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Algorithmic route optimization improves revenue for a logistics company

A new operating model reduces vehicle usage, route times, distance traveled, and related costs.

OPTIMIZATION IMPACT

3.6%

reduction

in line haul network cost

.8%

reduction

in transit time

16%

profit increase

without compromising quality

Challenge

An Asian logistics company struggled to match fleet supply and routing with customer demands for several reasons:

  • an increased need for trucks to pick up cargo from different locations and deliver it to hubs
  • restricted use times for trucks because of regulatory changes
  • changes in customer expectations regarding delivery times

Together, these forces made planning complex and difficult. The client sought an end-to-end solution to improve operations during pickup and delivery to secure top- and bottom-line improvements.

Discovery

The global McKinsey team included network-optimization specialists, statisticians, digital architects, interface designers, and app designers. They collected data from multiple sources across the company’s operations, documenting customer locations, available fleet resources, and existing and potential hub locations to determine improvement opportunities at each point in the process. With this information, they customized and tested the route-optimization model to generate schedules for each vehicle on a daily basis. This helped manage factors such as travel times, utilization costs for different types of trucks, and maximum load-outs.

Combining the Network Optimization Algorithm (NOAH) model with the expertise of McKinsey’s Strategic Network Analytic Center (SNAC) helped in creating visual guides. These guides brought opportunities to life for the client, particularly for its drivers in the form of daily route maps.

In addition, the team created a customized digital solution that ran on handheld devices, feeding real-time data to dispatchers and drivers alike. This helped increase transparency and further improve operations.

Impact

The client sought an end-to-end solution to improve operations across pick-up and delivery to secure top and bottom line improvements. Already experiencing a 60 percent compound annual growth rate (CAGR), the logistics company reduced costs by 3.6 percent in what is traditionally a low-margin business. It also realized efficiencies in its line-haul network. This improved profit by about 16 percent—with no compromise to quality.