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Freight Transportation Resilience in Response to Supply Chain Disruptions (2019)

Chapter: Appendix C: Case Study of Grain Supply Chain from Illinois to New Orleans

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Suggested Citation:"Appendix C: Case Study of Grain Supply Chain from Illinois to New Orleans." National Academies of Sciences, Engineering, and Medicine. 2019. Freight Transportation Resilience in Response to Supply Chain Disruptions. Washington, DC: The National Academies Press. doi: 10.17226/25463.
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Suggested Citation:"Appendix C: Case Study of Grain Supply Chain from Illinois to New Orleans." National Academies of Sciences, Engineering, and Medicine. 2019. Freight Transportation Resilience in Response to Supply Chain Disruptions. Washington, DC: The National Academies Press. doi: 10.17226/25463.
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Suggested Citation:"Appendix C: Case Study of Grain Supply Chain from Illinois to New Orleans." National Academies of Sciences, Engineering, and Medicine. 2019. Freight Transportation Resilience in Response to Supply Chain Disruptions. Washington, DC: The National Academies Press. doi: 10.17226/25463.
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Suggested Citation:"Appendix C: Case Study of Grain Supply Chain from Illinois to New Orleans." National Academies of Sciences, Engineering, and Medicine. 2019. Freight Transportation Resilience in Response to Supply Chain Disruptions. Washington, DC: The National Academies Press. doi: 10.17226/25463.
×
Page 118
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Suggested Citation:"Appendix C: Case Study of Grain Supply Chain from Illinois to New Orleans." National Academies of Sciences, Engineering, and Medicine. 2019. Freight Transportation Resilience in Response to Supply Chain Disruptions. Washington, DC: The National Academies Press. doi: 10.17226/25463.
×
Page 119
Page 120
Suggested Citation:"Appendix C: Case Study of Grain Supply Chain from Illinois to New Orleans." National Academies of Sciences, Engineering, and Medicine. 2019. Freight Transportation Resilience in Response to Supply Chain Disruptions. Washington, DC: The National Academies Press. doi: 10.17226/25463.
×
Page 120
Page 121
Suggested Citation:"Appendix C: Case Study of Grain Supply Chain from Illinois to New Orleans." National Academies of Sciences, Engineering, and Medicine. 2019. Freight Transportation Resilience in Response to Supply Chain Disruptions. Washington, DC: The National Academies Press. doi: 10.17226/25463.
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Page 121

