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61 CHAPTER 5: ANALYSIS TOOLS AND MODELS FOR SUPPLY CHAIN RESILIENCE 5.1 INTRODUCTION An important component of anticipating and addressing the characteristics of supply chain disruptions is understanding freight flows and how they might respond to changes in network availability, and to quantify the costs versus benefits of proposed remedial actions. This is especially relevant where damage to the nationâs transportation infrastructure is widespread and the loss of connectivity is protracted. This chapter reviews the application of analysis tools and models to derive a deeper understanding of transportation network disruptions and their influence on freight network resilience. In particular, the use of supply chain modeling of freight movements is investigated. While to date such supply chain models have only been developed for a small number of planning agencies, they offer a significant improvement in the modeling of transportation choices, including the sort of choices available to freight carriers faced with significant disruptions to the nationâs transportation networks. To orient the reader at the outset, supply chain freight models assume the origin of the freight is the shipper and the destination is the receiver of that freight thus including all the individual movements that are part of the overall trip. Supply chain models are used to model freight trips by each mode (e.g., truck, rail, barge, aircraft) and link all these trips into an origin-destination (O-D) supply chain. When a change of mode occurs, this may involve costly cargo transfer activities via truck, rail, water, or air transportation via intermodal terminals, or in the case of trucking between long-haul and local freight consolidation and break-bulk facilities. Choosing a supply chain in these models means allocating freight based on the utility of each supply chain option available to a specific O-D pair. Hence a supply chain might include the shipment of cargo by truck to a rail terminal, then long haul by rail to another rail terminal, followed by a truck trip to deliver that cargo to its destination. The choice of the supply chain is determined by 1) the utility of each modal link, or trip, in the supply chain, and 2) the utilities of handling (e.g., time and cost) at the transfer points, or nodes between modes along the supply chain. What is maintained throughout the entirety of the supply chain is the nature of the cargo being transported. The choice of supply chain may also require certain attributes of the freight cargo (e.g., the effect of shipment size, shipment frequency, cargo fragility, etc.) be known. This approach is different from trip-based models, which usually maintain a single mode for the long-distance freight movement and treating the transfer between modes as the origin of a new trip, typically as a special freight generator.
62 5.2 TRADITIONAL TRIP BASED FREIGHT PLANNING MODELS For most regional planning authorities, the modeling of freight movements means developing a long-range (e.g., a 30-year) plan using a well-established âfour-stepâ modeling approach geared to the estimation of 1) location-specific freight production and consumption, and its translation into 2) O-D commodity flow volumes, 3) mode selections, and 4) mode-specific route choices. Over the past quarter century, several regional planning agencies have used one or more of the above modeling steps to assess the impacts of disruptions to freight movements, usually by considering both supply and demand aspects of disruption modeling within a specific corridor or class of commodity. Most applications have been for disruptions to the nationâs highway system (for example Chang et al, 2010; Unnikrishnan and Figliozzi, 2011; the papers in Taylor, 2012; Chen et al, 2017; Du et al, 2017; Ashrafi et al, 2017; Mesa-Arango et al, 2017), with a few papers also considering disruptions to inter-regional and multi-modal ground (i.e., road-rail- waterway) networks (for example, Kim et al, 2002 and Ham et al, 2005). Dong et al (2015) use a multimodal freight transportation network model to simulate commodity movements and evaluate the impacts of hypothetical Midwestern highway, waterway and rail transport disruptions. For rail, the models used a fluid-based dynamic queuing approximation to estimate the delays at classification yards and waterway locks caused by network disruptions). See also Jalic (2015) for a review of large-scale disruptions to air transport networks (where a good deal of cargo is carried as âbelly freightâ in passenger aircraft), and a post event example of modeling the impacts of the 2012 Superstorm Sandy. In nearly all cases, the emphasis to date has been on simulating the physical responses of freight movements to infrastructure-damaged networks, notably road networks. Most studies have also tended to focus on assessments of specific aspects of resilience, examining such characteristics as transportation network redundancy (the provision of excess network carrying capacity), diversity (alternative routes and modes), and responsiveness/adaptability to changing conditions (the substitution, temporarily or on a more protracted basis, of alternative sources, markets, and in theory at least, the substitution of one good for another). Most of these modeling efforts are âwhat-ifâ scenario generation studies. Reggiani et al (2015) call this analysis focus non-tested phenomena, i.e. pre-disruption simulation of events that can potentially occur, as opposed to after-disruption event case studies in which resilience or vulnerability measures are directly applied to real-world data. Mattsson and Jelenius (2015) also find that past network disruption studies have tended to focus on how to anticipate and plan/design for more robust-to-disruption networks, with less attention given to modeling