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Consumers want their products immediately. Even for larger company buyers, once they decide to purchase the item, they want to begin receiving its expected benefits as soon as possible. Software developers have shortened consumers’ expectations for product lead times by creating transportation management systems (TMS).
These systems aid companies in logistics planning by modeling shipping paths to shorten the time the products take to reach their destination. And with over 21 billion packages shipped annually in just the US1, TMS software ensures the shipping paths and carriers intersect and interfere with each other as little as possible.
This blog explores how AI can enhance TMS to improve freight logistics, achieve maximum cost savings, shorten delivery times, and promote eco-friendly practices that reduce the carbon footprint of freight.
TMS systems have three primary functions that help them reduce complexity and boost efficiency:
The TMS software compares shipment rates for various carriers to optimize costs based on the transit route. This step accounts for variables such as container size, loading geometry, and the mode of freight transport—road, rail, ocean, or air—to maximize the number of goods per shipment package.
For instance, “containerization” refers to the orientation and stacking of packages within a shipping container. For high-volume goods, orienting the parcels to add another row inside the container can have massive financial benefits. In addition, if the commercialization schedule can absorb ocean freight versus air, for example, the time it takes to receive products over the ocean could more than offset the time savings by (more expensive) air freight.
The freight management function addresses processes like quoting freight, executing the contract, and dealing with quoting, billing, and disagreement resolution with the various transportation carriers.
The third piece is a dashboard to collect analytics and forecast freight demand. The TMS software analyzes the profitability and modifies transportation dynamically when conditions change. Having a system with visibility in each step in the logistics process helps flag issues as they happen.
The primary TMS benefits come from the ability to collect data that optimizes the functions outlined above. Collecting data throughout the process allows logistics planners to consider changes in carrier strategy, pricing structure, or transportation method. In addition, logistics planners can collect data around product breakage by carrier or transport method, factoring that inefficiency into transportation economics. The ability to improve transportation through data-based optimization makes TMS ideal for AI.
The digitalization of transportation and logistics led to the efficiency gains noted above. And because you can’t solve a problem you don’t know exists, collecting this data and tracking trends was the first step to tightening up the steps of the logistics process. Three applications stand out among the many improvements possible with AI-driven TMS.
With AI, TMS can absorb the increasing amounts of data to inform and direct the logistics process toward continuous improvement continuously—in real time. Instead of bulk assumptions for ocean freight timing versus air, TMS can collect information to model goods transport in both modes and recommend a plan that conserves cost and energy.
AI can integrate with traffic data to continuously optimize truck routing throughout the day. Larger cities have heavy rush hour traffic so the software can learn the traffic pinch points over time and advise optimized routes to avoid those. In addition, AI-driven TMS can avoid costly delays by tracking crashes, weather, or other unplanned events that disrupt typical pathways.
Smart TMS software can collect breakage data input from the purchasing entity and any customer service complaints upon the shipment’s arrival. The system can compare product quality loss with various route suggestions and factor that into the predictive modeling during route definition.
In addition, adding smart sensors to vehicles allows the TMS software to collect data that helps predict upcoming transport vehicle maintenance needs before they happen. These smart sensors might include emissions sensors that monitor the engine's exhaust or vibration sensors that monitor the engine or transmission vibrations. The information from these sensors further minimizes downtime and catastrophic cost or safety risks that result from significant vehicle failure in the field.
A third benefit of integrating AI into TMS is the compound impact of lowering costs and the carbon footprint. Optimizing transport routes shortens transit times and boosts delivery economics. But another benefit of optimizing transport routes also extends to minimizing the transit time of empty containers on the return trip. Return travel is a necessary inefficiency that retrieves the trucks and containers, but transporting empty containers is a wasteful process.
AI-driven TMS software can optimize the routing of empty containers to closer drop-off or pickup sites to help reduce the return times. This helps companies save substantial fuel costs and extends vehicle life through reduced travel, improving both cost and carbon outlays in the process.
In response to consumer demand for rapid product delivery, transportation management systems have become cornerstone tools in logistics. These systems streamline transportation planning, freight management, and data analytics to optimize operations.
Now, smart features within TMS software generate an even larger amount of data, creating an ideal scenario to apply AI and the developing capabilities of machine learning (ML). Going forward, ML will continuously optimize processes and tasks, while AI provides the best human response to react instantly to a negative signal in the data. ML and AI offer advantages that improve the supply chain's logistics efficiency, consumer cost, and lifecycle climate performance.
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Adam Kimmel has nearly 20 years as a practicing engineer, R&D manager, and engineering content writer. He creates white papers, website copy, case studies, and blog posts in vertical markets including automotive, industrial/manufacturing, technology, and electronics. Adam has degrees in chemical and mechanical engineering and is the founder and principal at ASK Consulting Solutions, LLC, an engineering and technology content writing firm.