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 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.
TMS Machine Learning Enables Additional ROI
TMS machine learning 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.
10 Ways TMS Machine Learning Benefits Freight Management
TMS machine learning can deliver a variety of use cases and benefits to freight and logistics management. Here are 10 use cases cited by Matellio.
- Final mile delivery optimization.
- Warehouse management.
- Workforce planning.
- Quality control.
- Real-time tracking.
- Self-driving vehicles.
- Supplier relationship management.
- Dynamic pricing.
- Fleet management and optimization.
- Risk management and safety enhancement.
Reap Rewards of TMS Machine Learning Functionality
The value of TMS machine learning 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.
