Distance, Dead Miles, Big Savings
Explore All
Discover our complete range of high-quality products designed to meet your needs. Explore now and find the perfect fit for your requirements.