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Repositioning Empty Containers for Agricultural 

Container Logistics (Summary)

Agricultural 

Marketing 

Service

August 2020

WHAT IS THE ISSUE?

U.S. exporters have a continual demand for empty containers. Yet, matching this demand—particularly for 

agricultural exports—with the supply created by imports presents a greater challenge than would seem at first 

glance. The number of containers available from U.S. imports of consumer goods far outstrips the number 

needed to send U.S. products overseas. In theory, this system creates a synergy for the U.S. exporter and the 

ocean carrier, as it affords exporters lower (backhaul) ocean freight rates. However, for many agricultural 

shippers, the long geographic distances between their export product and the pool of empty containers pose a 

significant logistical hurdle. 

Destinations for import containers are typically densely populated metropolitan regions, rather than small, 

rural communities where most agricultural production originates. Repositioning empty containers from 

these populated metro areas to rural agricultural production centers is an expensive endeavor. Nevertheless, 

agricultural exporters do it every day to serve their overseas market, despite eroding their own profitability. 

These formidable obstacles and high stakes make empty container repositioning (ECR) one of the most 

significant and challenging issues facing the container shipping industry. A study by the New Jersey Institute of 

Technology (NJIT) develops effective solutions for U.S. grain shippers to leverage empty containers using ECR 

and transloading operations.1

1 Transloading is one such strategic solution to containerized agricultural exports, as it uses inland transportation conveyances to bring grain and 

other agricultural products to the maritime containers. Transloading refers to the process of physically moving the cargo from one conveyance to 

another with the aid of transloading facilities, such as elevators and conveyers (Thomson, 2012).

This is a summary of “Repositioning Empty Containers for Agricultural Container Logistics,” by Junmin Shi, 

an associate professor in New Jersey Institute of Technology’s Tuchman School of Management. Funding 

for this paper came from the Agricultural Marketing Service (AMS) through cooperative agreement number 

16-TMTSD-NJ-0008. The opinions and conclusions expressed are the author’s and do not necessarily reflect the 

views of USDA or AMS. The full paper is available at: https://web.njit.edu/~jshi/USDA-ECR-Final-Report.pdf.

April Taylor and Jack Novak
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WHAT DID THE STUDY FIND?

A surplus of empty containers is typical in the U.S. container shipping market, mainly because of a trade or 

transshipment imbalance. The study considers two ways in which exporters can respond to this surplus: (1) 

use rail to reposition empty containers near a production site for source loading and shipment to port, or (2) 

ship cargo to a port via truck, then transload the cargo into a container.2 The study refers to the first option as 

‘multimodal’ and the latter as ‘truck-only.’ With either option, exporters and carriers sustain additional costs; 

however, either option may be optimal, depending on circumstances. 

Researchers determined several variables that affected optimal shipper strategy. Then, tipping points—the 

minimum value at which multimodal shipping is optimal—were calculated for volume and distance. Next, 

researchers conducted sensitivity analyses to determine how these critical values respond to changes in ECR 

cost.3

All else equal, total drayage distances above 204.69 miles indicate a shipper should use multimodal shipping. 

Alternatively, using a fixed distance, researchers find a tipping-point volume of 358.84 tons. For values of 

distance and volume beyond the tipping points, savings associated with multimodal transportation generally 

increase as distance and volume increase. 

To measure how ECR costs affect exporter decision-making, the study considers two scenarios in which 

the drayage distance for a truck-only option is either 10 percent shorter or 10 percent longer than that of 

multimodal shipping. The tipping points of distance and volume for both scenarios are then integrated with 

ECR costs ranging from $1 per ton to $8 per ton.

Not surprisingly, tipping-point values for volume and distance are lower when truck-only drayage is longer than 

multimodal drayage, compared to the opposite scenario. Furthermore, tipping-point values for both distance 

and volume increase as ECR costs increase.

To help shippers decide whether to employ truck-only or multimodal shipping, the study includes a dynamic 

programming model that allows them to compute the optimal option for their situation. The model allows 

shippers to program distances, volume, ECR costs, transloading fees, shipping costs, storage cost, grain price, 

beginning stocks, and harvest quantity across multiple harvest seasons to determine their optimal shipping 

strategy.

HOW WAS THE STUDY CONDUCTED?

The researchers consulted several different organizations for the information included in the study. Third-party 

logistics companies provided shipping cost per ton-mile, while farmers and farmer cooperatives provided 

volume data. Furthermore, data were drawn from IHS Markit’s PIERS 2016 dataset, the Public Waybill, and 

USDA.

Throughout the project, the researchers leveraged several quantitative methodologies, including hypothesis 

tests, regression analyses and forecasting, dynamic optimization and simulation, and sensitivity analyses. Once 

significant variables were established, static analyses were conducted to determine tipping points for volume 

and distance. Consequently, sensitivities were conducted with respect to ECR costs.

2 Shippers can also ship cargo to ports for transloading by rail. For simplicity, the study only considers transloading operations which rely exclusively 

on truck transportation.

3 Sensitivity analysis refers to “the study of how the uncertainty in the output of a model can be apportioned to different sources of uncertainty in 

the model input.” (Saltelli, 2002)
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The researchers also analyzed the seasonality of harvest and trading to create a dynamic model of inventory 

management. This model (and a description) is included in the appendices to allow shippers to customize 

model specifications.

Preferred citation:

Shi, Junmin. September 2019. Repositioning Empty Containers for Agricultural Container Logistics. New Jersey 

Institute of Technology, Web. <http://dx.doi.org/10.9752/TS275.08-2020>

Photo credit: USDA 

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