Reimagining Freight Forecasting Using Machine Learning Technology

Advanced Machine Learning algorithms are streamlining the freight procurement process by accurately predicting lane pricing

Freight procurement is a critical function in logistics. Shippers and 3PLs often follow laborious conventional contracting processes to secure reliable capacity and move their loads cost-effectively. The advent of Machine Learning (ML) technology has altered the game in freight logistics by accurately predicting lane pricing. Modern ML models based on advanced algorithms integrate dynamic market data and historical freight data to correctly forecast lane pricing. This streamlines the process of freight procurement, ensuring shipper demands are met with adequate carrier capacity.

In our new whitepaper, we talk at length about the freight contracting process and the impact of technology in guaranteeing accurate freight rate forecasting. You will get deeper insights on the following –

Factors impacting lane pricing

Why it is important to consider dynamic market disruptions while determining freight rates

Limitations of the traditional freight contracting process

How Machine Learning technology has streamlined freight forecasting, leading to increased efficiencies and business value

If you are wondering how to integrate advanced ML models in your Transportation Management System to benefit from real-time freight forecasting, download this whitepaper today!

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