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115 APPENDIX C: CASE STUDY OF GRAIN SUPPLY CHAIN FROM ILLINOIS TO NEW ORLEANS A supply chain model was used to simulate the disruption of the shipment of cereal grains by barge between Chicago and New Orleans. The origin is the Illinois "Remainder FAF Region" (the portion outside of Chicago) and the destination is the New Orleans FAF Region. The grain industry is highly price-driven as farmers work with narrow profit margins and search for the best transportation costs possible. Cereal grains are the fourth largest tonnage in the U.S., and exports are a significant part of the cereal grains produced. Major competitors to the U.S., such as South American countries like Brazil and Argentina, have lower production costs, but the U.S. can compete in the global market because its efficient transportation infrastructure keeps shipping costs low. If a supply chain disruption causes the shipping costs to increase, U.S. grain suppliers could lose global market share, leaving a surplus of grain in the domestic U.S. market. A grain surplus would drive prices down significantly, and lower prices of grain could result in a loss in profits by producers. Therefore, minimizing the negative effects of a disruption in the supply chain is an important part of securing the nation’s global market share. Modelling a disruption can provide useful insights into how the supply chain responds to such an interruption. A disruption in this analysis was defined as an event, natural or manmade, that interrupted the flow of grain from its origin to its destination. Examples of a disruption event include natural disasters (e.g. flooding, drought) and infrastructure failures (e.g. lock failures). As before, the assumption in this analysis was that the distributor can only change the transportation mode and not the route or business partner. Vital to keeping down shipping costs for cereal grains is the efficient operation of the U.S. inland waterway system. The waterways overall have the greatest cost advantage for long-hauls, over both rail and highway (truck) long hauls. Trucks are useful and efficient at moving cereal grain short distances (less than 250 to 400 miles), with railroads possessing a cost advantage over truck for longer distances, but barges overall have the greatest cost advantage for long-hauls. The Agent-Based Supply Chain Modeling Tool is a computer code developed for the CMAP to simulate the transport of freight into, out of, and within the Chicago metropolitan area. In this study, certain parameters within the CMAP model were manipulated to simulate a disruption in the flow of grain between Illinois and Louisiana. Grains are shipped between Illinois to New Orleans mainly by barge and truck. Using information gathered through industry outreach and internet research, the outputs from the freight model simulations were validated. When waterway transportation was removed as an option, the model showed that the barge traffic shifted to rail. The railways replacing the waterways is consistent with the literature and with our interviews with industry officials in this market. About the CMAP Model The Agent-Based Supply Chain Modeling Tool developed for CMAP is a travel demand model that simulates the transport of freight between supplier and buyer businesses in the U.S., focusing on movements in the Chicago metropolitan area. In this study, certain parameters within the CMAP model were manipulated to simulate a disruption in the flow of grain between Illinois and Louisiana. The CMAP model uses FAF3 data, unit costs and times by mode per origin and destination pair, and other data such as county employment data as inputs. FAF3 provides origin-destination commodity flow data by SCTG2 for all the country. Cereal grains are represented by SCTG2 #02 in the FAF3 dataset, consisting of wheat, corn, rye, barley, oats, grain sorghum, rice and other cereal grains including seed, but not including sweet corn, soy beans or other oil seeds.

116 The model is built in the “R” programming language, a computer programming platform with robust statistical analysis capabilities. The model consists of 13 major parts that work sequentially. These parts, called “steps” in the model, include (based on the order of use): 1) firm synthesis, 2) supplier selection, 3) FAF flow apportionment, 4) business location assignment, 5) distribution channel, 6) shipment size, 7) mode path selection, 8) vehicle choice tour pattern, 9) stop sequence, 10) stop duration, 11) time of day, 12) preparation of trip tables, and 13) CMAP zone trips. Steps must be run sequentially. For this study, only steps 1 through 7 were completed. Step 7 is the ""mode path" choice portion of the code. This step allocates commodity flows among the minimum logistic cost paths, based on input variables such as unit cost and time between specific origins and destinations. Inputs that are important for developing the base case are the annual FAF commodity flows, as well as transportation cost and time estimates. The CMAP model uses FAF3 data, unit costs and times by mode per origin and destination pair, and other data such as county employment data as inputs. FAF3 provides origin-destination commodity flow data by SCTG2 for all the country. Cereal grains are represented by SCTG2 #02 in the FAF3 dataset, consisting of wheat, corn, rye, barley, oats, grain sorghum, rice and other cereal grains including seed, but not including sweet corn, soy beans or other oil seeds. The model is built in the “R” programming language, a computer programming platform with robust statistical analysis capabilities. The model consists of 13 major parts that work sequentially. These parts, called “steps” in the model, include (based on the order of use): 1) firm synthesis, 2) supplier selection, 3) FAF flow apportionment, 4) business location assignment, 5) distribution channel, 6) shipment size, 7) mode path selection, 8) vehicle choice tour pattern, 9) stop sequence, 10) stop duration, 11) time of day, 12) preparation of trip tables, and 13) CMAP zone trips. Steps must be run sequentially. For this study, only steps 1 through 7 were completed. Step 7 is the ""mode path" choice portion of the code. This step allocates commodity flows among the minimum logistic cost paths, based on input variables such as unit cost and time between specific origins and destinations. Inputs that are important for developing the base case are the annual FAF commodity flows, as well as transportation cost and time estimates. The model produces a list of commodity shipments by mode and converts them to daily vehicle truck trip tables that can be assigned to the national and statewide networks. The model forecasts the allocation of commodities among competing supply chains. The freight generation modeling step is based on the mesoscale40 freight model, using FAF3 data, and can only model movements that originate (or terminate) in the counties defined by the model as falling within the Chicago area. Error! Reference source not found.1 shows the counties in the Chicago Metropolitan area. The CMAP model area consists of 15 Illinois counties, three Wisconsin counties, and three Indiana counties. In defining the study area for modelling the cereal grain supply chain disruption, the origin study area was limited to the 15 Illinois counties in the CMAP model area (shown in blue in Figure C-1). Error! Reference source not found.Figure C-2 shows the parishes in New Orleans that served as destination zones. 40 Mesoscale means only mode choice and FAF serves as the surrogate for Trip Generation and Trip Distribution.