the response and recovery phases after a disaster. Recognizing that widespread and prolonged disruptions to a regionâs transportation networks can have large indirect economic impacts that can spread far beyond the region and the industries directly involved, several network disruption studies have combined or linked a freight planning model with an inter-industry economic impact model. For example, Kim et al (2002) and Ham et al (2004) used an inter-industry, inter-regional input-output (I-O) modeling approach to simulate the response to a hypothesized earthquake in the New Madrid Seismic Zone in the center of the U.S. Ivanov et al (2008) also used the IMPLAN I-O model to capture the economic impacts of storm-related damage to the I-5 and I-90 Washington State highway corridors in the winter of 2007-08, but combined in this instance with ex post data from a statewide survey of the disruption experience by some 2,758 affected trucking firms and freight-dependent businesses. Tatano and Tsuchiya (2008) provide an example of integrating a spatial computable general equilibrium (SCGE) model with a transportation model that can estimate the traffic volumes of freight (and passengers). The model, applied to the large Niigata-Chuetsu earthquake of 2004 in Japan, considered the damage to transportation infrastructure and indicated the extent of regional economic losses arising from the earthquake because of disruptions in both intra- and inter-regional trade.
63 5.3 BEHAVIORALLY BASED, SUPPLY CHAIN MODELING OF NETWORK DISRUPTIONS While important for infrastructure utilization, land use planning, and traffic congestion studies, the traditional flow- aggregated and trip-based approach to freight modeling has received extensive criticism for its lack of behavioral realism. Comparatively little effort has been given to incorporating the responses of the different freight actors involved in goods movement, including the shippers, carriers, receivers, warehouse operators and brokers of goods contracts. This weakness affects our ability to model freight agency responses to sudden, catastrophic, and protracted disruptions to the nationâs transportation infrastructure. As numerous authors have pointed out, the increased use of global supply chains can spread the impacts of major network disruptions quite broadly, both geographically, economically, and ecologically. These supply chains can also involve a significant number of different freight agents. A long-distance goods shipment may require more than one shipment leg, making use of a cargo consolidation or break-bulk and transfer facility, possibly involving a cross-docking activity or transferring cargo between two different modes of transport (e.g., via truck-rail, rail-water, or truck-air terminals). A disruption to one of the modes might affect the use of another, and planning models need to be able to recognize which situation is likely to exist and how best to respond to it. As more modes, transfer facilities, and freight agents become involved in producer-to-final customer deliveries, there is more opportunity for different supply chain solutions to emerge. Understanding both the movement options offered and the impacts of network disruptions brings planners closer to understanding both the resilience (or lack thereof) that currently exists in our transportation networks, and the likely impacts of taking specific actions towards system recovery. An extensive enterprise-centric supply chain literature on the topic of disruptions exists, including a wide range of concepts and methods for building resilience into cargo delivery systems (for useful recent reviews see Klibi and Martell, 2010; Snyder et al, 2014; Mattsson and Jelenius, 2015; Elleuch et al, 2016; Ivanov, et al, 2017; and Rosyida et al, 2018). However, as Mattsson and Jelenius (2015) point out, this sort of modeling involves the collection and use of a great deal of data. This not inconsiderable challenge, from a technical perspective, means finding ways to capture disaggregate, microeconomic activity data within necessarily aggregate, macro-analytic freight planning models. In working towards such a goal, a still limited but growing number of planning agencies have begun to explicitly incorporate behavioral elements of supply chain management into their agency models. However, agencies are still far away from explicitly incorporating appropriate financial/ transactional, informational, logistical and physical factors of supply chain management as indicated earlier. When combined with improved reporting and tracking of the physical aspects of natural and man-made network disruption events (causality, geographic extent, timing and duration), the following three modeling directions promise a more comprehensive approach to the modeling of planning for, responding to, and recovering from major disruption events. 1. The use of agent-based modeling to capture the major factors considered in the decision making of different supply chain participants (agents) to bring some freight business-based realism into the modeling process that leads up to the freight activity patterns we see in practice. This includes how these decisions translate into how (mode), when (schedule), where from (origin) and where to (market) goods are shipped, and which supply chain options are used to bring these decisions together. 2. The use of micro-simulation techniques to simulate and subsequently aggregate individual shipments to an inter-zonal (corridor or O-D channel) level of geography for network (congestion impacting) assignment. This allows for freight agent decision making to influence shipment load size, vehicle type, mode and route choice, including both single mode, direct dyadic connections as well as multi-leg and multimodal use of intermediate, distribution center/warehouse/terminal/port-based supply chains.
