Machine Learning and TMS: The Future of Freight Management Is Self-Optimizing

Machine Learning And The TMS

The combination of machine learning and TMS functionality offers the opportunity for self-optimizing systems, continuously generating more accurate reports and enabling informed decision-making through automated functions.

As reported by Supply Chain Quarterly:

“Machine learning is also becoming increasingly important in transportation management and execution systems. The most notable application is generating a more informed and up-to-date estimated time of arrival (ETA) for shipments.

“Machine learning is working with real-time visibility solutions to learn more about constraints (such as capacity, regulations, and hours of service) and then using that information to give a much better ETA for shipments to warehouses.”

Common Problems With the Current Use of a TMS

The biggest problem with the use of some TMS platforms and light solutions in the market is lackluster visibility. 

After all, some visibility can improve supply chain efficiency and generate ROI. However, complete visibility into all processes and the ability to leverage real-time data make a huge difference in overcoming obstacles. 

Furthermore, simplistic platforms may lack the combined power of machine learning and TMS functionality, opening the door to even greater costs without worthy rewards.

Machine Learning and TMS Enable Additional ROI

Machine learning in TMS empowers shippers and logistics service providers (LSPs) to reap additional ROI from system implementation. 

Machine learning relies on artificial intelligence and advanced data sets, including an analytics engine, to continuously reevaluate all factors. This continuous analysis yields the identification of correlations and patterns within data. Applied correctly, these insights offer a rubric to improving profitability and enhance the value of the transportation management system. 

Moreover, the ability to leverage artificial intelligence is rapidly becoming a differentiator in the market, and companies that apply machine learning and TMS functionalities in conjunction often see increased market share against competitors.

How Self-Optimizing Freight Management Generates Additional Value

Self-optimizing systems can add more confusion to an already complex supply chain, but the value-add is clear. As explained by Steve Banker of Forbes:

“But TMSs – particularly network-based solutions like those from TMC, BluJay Solutions, and Transplace – are rich in data. Machine learning depends on Big Data sets. The problem above would probably be solved using supervised learning. In supervised learning the system is provided with a raw data set but then also provided with a target. In this case, the system is asked to predict on-time deliveries (OTDs) based on a variety of data inputs. How does OTD change based on multi-stops? Based on region?
Based on type of customer (customers classified as grocers, for example)? Based on real-time milestone data. And potentially based on lots of other data sets, even data sets that exist outside the TMS. Then, the algorithm attempts to understand how to match the input to the output and whether an input has any impact at all on the desired outcome.”
The path to progress is simple. Track all the data. Simplify its use with machine learning. Let artificial intelligence make decisions. Continue working to build better customer and employee experiences and gain efficiency with less physical work than ever.

Reap Rewards of Machine Learning and TMS Functionality Now

The value of machine learning and TMS capabilities are significant. Organizations that adopt automation already see gains that average on 10%; applying machine learning could push those gains to more than double original projections. 

This is what creates Smart Transportation: using all information always to secure the best rates possible and up to 2% more on ROI for TMS implementation.

See how combining Machine Learning and TMS brings value to your business.


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