The SCM thesis Reading the Freight Forecast: Making Sense of the Truckload Market Cycle was authored by Sarah Roman and Wirinratch (Bonus) Kirirak, and supervised by Dr. Chris Caplice (caplice@mit.edu) and Dr. Angi Acocella (acocella@mit.edu). For more information on this research, please contact the thesis supervisors.
The U.S. dry van full truckload (FTL) market is a balancing act between truck capacity (supply) and freight demand. This dynamic was shaped largely by the Motor Carrier Act of 1980, which deregulated the industry and lowered barriers to entry. Deregulation introduced greater price volatility, creating a cyclical pattern that alternates between tight and soft market conditions. Though many in the industry have worked to make sense of these cycles, no shared definition or forecasting framework existed. This study addresses this gap by proposing an industry-driven definition of the truckload market cycle and identifying influential variables to forecast the timing of future phase shifts using statistical modeling.
Defining the cycle, forecasting the future
Given the lack of a consensus-based definition of the truckload market cycle, and with sponsorship from C.H. Robinson, we developed a clear, structured framework to define its phases and enable more effective forecasting. We conducted 20 interviews with experts across the industry, revealing key external factors shaping the market. These insights informed a broader industry-wide survey to validate and expand the findings. Spot and contract rates emerged as the most widely used indicators of market conditions and were chosen as dependent variables for forecasting. Based on this input, we defined a four-phase cycle definition represented by spot rate, contract rate, and spot-premium ratio.
From this process, 31 potential influencing variables were identified and evaluated for their relationship to spot and contract rates. Statistical testing found 12 metrics to be significant, including inventory to sales ratio, housing starts, Producer Price Index (PPI), diesel prices, Commodity Research Bureau (CRB) and Bloomberg Commodity indices, and Class 8 indicators. To evaluate the model, we introduced a “timing error” metric that measures how closely predicted phase shifts aligned with actual market transitions. These insights can help stakeholders anticipate changes in capacity and demand, enabling tactical and strategic adjustments in response to external shifts.
For more about this capstone project, and to see the full results of this research, visit the Supply Chain Management Review online at SCMR.com.
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