117 Figure C- 1: Origination Counties in the Chicago Region

118 Figure C- 2: Destination of Grain Shipments, Parishes in the New Orleans Region Base Case The base case describes shipping modal choice during supply chain operations. The analysis was to consider the movements of cereal grains by the inland waterway between the Chicago and the New Orleans metropolitan areas. However, in the original version of the model, inland waterway movements were not represented as an option for grain shipments, even though barge movements of grain do occur. To define a proper base case scenario, the water mode paths to inland waterways had to be activated. This allowed the model to simulate grain movements according to the scenario. No other changes were made to the model. Without the unit cost and time between the origin area and the destination for the inland waterway paths, the model would not be able to assign any traffic to the waterway mode. To accommodate the study, CMAP staff calculated the unit costs and times with this mode of travel and added them to the input file for the base case. This change was essential to properly represent grain waterway movements for the base case. Error! Reference source not found.1 shows the statistics associated with these unit costs and times. Paths 1 and 2 both represent movements on the inland waterway and differ only slightly based on which ports were used as the origin. The travel time for the shipments on the waterways between the origin and destination is roughly 18 days. This is consistent with the speed at which barges travel down the river, at between 1.5 to 3 miles per hour with roughly 925 miles between Chicago and New Orleans.

119 Table C. 1: Base Case Input -- Cereal Grains Cost and Time between the Chicago and New Orleans Metropolitan Areas by Waterway Cost for Path 1 ($/kilo-ton) Cost for Path 2 ($/kilo-ton) Time for Path 1 (hours) Time for Path 2 (hours) Average 52.43 53.81 442.50 449.25 Standard Deviation 1.65 1.66 0.46 0.46 Source: CMAP model Base Case Model Run Table C-2 illustrates the base case results for Cereal Grains transported from the Chicago area and the New Orleans area. The waterways are the dominant mode, accounting for 90 percent of the total tonnage and value moved, respectively. Truck is the second mode with a nine percent share of tonnage and value. Only a small fraction of the cereal grains is transported by rail. Truck movements are the secondary mode of transportation in the base case. Although trucks come with much faster delivery times, they do so at a much steeper price, and are generally used for short distances. However, trucks are an important part of the supply chain as they help consolidate grain shipments from farm to grain elevator or export terminals, and they also complement the waterways by bringing grain to ports. The input commodity flow information has limitations in the way this data is represented. Even with the above limitations, the model outputs were consistent with the findings from the industry outreach and from other research studies. Table C-2: Cereal Grain Tonnage and Value, Base Case (2007) Mode Tonnage (K-tons) Tonnage % Value ($M) Value % Water 1,396,429 90% 200,161,833 90% Rail 7,207 0% 1,034,400 0% Truck 146,311 9% 20,896,420 9% Air 0.256 0% 37 0% Total 1,549,947 100% 222,092,689 100% Source: CMAP model Disruption Case The disruption case simulates the movement of cereal grains between Chicago and New Orleans when waterway movements are not available. This scenario is not unrealistic. The effect of disruptions on the inland waterway system such as river closures and restrictions due to low water, high water, and lock maintenance or failures can lead to congestion, delays, spoilage, diversion to other transportation modes and ports, higher transportation costs, lost sales and lost market share.41 Shippers have several options under this scenario. If the waterways in Illinois are shut down, the first option preferred by shippers according to the interviews is to store the grain until the water movements become available again since this is the most cost-effective option. Modern grain elevators have improved their ability to store grain for long periods of time, allowing grain sellers to hold onto their product if they foresee a rise in market price. Another option is to dray the grain to another waterway, such as the Ohio River or through the Great Lakes. This method is 41 McGregor, Brian. A Reliable Waterway System is/ Important to Agriculture. February 2017. U.S. Dept. of Agriculture, Agricultural Marketing Service. Web. <http://dx.doi.org/10.9752/TS050.02-2017>

