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:
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.
