Success Story

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

Challenges

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

Solutions

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:

1.Combining structured and unstructured customer data from various data sources – Transport Management System (TMS), Financial Systems, Bing maps etc

2.Pareto and Cluster Analysis to identify most important routes for the client and categorize cities that attract most demand

3.An intelligent carrier allocation system driven by expected demand, threshold miles, driver availability & work-life balance constraints

Impact Delivered

25%

Reduction in dead miles

1.2x

Increase in profitability

12%

Increase in average weekly load delivered

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