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The Russian invasion of Ukraine disrupted the grain flows from that region and worldwide. These changes are critical due to the war’s influence on logistical costs, routes and capacities. As a result of the invasion, Ukraine has evolved from having some of the lowest logistical costs in the world to having the highest logistical cost. Logistics are critical for international competitiveness in commodities, and due to the invasion, these functions have been severely affected. Essential features for a logistical competition include internal logistical functions and costs, quality, port capacity and ocean shipping costs, each compounded by seasonal demands. This paper’s purpose is to analyze the effects of the Russian invasion on the logistical functions and the costs for corn exports from Ukraine and its competitors using an optimized Monte Carlo simulation model. The findings indicate that before the invasion, Ukraine had logistical advantages for shipments to the European Union (EU) and was highly competitive in Indonesia and China; the United States had a logistical cost advantage over Ukraine to serve China, South Korea (from the U.S. Gulf) and Japan (from the Pacific Northwest (PNW)). The changes due to the invasion are substantial. Most important is the radical increase in shipping costs from Ukraine, reduced port capacity and export supplies. However, concurrent with the invasion were changes in some critical trade and marketing policies, thus influencing the international competition for corn.

In addition to the traditional market price risks, origin shippers of grains and oilseeds face risks related to the costs associated with the handling, storage, and transport of crops to their destination markets. To more closely examine the nature of these risks, a Monte Carlo optimization model was constructed using the Material Requirement Planning (MRP) methodology. The goal of the model was to minimize the mean and 5% value-at-risk (VaR) of the sum of these associated costs. The primary decision variable was the number of unit trains to purchase in the primary rail market. The model was also subjected to additional sensitivity and stress analysis to analyze the properties of the optimal results. A primary observation derived from this analysis is the importance of the primary and secondary railcar markets to the management of these risks, both from an informational and risk management perspective. Another key driver was the ability to manage the seasonality of farmer deliveries via forward contracting to capture returns to storage. Finally, another key contribution of this study is to demonstrate the utility of using subject matter expert (SME) time series modeling in Monte Carlo simulation.