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CASE STUDY

Using Machine Learning for the Long Haul

Client:
Atlas Van Lines

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Challenge

Atlas Van Lines needed to find a way to adjust their capacity and price based on future market demands.

Result

Pariveda assisted Atlas in building a machine learning model. This model reliably predicts the demand by region, every day, for up to six weeks.

Impact

Atlas’s operations team can now quickly see which days need extra attention. This results in significant cost savings due to decreased shipment delays.

Atlas Van Lines, Inc., is comprised of more than 430 companies in the U.S. and Canada, plus 1,500 global partners in 17 countries. The company is based in Evansville, Indiana, and offers local, long-distance, and international moves.

The Challenge

Atlas needed to predict demand for long-haul moving in order to price dynamically.

During peak moving seasons, the Atlas agent network of Atlas Van Lines works together across markets to meet customer demand. However, their ability to forecast capacity had been completely manual, and labor intensive, relying on the wisdom of people with many years of experience and, admittedly, their gut instincts. Atlas possessed the historical data from 2011 forward and desired to find a way to dynamically adjust capacity and price based on future market demands.

The Result

How Pariveda helped Altas predict demand:

The Impact

The new model allows Atlas’s operations team to quickly and reliably see which days will require extra attention. This results in significant cost savings due to diminished shipment delays. Additionally, the marketing team can now see which days may require additional effort to secure enough orders to fill capacity.

Pariveda developed a Machine Learning approach to logistics-demand forecasting that enabled new operational efficiency.

Learn how we deliver on the essential, strategic needs that enable companies to sustain a resilient and impactful business.

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