120 readily employed for droughts, floods and lock closures. Changing the port is easier than changing the mode of transportation or changing the buyer, which are the other two options available to grain sellers. In the case of bulk cereal grain shipments, the next most cost-effective option is by railroad. Several Class I railroads run north/south along the Mississippi River basin from Chicago to New Orleans. Both the waterways and the railroads provide efficiencies over long distances. Although the waterways have a cost advantage, the railroads compete on rates. The last option for the shipper is changing customers. Most of the grain in Illinois is corn, and corn can be used domestically for animal food production, ethanol production and many other food products. For our purposes, the only disruption scenario that can be modelled using the CMAP model is the shift of transportation modes. Storing the grain, changing the route, or finding a different buyer (destination) were not feasible due to the following reasons. First, the model does not model time-dependent trips. Second, modeling a route change requires trip assignment, choice set generation, and route choice modelling, which were beyond the scope of this task. Third, the origin and destination were fixed in the scope of the project. The CMAP model was manipulated to reflect the changes in cereal grain mode choice given disruptions to transportation modes. This corresponds to the hypothetical scenario above that assumes waterways are not available due to flood, drought or lock closure. To remove the waterways from the modal options available, the table of unit costs needs only to be adjusted upwards sufficiently to prevent waterway use. When doing so, it as expected that the next least expensive transportation mode (i.e., rail) would be selected. Disruption Case Setup and Results For the disruption scenario, we removed “inland waterway” as an option for the movement of grain (SCTG 2) originating from the study area and destined for New Orleans. The results of modelling the supply chain disruption for cereal grains matched the expected results. Error! Reference source not found.Table C-3 compares the scenario outputs of the base case with the disruption case. From this side by side comparison, we can see that the waterway movements shifted 100 percent from waterways to the railways. Considering how price sensitive the grain industry is to shipping costs, it was expected that the grain movements would switch to the next most affordable mode. Even through truck movements represented nine percent of the traffic in the base case, none of these truck trips shifted modes. This is expected as truck delivery is more expensive than both waterways and railways. Table C-3: Mode Paths used for Cereal Grains between Chicago and New Orleans, Percent Change between Base Case and Disruption Scenario Base Case (kilo-tons) % of Total Base Case Value % of Total Disruption Case (kilo- tons) % of Total Disruption Case Value % of Total Water 1,396,429 90% 200,161,833 90% - 0% - 0% Rail 7,207 0% 1,034,400 0% 1,403,636 91% 201,196,233 91% Truck 146,311 9% 20,896,420 9% 146,311 9% 20,896,420 9% Air 0.256 0% 37 0% 0.256 0% 37 0% Total 1,549,947 100% 222,092,689 100% 1,549,947 100% 222,092,689 100% Source: CMAP model

