25% reduction in dead miles through Machine Learning powered Route Planning and Carrier Allocation System for a US-based 3PL

About the client

A full-service 3PL company specializing in the transportation of agricultural products across the US

Problems:

Our client wanted to determine the most effective ways of picking up and delivering freight from customers to other customers, while efficiently allocating orders between one-week and two-week rounds/trips. This included:

• Understanding the load distribution across regions in US and Canada – Demand clustering in freight logistics networks
• Estimating the revenue, cost, and profitability of lanes/transportation routes by accounting seasonality of demand
• Leveraging potential economies, optimizing operations, and developing optimal carrier assignment strategies

Solution:

Innover developed a Machine Learning powered efficient route planning and carrier allocation system focused on minimizing costs and augmenting profit for daily/weekly/monthly planning horizons. This included:

• Combining structured and unstructured customer data from various data sources - Transport Management System (TMS), Financial Systems, Bing maps etc
• Pareto and Cluster Analysis to identify most important routes for the client and categorize cities that attract most demand
• An intelligent carrier allocation system driven by expected demand, threshold miles, driver availability & work-life balance constraints