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Freight shipping has relied on the same systems for years, ranging from manual entries to a constant struggle in balancing delivery windows with costs. However, the pressure for tighter, faster and more deliveries has increased to a point where the only solution is change. According to William B. Cassidy of the Journal of Commerce, truck capacity is not tight; it is simply hidden from view. Meanwhile, technologies, like machine learning, artificial intelligence (AI) and automation, will catalyze a reinvention of the transportation industry and less-than-truckload (LTL) shipping above all other modes. Before these changes can happen, shippers need to understand a few things about LTL shipping challenges and ways Less Than Truckload technology will impact them.

What Are the Major LTL Shipping Challenges?

The major issues with LTL shipping derive from assumptions shippers make during carrier selection. Some of these problems include the following:

  • “Not all carriers handle LTL shipments,” so finding a carrier that offers LTL shipping at a competitive rate is difficult at best.
  • Poor transit time occurs as some carriers place LTL shipments on the proverbial backburner, resulting in delays.
  • Carrier rates may vary. This problem tends to go unnoticed unless a transportation management system (TMS) is used to compare carrier rates side-by-side.
  • Deadhead results in higher costs. Every mile driven without using cargo space is lost money to carriers and shippers alike. However, freight consolidation and LTL shipping can use Less Than Truckload Technology to reduce deadhead and increase profitability.

Machine Learning Will Enable Adaptive, Predictive Pricing Models.

Machine learning refers to computer systems, including cloud-based TMSs, that consider historic data and use it to improve the system’s existing processes. In a sense, this is an automatic process, but it can be leveraged to define new routes, find lagging systems and errors in shipping practices and replace electronic data interchange (EDI) systems. In addition, machine learning is adaptive, so data coming into the system increases its efficiency and capacity to provide value for shippers and carriers. Moreover, machine learning will power adaptive, predictive pricing models, eliminating risk from misclassification or incorrect carrier selection.

AI Will Gradually Replace Manual Processes.

Think of AI as machine learning on steroids. It functions through an ongoing series of algorithms and internet-connected devices, the Internet of Things (IoT), to make data-based decisions before shippers overlook something. This is an important change in how small and mid-sized shippers operate, enabling rapid scalability and unmatched accuracy in both invoicing and shipping through LTL carriers.

For example, shipper A uses manual processes to manage invoices. Over one year, the shipper pays up to 50-percent higher costs due to double billing. But, shipper B uses AI, enabling automated payments and auditing within the system. It finds billing and compliance issues and implements changes, like triggering chargebacks, to carriers. Thus, billing is corrected, and the physical resources needed to process LTL shipments decreases.

An additional use of AI includes the use of embedded analytics to evaluate factors influencing an LTL shipment’s rate, explains DC Velocity, considering possible delays and issues, giving shippers more information before selecting a carrier.

Automation Will Reduce Errors and Enhance Existing Shipping Practices, Reducing Problems.

AI also relies on automation, but for the purposes of this discussion, think of automation as systems that auto-populate data and replace the standard operations performed by people.

People are reactive. Shippers see a problem when it reaches the point of causing a disruption in the supply chain. But, automation can be used to identify the indicators of a forthcoming disruption before they come to fruition. Like changes in weather patterns that increase the risk of flooding. As a result, routes can be automatically adjusted to circumvent the problem. This is based on a proactive, not reactive, risk management strategy within the supply chain.

Now, consider the impact of automation in LTL shipping and last-mile delivery. Automated delivery via drones or hand-held scanners upon delivery increase delivery accuracy. In addition, automated tracking of shipments ensures visibility to consumers, carriers and shippers. As a result, customer service levels increase, safety increases and the product cycle continues.

Automation rests on the cornerstone of making the best carrier selection for each type of shipment, big or small. In LTL shipping, certain carriers have already switched to dimensional pricing models (DIM pricing), that considers weight and shipment dimensions in rate determination. With a growing number of shippers and local carriers working to meet the demands of e-commerce, automation is essential to maintaining today’s companies’ operability.

Could Less Than Truckload Technology Replace Everything?

Automation, AI and machine learning are a triad of Less Than Truckload technology that improve upon one another. These technologies overlap, producing many grey areas that can improve supply chains and reduce the “legwork” in LTL shipping. Furthermore, the technologies have the potential to replace all human jobs in shipping, but that possibility is far from the capacity of today’s technologies. For shippers concerned about the economic impact, consider how a change in Less Than Truckload technology standards will result in the need for more skilled workers to manage and develop such systems.

 It is a shift away from traditional shipping practices to a digital-driven workforce and series of best practices. Unlike terminal-based systems of the past, these technologies reside the cloud, serving to empower new generations of technological systems that will enhance and propagate efficiency and resolution of LTL shipping challenges.

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