The Transportation Management System market continues to grow at an impressive rate. I’ll get to the main factors that are driving this growth in another article, but the main reason is the robust ROI attributed to a TMS. Companies buy a TMS to achieve freight savings which can be attributed to simulation and network design, load consolidation and lower cost mode selections, and multi-stop route optimization. There are a number of key trends that are driving customers to explore TMS and suppliers to enhance their offerings.
Transportation Management Trend 1: Visibility Tools
Real-time visibility solutions are set to explode. The visibility is based on integration to truck carrier’s systems. The carriers in turn are tracking the ELD devices on their trucks or by a downloadable app that the driver puts on his smart phone. There are a variety of external data streams that also play a role in providing better visibility and improved ETAs.
An emerging data stream takes IoT data from trucks to get a better understanding of driver behavior. This includes basic information such as understanding typical driving speeds and times of drivers, as well as how they operate in heavily congested areas. Trimble Transportation’s True ETA application, for example, takes sensor data from trucks and incorporates hours of service rules to know when, where, and for how long a driver needs to stop. The application also understands that where and when the driver stops will have an impact on the ETA. This is especially true if the driver stops before a major city and will have to endure rush hour traffic once they start driving again.
Transportation Management Trend 2: Artificial Intelligence and Machine Learning
Real-time visibility solutions are raising the prospect that machine learning can be used to improve ETAs. But unless a TMS provider has access to network transportation data (public cloud providers do), the visibility provider will be in a better position to use artificial intelligence to provide these enhanced ETAs than the TMS provider.
Aside from improved ETAs, machine learning plays a role in other aspects of transportation management. Shippers can learn which carriers meet on-time service levels and which do not, which lanes typically carry more chance for delays, and whether there is an optimal number of stops before shipments become late. Machine learning can aid shippers in better understanding how to drive efficiencies without sacrificing service levels. For example, in last mile routing the time a job takes to complete is dependent not just on the miles that need to be driven, but on the congestion, the type of product being delivered, the type of residence, and whether the value-added services are provided at the destination. Machine learning can be used to “learn” these constraints rather than having to do time studies and hard code these constraints into the solution.
JDA is an example of a company that is using machine learning to improve service levels. The company uses multiple data streams to better understand potential disruptions in the travel time for shipments and plan accordingly. Additionally, JDA is using machine learning to correlate variables that disrupt plans; companies can then make more resilient plans that absorb disruption without making major changes.
Transportation Management Trend 3: IoT
IoT and connected trucks and containers are gaining a lot of traction in the market. When assets are on the move, data must be used to optimize functional operations. This includes both fleet safety and route optimization. Often, customers have a difficult time understanding the data they are collecting from their assets. However, when combined with artificial intelligence, the right IoT application can help make sense of data to improve asset performance. Sensors in trucks can help provide better visibility and make predicted ETAs more accurate. Additionally, connected trucks can provide information on specific driver’s driving patterns and habits, leading to improved safety as well as visibility.
Tata Consultancy Services (TCS) is an example of a company that is leveraging IoT for fleet management capabilities. Its offering is designed to provide industry versatility, mapping industries with both fleet management capabilities as well as integrated logistics capabilities. The IoT solution goes beyond just tracking a truck. Instead, the result is better fleet tracking, reduced wait times at destinations, better vehicle utilization, and cost savings from proactive maintenance.
Transportation Management Trend 4: Partnerships
Partnerships within the TMS market have become big news lately. A lot of this can be attributed to the need for better visibility tools for improved ETAs. As I mentioned earlier in this article, many TMS companies are partnering with visibility providers such as project44, FourKites, 10-4 Systems, Descartes MacroPoint, and a host of others. On top of that, the rise of digital freight matching solutions has provided an opportunity for partnerships. While hundreds of millions of dollars are pouring into startups looking to disrupt the transportation industry by matching contract drivers to loads, there are few startups that appear to have a winning formula in this area. Many large TMS suppliers are now including digital freight matching marketplace integration with their core TMS.
There are certainly other areas of interest for partnerships as well, especially given the ever-increasing need to meet customer expectations. MercuryGate, for example, recently announced a partnership with Convey, a provider of Delivery Experience Management (DEM) software. The partnership integrates Convey’s DEM technology into MercuryGate’s TMS to allow shippers to launch DEM programs that provide complete visibility through the last mile of delivery. This means that shippers can manage the entire shipping experience down to the final delivery. Shippers also will have access to end-to-end visibility which can help in future planning of shipments.
The TMS market is poised for growth for a variety of reasons. That being said, there are four key trends that are helping to shape the market, drive customers to explore TMS adoption, and push suppliers to enhance their solutions. The need for real-time visibility into shipments is critically important for providing updated ETAs which are required for planning purposes as well as improving service levels. Artificial intelligence and machine learning can aid shippers in better understanding how to drive efficiencies without sacrificing service levels. IoT is the next wave in big technology, as it allows for better tracking of assets while improving driver safety. And finally, as new technologies hit the market, TMS providers will need to partner with these suppliers to ensure customer expectations can be met.
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