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Understanding How AI is Making Freight Procurement Easier

Understanding How AI is Making Freight Procurement Easier

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AI is becoming a power player in logistics just as it has been in many other industries. The ability to automate the freight procurement process to positively impact key performance metrics, such as on-time delivery (OTD) is helping ease the strain for many shippers-- especially when capacity is tight!

However, many shippers and procurement teams are still reticent to adopt the new technology out of “fear of the unknown”, or because they think it would be too difficult to integrate into other platforms, or simply being too comfortable with their long-established processes (even if they can no longer handle today's volatility). Here are a few reasons why shippers are falling in love with AI and why it is time for existing holdouts to take a much deeper look. 

Routing and Rerouting

Routing is still one of the most important aspects of freighting today. But getting routing right and then adjusting routes to account for disruptions -- which happen numerous times a day -- requires a lot of legwork and time. The good news is that AI can predict what happens before it actually happens so teams can be proactive, opposed to reactive in sticky situations. AI allows teams to circumvent and/or solve problems in a fraction of time, freeing up valuable time to focus on other priorities.

Optimizing the Carrier Network

AI can dynamically find and match qualified, asset-based carriers when truckload capacity is needed most! It's no secret that acceptance rates are at all-time lows, which means many shippers are scrambling to find carriers to move their goods. New AI-backed technology, such as OTS Freight Procurement Software, empowers users by custom configuring over 80 load attributes so the shipper can dynamically match a load to a perfect, compliant carrier. 

Forecasting and Intelligence 

AI is not just for what shippers can do now, but what they can do in the future. AI can predict and analyze trends by using analytics, taking statistics from large supply chain, manufacturing, and logistics datasets to forecast what’s next for the industry -- something that is virtually impossible to do using traditional tools like Excel and rudimentary data analysis.