121 Conclusions The modelled disruption can represent many possible scenarios that cut off the waterway transportation of cereal grains from Chicago to New Orleans, including flooding, drought-lowered water levels and lock closures. The results of the model match the expected outcome. These results imply that when waterways are not available, railways would be the most likely used substitute mode. Rail transportation itself is prone to types of disruptions that, in some cases, might be the same as those affecting the waterways such as massive flooding. In such cases, shippers would have to decide whether to store the grain (something as noted that could be a feasible consideration) or to find alternative transportation via more circuitous routes (for example, still using rail but using network paths that bypass affected regions). A recurring theme in using the CMAP model was the appropriate use and application associated with the model itself. The CMAP model is mostly a long-range planning tool, verified and validated to movements as close as possible to what exists currently (or to some base year). Then, projections for the future, such as the increase or decline in commodity volumes, are combined with other projections, like employment changes and expected changes to land use, to approximate future flows based on extrapolation of the present conditions. It is designed to forecast freight flows moving to, within, and out of the Chicago Metropolitan Area. It must be used with care and caution when used for short-term planning such as disruption modelling. The other limitation to the model, as used in this study, is that it only modelled transportation mode shifts. In developing the final trip tables, an equation is used employing constant parameters based on empirical research. It seems likely, therefore, that using the model for such things as changing flow destinations or origins (one possible response strategy to a disruption) could require changes or extensions to the model that would require a new verifiable and validated model calibration. Such an effort was beyond the scope of this task. Other possible responses to a disruption, waiting and storing the grain, switching ports and waterways to detour around the waterway disruption, or changing buyers and subsequently shipment destinations, cannot be modelled without considerable additional work or by essentially developing a new model. Each of these other possible responses have complications that are related to firm behavior economics. It is also likely that the CMAP model does not capture all aspects of response behavior to shocks to the system. For example, without competition from water transportation, railroads would likely raise prices thus increasing the transportation costs to agricultural shippers (studies have shown that not surprisingly rail prices are lower in areas where the railroads compete on rates with the waterways). If the waterways were not available, the railway prices would be predicted to rise, which could make switching to railways no longer an attractive option. Since the model is not structured to show rail rates increasing in response to loss of a competitor, this aspect is not automatically represented in the model (although such could be modeled with more study of how much higher rail costs would likely rise).

Next: Part 2: Guidance for Stakeholders to Mitigate and Adapt to Disruption on Supply Chains »
Freight Transportation Resilience in Response to Supply Chain Disruptions Get This Book
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TRB’s National Cooperative Freight Research Program (NCFRP) has released a pre-publication version of Research Report 39: Freight Transportation Resilience in Response to Supply Chain Disruptions. The report provides guidance to public and private stakeholders on mitigating and adapting to logistical disruptions to supply chains resulting from regional, multi-regional, and national adverse events, both unanticipated and anticipated.

The report, which makes a significant contribution to the body of knowledge on freight transportation and system resiliency:

(1) assesses research, practices, and innovative approaches in the United States and other countries related to improving freight transportation resiliency;

(2) explores strategies to build relationships that result in effective communication, coordination, and cooperation among affected parties;

(3) identifies factors affecting resiliency;

(4) analyzes potential mitigation measures;

(5) characterizes spatial and temporal scale considerations such as emergency planning and response timeframes;

(6) prioritizes response activities by cargo types, recipients, and suppliers;

(7) identifies potential barriers and gaps such as political boundaries, authorities, ownership, modal competition and connectivity, and social and environmental constraints; and

(8) examines the dynamics of supply chain responses to system disruptions.

The report also includes a self-assessment tool that allows users to identify the current capability of their organization and institutional collaboration in preparing for and responding to supply chain disruptions.

Disruptions to the supply chain and their aftermath can have serious implications for both public agencies and companies. When significant cargo delays or diversions occur, the issues facing the public sector can be profound.

Agencies must gauge the potential impact of adverse events on their transportation system, economy, community, and the resources necessary for preventive and remedial actions, even though the emergency could be thousands of miles away.

Increasing temporary or short-term cargo-handling capacity may involve a combination of regulatory, informational, and physical infrastructure actions, as well as coordination across jurisdictional boundaries and between transportation providers and their customers.

For companies, concerns can include such issues as ensuring employee safety, supporting local community health, maintaining customer relationships when products and goods are delayed, and ultimately preserving the financial standing of the company.

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