How to predict LTL volume trends and modal shifts

In the dynamic logistics and transportation industry, shippers and carriers face a perennial challenge: volatility caused by modal market cycle shifts. These changes can have a significant impact on pricing, service, routing efficiency, and carrier partnerships. Recent MIT research, sponsored by C.H. Robinson, provides valuable insights into how the full truckload (TL) and less-than-truckload (LTL) sectors respond to market dynamics¹ and offers a data-driven methodology that helps industry professionals to better forecast modal shifts in demand².

Understanding market cycles and freight migration

Market cycles in logistics tend to follow a familiar pattern, influenced by demand fluctuations, economic indicators, and supply chain disruptions. As demand ebbs and flows, capacity becomes either constrained or abundant, pushing shippers to consider alternative transportation modes.

Freight migration, specifically from LTL to TL, often occurs when the TL market is in a state of over-supply and costs decrease. During this time, shippers may move their larger-sized LTL shipments via full truckload due to the advantageous costs and transit times

Conversely, migration from TL to LTL often occurs when demand surges in TL markets, creating capacity limitations. Shippers facing these constraints seek flexibility or price savings by moving to LTL.

Anticipating these freight migration moments presents an opportunity for shippers, carriers, and freight brokers to improve their budget planning by managing costs and proactively securing capacity. Predictive models and heuristics, drawing on indicators like C.H. Robinson Managed Solutions™ Route Guide Depth (RGD), DAT’s Load-to-Truck Ratio (LTR), and ISM’s Manufacturing Purchasing Managers’ Index (PMI), can help professionals anticipate when a conversion of freight will happen.

Leveraging predictive models for strategic decision-making

The studies from the MIT Center for Transportation and Logistics analyzed various industry metrics to determine the most effective bellwether variables for LTL volume shifts. Ultimately, the study found RGD, LTR, and PMI most effective.

Based on these results, a machine learning model was developed to forecast LTL volume shifts with on average 75% accuracy. It can provide early signals of when TL demand will outpace supply and push shipments to LTL. For example, when the Load-to-Truck Ratio reaches a certain threshold, it strongly correlates with an uptick in LTL volume in the following month.

This predictive capability can be crucial for shippers seeking to lock in lower LTL contract rates ahead of a rate surge, potentially providing substantial cost savings. The output of this model enables more proactive strategies, allowing brokers and shippers to better understand market pressures and align their pricing and capacity planning accordingly.

Applying the predictive model to your supply chain strategy

To make this predictive power more easily accessible, the researchers translated their findings into a user-friendly set of rules. These rules are shown in a decision-making matrix that boasts on average 65% accuracy.

Here’s how you could apply it to anticipate LTL volume expansion:

1. Monitor thresholds

Set specific values for each indicator:

If the RGD increases up to or beyond 1.42, shippers can likely expect LTL volume expansion within the next 2-5 months.
If the monthly LTR reaches 4.4 or higher, shippers could anticipate an increase in LTL volume within a month.
A PMI score exceeding 53 or 61 signals a probable increase in LTL volumes within a month.

2. Determine urgency levels

By combining indicators into categories, shippers and carriers can tailor their responses based on market conditions. The categories include:

Watch: This suggests a potential modal shift several months out, but more input is needed to determine timing.
Advisory: This indicates a high probability that an increase in LTL volume is nearing and shippers should consider freight network adjustments to prevent cost increases and/or service failures.
Warning: This signals that an increase in LTL volume is imminent, likely within a month.

3. Strategize proactively

Carriers and shippers can use these indicators not just to react, but to also seek alternative modal options, plan routes, consider optimization opportunities, and work with partners to review procurement timelines before broader market changes.

LTL volume contraction can also be foreshadowed with similar parameters. When the RGD decreases beneath 1.42, contraction is likely on the horizon within 2–5 months as truckload becomes a more favorable option. Similarly, when the PMI decreases below 51 and/or the LTR reaches 3 or less, LTL volume contraction is likely to occur within a month.

Benefits of forecasting parameters

By accurately predicting capacity shortages or cost increases, companies can create more resilient budgets, negotiate smarter, avoid last-minute service failures, and help navigate cost/service trade-offs. Unfortunately, many companies don’t have the resources to develop in-depth models, so guidelines like these, based in industry research, are an important tool for shippers and carriers to plan and manage their budget.

For companies that prioritize adaptability and operational efficiency, these insights represent a crucial shift in managing the delicate balance of cost, capacity, and customer satisfaction. There’s no doubt that logistics are complex, but the parameters developed and outlined here offer a simple, clear method to reduce ambiguity and risk.

For more information or details surrounding the enhanced budgeting process, reach out to a C.H. Robinson representative.

NOTES