64 3. The fusion of a variety of data sources, running the gamut from remotely-collected digital datasets to expert interviews. The data collected includes characteristics of goods and freight traffic movements and costs (from federal, state and local surveys), private sector-purchased transactions, digitally based âbigâ datasets (including GPS, RFID, cellular, bar-code, and traffic counter datasets), and the use of Delphi focus group responses with shipper, carrier, customer, and (as yet largely untapped) freight broker interviews. All these efforts aim at understanding business responses to both past and potential future disruptions to freight networks. European modelers led early developments in this type of modeling. Roogra et al (2010) described some of these modeling efforts, while more recent model applications are described in, for example, (Cavalcante and Roodra ,2013; Holmgren and Ranstedt, 2017; Ben-Akiva and de Jong, 2013; Burgholzer et al, 2013; and Reis, 2014). Indeed, the state-of-the-art in freight modeling is still undergoing development even at the enterprise level. An early attempt at enterprise-level modeling was conduct by Nagurney et al (2002) who used a multi-level network formulation based on the solution of a variational inequality. Subsequent effort by Xu et al (2003) integrated the physical components of the transportation network with appropriate components of the broader business logistics network, financial network, and information network using micro simulation and GIS tools to address freight transportation problems within a much broader decision-making and policy sensitive environment than current models. Bringing similar ideas into regional or network-wide planning models, and finding ways to access the necessary data sources, represents a task for future research. Southworth (2018) provides a brief summary of similar model developments in the U.S., pointing out the relatively recent emergence of actual modeling applications by planning agencies There are only a handful of such model applications in use by metropolitan planning organizations (MPOs) and state DOTs, including: 1) the Chicago Metropolitan Agency for Planning (see RSG Inc. Cambridge Systematics, et al, 2015, also Gliebe et al. 2013, and Outwater et al. 2013); 2) Mariposa Association of Governments (MAG) for freight movements within the Phoenix and Tucson megaregion (see Cambridge Systematics, Livshits et al, 2017; Stinson et al, 2016, and Hong et al, 2017); 3) Puget Sound Regional Council (PSRC) in Seattle and 4) Florida, Maryland and Wisconsin DOTs (See Cambridge Systematics, 2016a). It is important to understand that all these U.S. MPO and DOT supply chain freight models rely on the origin- destination commodity tables from the FHWA Freight Analysis Framework (FAF), which is based on the National Commodity Flow Study (CFS). Both the FAF and CFS are based on national surveys and therefore have limitations and complexities that will impact model analysis. However, they can be used to model supply chain disruptions, as demonstrated in this study. Another important consideration was identifying candidate supply chains as part of this research that would be reflected in the FAF data from origin to destination. For example, the FAF does not recognize seaports for international outbound or inbound flows since the FAF is a compilation of domestic transportation movements. Therefore, supply chains connected to ports were not selected for modeling. In addition, the study team evaluated the other candidate supply chains to be sure FAF data were documented for that movement. Taking into consideration these limitations, the two existing models were chosen for illustrating how freight models could be used for understanding freight resiliency. They included the (MPO level) Chicago Mesoscale Freight Model developed for the Chicago Metropolitan Agency for Planning (CMAP) and FreightSIM developed by RSG, Inc. for the Florida Department of Transportation (FDOT).
