TMS Fall Maintenance: Pruning Data Decay & Using AI for Peak Performance

A TMS is a major investment, so when it doesn’t live up to your expectations, it’s frustrating (and that's putting it mildly). The good news is that all is not lost—a little seasonal maintenance will go a long way.

In our latest LinkedIn Live, Brad Forester, JBF CEO, and Mike Mulqueen, JBF Executive Principal, Strategy & Innovation, discussed the importance of TMS maintenance and how AI can help identify and correct system issues.

• Maintenance is Crucial: Regular updates and maintenance of a TMS are essential to ensure data integrity and system performance. Without ongoing attention, systems will degrade, leading to ineffective solutions. 
• Investment in Knowledge: Companies often allocate significant resources to implement a TMS but neglect to invest in understanding its functionalities and maintenance requirements. Proper knowledge and training are vital to maximize the TMS's potential. 
• Collaboration with Vendors and Consultants: While shippers are responsible for maintaining clean data, tech vendors and third-party consultants can provide invaluable support in identifying system issues and bridging gaps in data management and process efficiency. 
• AI's Role in TMS: The future of TMS lies in the integration of AI that not only identifies opportunities for improvement but also facilitates the implementation of those recommendations.

In order to run, a TMS relies on data. As long as it’s running, the assumption is that the data is good, but without continual maintenance, problems are inevitable. 

Companies often invest millions, if not tens of millions, of dollars into a TMS but fail to invest in their own knowledge. Understanding the maintenance that your TMS needs and what it’s truly capable of is crucial.

In the end, there’s no magic to it, it’s just data. If bad data goes in (or if data isn’t up to date), guess what? A bad solution is coming out.

The following are a few examples of things that need to be done to ensure optimal results:

• Be sure master data is up to date (read more here)
• Keep all rates current
• Look at your freight accruals or your account codes and ensure they’re mapped to the correct general ledgers 
• Make sure somebody knows what needs to be done when your business changes or when a new acquisition is pulled into your organization

In a perfect world, shippers would be able to manage their own systems, but, realistically, we may be asking too much of them to do it on their own. Very few shippers excel at master data management and ensuring processes, creating a gap that leads to degradation of the overall solution quality and a loss of trust in the system. 

The vast majority of shippers are not equipped to bridge this gap. On the flip side, vendors and third-party consultants are. For example, we at JBF could go back through for a shipper and look for signs of decay (i.e. How often are planners overriding the solution? How often are data coming in that are failing for one reason or another). AI could potentially be used to identify areas of decay within the TMS as well.

In the end, ensuring clean data and the functionality of the system falls to the shipper, but tech vendors and third-party consultants are uniquely positioned to help.

AI is constantly evolving. Right now, we are seeing the utilization of AI mainly through chatbots, and it is adding a ton of value. JBF uses AI internally, and we can attest that it's great for data retrieval, data analysis, and similar tasks.

However, the next generation of AI will not only identify opportunities but help operationalize its recommendations based on the insights.

For example, AI would generate a report for a client telling us we should have been using a specific intermodal carrier on these particular lanes over the last month, and if we had, we would have saved $37,000. Next, the AI would ask, “Would you like to operationalize this suggestion?” Assuming the user consents, it would automatically go in and reconfigure the application so the next time an order comes in, it can actually do it.

To reiterate, AI is not there yet, but we are cautiously optimistic that it will be in the future. Right now, Loadsmart is hitting that first piece. They recently did a demo, and their AI was able to provide great insights. It compared their rates against the contract and spot markets and showed them where there were potential opportunities to do mode shifting (mainly intermodal to truckload). This is the next step for AI.

Without continuous maintenance, your TMS will decay. Now is the perfect time to ensure your data is clean. Remember, even if you spend tens of millions of dollars on your TMS, it is only as good as the data it's given.

AI is evolving rapidly evolving and is likely to become a key tool for managing your TMS. In the meantime (and even after), it is important to understand your TMS’s capabilities and the maintenance it needs to run optimally.

If you want to get the most out of your investment, the work doesn't stop after the implementation—it requires continual maintenance. Set aside time this fall (and all subsequent falls) to ensure your TMS is ready for the next peak season.

You can view Brad and Mike’s conversation in its entirety here.