As we grasp what TMS technology brings to the table in process efficiency, the exploration of Machine Learning and Artificial Intelligence (AI) is beginning to shape how supply chains will be executed in the future. We have all been privy to the number of technology companies and start-ups taking a swing at the supply chain to change or enhance the future of our industry. Up until now, the primary focus for AI and Machine Learning has been identifying capacity and solving the age-old challenge of finding a truck, not an easy challenge to overcome. However, the companies that seamlessly marry a shipper to a truck will grow their reach and ability to deliver a truck when and where it’s desired. The challenge, bringing together all the available data points and potential assets into visibility.
What we know today is that Machine Learning applied to the capacity challenge is an application that is driving internal LSP (Logistic Service Provider) benefits in the ability to identify capacity and drive operating costs lower. The application of Machine Learning that is being applied most frequently is Natural Language Processing (NLP) which is an offshoot of AI and Machine Learning. NLP takes the information in communication forms such as emails and structures the information to be utilized by an LSP in identifying truck capacity and available loads at a frequency and volume that breaks the traditional processes used by the Truck Brokers. Traditional processes include dialing carriers and receiving emails from carriers illustrating where their trucks will be or are current location.
There are two primary channels of data that are being harvested by NLP applications. The first is email blasts from carriers illustrating where their trucks are empty or will be empty. Secondly, shippers distribute long lists of loads available to their asset and non-asset providers requesting coverage. The ability to take multiple email and file formats and structure the data points into a singular format for integration into the LSP’s system delivers visibility of where trucks are and where are they needed. Powerful when considering that automated workflows within an LSP’s system can then build, accept the tender from the shipper, and secure a truck all without any manual touch points. Metrics, such as loads per head, get increased, which ends up benefiting shippers as the cost to service their supply chain is reduced. Metrics such as these push LSPs to push the boundaries of Machine Learning and lower their internal cost to serve.
As we look ahead, we still envision obstacles to overcome. Those challenges illustrated below, are now real challenges, not a future visionary statement.
For more information on Natural Language Process applications and their potential positive impact on your business as a Shipper or an LSP please reach out to a Rockfarm Representative. Our Supply Chain Coaches will be available to walk through how NLP has become a game changer in our Fulfillment operations and how we expect the growth of NLP and AI applications to become the trend that changes the supply chain landscape for many years.
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Brad’s journey into logistics began as a Marine Officer and transitioned from the LTL docks to the non-asset side within the logistics service provider arena. As a co-founder of Rockfarm, Brad drives our business development efforts and delivery of our promise. An Arizona native, Brad enjoys spending time outdoors in his home state with his wife and family.
“Our approach to the market allowed us an opportunity to push forward in 2008 and enable our mission, “lower the cost to serve” to stand as a cornerstone to our company today.”
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