65 5.4 CASE STUDY: THE PHARMACEUTICAL SUPPLY CHAIN FROM FLORIDA TO TEXAS This case study focused on the pharmaceutical supply chain from Miami, FL to Houston, TX (see Figure 4). The pharmaceutical industry is highly consumer-driven as manufacturers strive to match production volumes with consumer demand. Small profit margins, operational costs, and market fluctuations have encouraged many pharmaceutical suppliers to adopt a âjust-in-timeâ delivery strategy. Because the availability of drugs to patients heavily relies on the supply chain, catastrophic consequences could follow if the supply chain fails. Pharmaceutical stakeholders need to be proactive regarding both anticipating a disruption event and developing protocols to minimize the negative effects of a disruption. Theoretically, pharmaceutical distributors have the option of changing the transportation mode, changing the transportation route, or changing the business partner in the event of a disruption. The assumption in this analysis is that the distributor can only change the transportation mode and not the route or business partner. The FreightSIM model was manipulated to simulate a disruption in the flow of pharmaceuticals between Miami and Houston. Outputs from the program were analyzed and tested against information gathered through industry outreach and internet searches. Pharmaceuticals are shipped between Miami and Houston primarily by air and truck. Outputs from the model confirmed this mode split for the majority of the tons before the disruption was simulated. The model estimated that an interruption in the air mode would shift most of the tonnage to truck with a minimal amount likely to be shifted to rail. 5.4.1 About FreightSim and the Florida Statewide Model âFreightSimâ is a travel demand model component integrated into the Florida Statewide Model (FLSWM). The model simulates the transport of freight between supplier and buyer businesses in the U.S., focusing on movements that include movement in Florida. FreightSim produces a list of commodity shipments by mode and converts these to daily vehicle freight truck trip tables that can be assigned to national and statewide networks in the FLSWM along with trip tables from the passenger model.
66 Figure 4: Origin-Destination Pair for Pharmaceutical Supply Chain Scenario FreightSim is designed to be a policy-sensitive freight model that can be used to: ï· Inform infrastructure investment decisions ï· Evaluate congestion on Florida highways ï· Test the effectiveness of statewide transportation policies on mobility and the economy ï· Produce multimodal system performance measures for freight ï· Evaluate the impacts of private sector decisions on the state transportation system ï· Provide regional agencies with intercity freight travel options for regional planning purpose The model uses FAF Version 3 (FAF3) data along with other data such as Florida employment data as inputs into freight flow forecasting. FAF3 provides origin-destination commodity flow data by 2-digit Standard Classification of Transported Goods (SCTG2) for all the U.S. Forty-four (44) commodity groups in FAF3 data are represented by SCTG2 codes. These commodities range from Live Animal/Fish (SCTG2 #01) to Mixed Freight (SCTG2 #43). Pharmaceuticals are represented by SCTG2 #21 in the FAF3 dataset. FreightSIM is built in the âRâ programming language, a computer programming platform with robust statistical analysis capabilities. The model consists of seven major modules that work sequentially. These modules, called âstepsâ in the model (based on their order of application), include: 1) firm synthesis, 2) supplier selection, 3) FAF flow apportionment, 4) distribution channel, 5) shipment size, 6) mode path selection, and 7) trip table preparation. In order to run the entire model, the steps must be performed in sequence.
67 The model utilizes pre-defined mode combinations to determine what modes are used to transport freight between each OD pair. These mode combinations are called âmode paths.â For example, a "truck-air-truck mode path" means cargo is shipped by truck from its origin to a nearby airport, shipped by air to another airport near the destination, and shipped by truck from that airport to the destination. Similarly, a "direct truck mode path" means that the cargo was shipped by only truck from origin to destination. There are 18 mode paths built into the model including: direct truck, truck-air, truck-rail, truck-intermodal (e.g., an intermodal container movement by truck, either connecting to an intermodal yard associated with a railroad, port or inland port) and others. FreightSIM produces many intermediary and final outputs. Intermediary output files are produced as a result of running each step, while final output files are produced after all steps are finished. Some of the major outputs of the model include tons and values by mode and by direction, tons and values by shipment size and direction, and number of truck trips between each OD pair. Analyzing a disruption scenario requires changes to one or more of the intermediary files. 5.4.2 Base Case The base case in this analysis produces the outputs of the model when supply chains are not disrupted. The base base tonnages and values were from the 2007 FAF3 database. Table 3 illustrates the base case for pharmaceuticals transported between Miami and Houston. The tonnage and value estimates in this table include both directions of the commodity flow (i.e., Miami to Houston and vice versa). As seen, truck is the dominant mode, by both tonnage and value, accounting for 63 and 58 percent of the total tonnage and value moved, respectively. Air is the second most common transportation mode with a 33 and 39 percent share of tonnage and value, respectively. Only a small fraction of the pharmaceutical flow is transported by rail. Table 3: Total Pharmaceutical Tonnage and Value between Miami and Houston Origin-Destination Pair, Base Case (2007) Mode Tonnage (K-tons) Tonnage % Value ($M) Value % Air (truck-air-truck) 99 33% 7,139,305 39% Rail (truck-rail-truck) 11 4% 601,631 3% Truck 189 63% 10,578,271 58% Total 299 100% 18,319,206 100% Source: FreightSIM model Table 4 shows the flow of pharmaceuticals from Miami to Houston. Comparing this table with Table 3 reveals that the overwhelming majority of the pharmaceutical flows between Miami and Houston are in the Miami-to-Houston direction. Only nine thousand tons of pharmaceuticals, valued at $2 million, were going from Houston to Miami. The mode split, however, is very similar to that of Table 3. Trucks dominate the tonnage and value, accounting for 65 percent of the total for both, while air accounts for 31 percent of the total in both tonnage and value. Table 4: Pharmaceutical Tonnage and Value from Miami to Houston, Base Case (2007) Mode Tonnage (K-tons) Tonnage % Value ($M) Value % Air (truck-air) 90 31% 5,049,924 31% Rail (truck-rail) 11 4% 601,631 4% Truck 189 65% 10,578,271 65% Total 291 100% 16,229,826 100% Source: FreightSIM model
68 Interviews with industry stakeholders suggested that that just over half of the pharmaceuticals transported to Houston from Miami are transported by air. At first glance this seems to contradict the findings from the model base case. A closer look, however, reveals that the share of truck in both tonnage and value is twice that of air. Considering that all air cargo requires truck transport for the first and last mile, and given that the shippers responding to the US CFS that underpins the FAF do not always know the line-haul mode used, it seems likely that a good deal of the value and tonnage reported by truck-only is actually delivered using a combination of truck first mile-last mile and airline-haul transport. If this is indeed the case, the model outputs are consistent with the findings from what was learned from talking to industry representatives (and shows the value of a supply chain-based approach). Choosing and defining a mode disruption proved to be a challenging proposition, involving both removing each mode one at a time. Although this might seem reasonable at first, there are legitimate reasons not to simulate a disruption for all the modes and thereby simplify the process. First, the truck mode cannot be removed from the model because both air and rail use truck for the first and last mile of the trip. FreightSIM uses mode paths to simulate the transportation of the goods, and at the end aggregates the tonnage and value of each mode path by mode. If âtruck- airâ and âdirect truckâ are considered as the available mode paths, the amount of tonnage and value transported via truck-air and direct truck are part of the estimated output; total tonnage and value are aggregated on truck and air. This results in adding the tonnage and value of truck associated with "truck-air" to those of the direct truck shipment. Even if "direct truck" was removed from the model, the output would still show values for truck which would belong to "truck-air." Second, simulating rail disruption is not worthwhile given that only a tiny fraction of the pharmaceutical flows is moved by rail. This leaves air as the only viable option for disruption modelling. 5.4.3 Disruption Case In order to model a disruption in the supply chain, changes were made in the mode path selection step of FreightSIM. These changes included removing a mode path option for the pharmaceuticals shipped between the Miami and Houston areas. Supply chain disruptions can have different implications for a freight carrier based on factors such as the commodity type and nature of the business. For a minor disruption such as a blocked roadway, the carrier could use an alternative route. For a more serious disruption, the carrier may have to use another mode. For instance, truck transport could be a viable option to air if the airport system itself is shut down due to a terrorist attack (a single airport shutdown might cause shippers to truck the product to a nearby airport if the value of the product is such that fast delivery is necessary).8 A much more severe disruption could force the carrier to change its destination or consumer. For example, a shipment of pharmaceuticals can be transported to Miami instead of Houston if Texas is expecting a hurricane to make landfall. This occurred in 2017 for a pharmaceutical third-party logistics provider during Hurricanes Harvey and Irma. For the purposes of this study, a second disruption scenario was modeled to supplement the analysis. Changing the route or destination was not considered feasible because: 1) modeling a route change requires trip assignment, choice set generation, and route choice modelling modules which were beyond the scope of this task, and 2) the origins and destinations were fixed in the scope of the analysis. FreightSIM was manipulated to reflect the changes in pharmaceutical mode split if a transportation mode was disrupted. This corresponds to the second scenario above that assumes the air mode is not available due to an airport shutdown. 8 As reported in FAF, all the flows were either by truck-air-truck or totally by truck. While an alternative movement could conceivably use truck-rail-truck or truck-water-truck, these were supply chains that were never selected in the database. All supply chains require some truck usage. If any truck usage is allowed, and when the airline haul is disrupted, then "all truck" is the only option available (according to the model).
69 In freight modelling, a mode path is selected based on the logistics cost associated with the use of that link. Intuitively, mode paths with low logistics costs carry more volume compared to mode paths with higher costs. FreightSIM uses logistics cost functions to predict the path and mode of long-haul movements of freight into, within, and out of Florida. The logistics cost function has the form (De Jong and Ben-Akiva, 2007): Total Logistics Costs = constant value + (ordering + transport + damage + inventory in transit + carrying + safety stock) costs The constant in the formula above represents all the hidden costs that cannot be accounted for in the model. The FreightSIM mode path selection step provides the change in mode split after a disruption is simulated. In order to discourage the use of a mode path in the model, a high number was assigned to this constant value for the truck-air mode, causing the âdirect truckâ mode to become the more cost-effective shipment option. Table 5 illustrates the mode split after a disruption in the "truck-air" mode was simulated between Miami and Houston in both directions. As observed, the direct truck mode is dominant, accounting for 87 and 86 percent of the tonnage and value, respectively. Rail carries the remaining flows, 13 and 14 percent of tonnage and value, respectively. Table 5: Total Pharmaceutical Tonnage and Value between Miami and Houston Origin-Destination Pair, with Disruption (2007) Mode Tonnage (K-tons) Tonnage % Value ($M) Value % Rail 39 13% 2,539,830 14% Truck 260 87% 15,779,376 86% Total 299 100% 18,319,206 100% Source: FreightSIM model Table 6 shows the mode split of pharmaceutical flows from Miami to Houston after disrupting "truck-air." Similar to the table above, truck is the dominant mode carrying 87 percent of both tonnage and value while rail accounts for the remaining 13 percent. Table 6: Pharmaceutical Tonnage and Value from Miami to Houston, with Disruption (2007) Mode Tonnage (K-tons) Tonnage % Value ($M) Value % Rail 37 13% 2,067,908Â 13% Truck 254 87% 14,161,917 87% Total 291 100% 16,229,826 100% Source: FreightSIM model 5.4.4 Results of the Analysis Overall, the results from the model analysis are intuitive. Pharmaceuticals are primarily transported by air and truck. Once air is not an option due to a disruption, truck is the best alternative since it is the most cost feasible means of transportation compared to rail and water. The model illustrates this in Table 5 and Table 6. These tables also show some volume and value increase for rail. This is expected since FreightSIM will not allocate all the air cargo to truck and assigns small portions of it to rail as well. Table 7 shows the mode split results for the base case (i.e., before the disruption) and the disruption scenario (i.e., after the disruption in "truck-air") between Miami and Houston area for both directions. Pharmaceutical distributors favor truck as the main mode of transportation for pharmaceuticals over other modes when air is disrupted. The truck share of tonnage and value increases by 24 and 29 percent, respectively, after the disruption. The share of "truck- rail," on the other hand, increases much more moderately with only 9 and 10 percent increase in tonnage and value, showing the inability of "truck-rail" to compete with "truck."
70 Table 7: Pharmaceutical Tonnage and Value between Miami and Houston, Base Case and Disruption Scenarios, Both Directions (2007) Mode Tonnage (Ktons) Tonnage % Value ($M) Value % Base Disruption Base Disruption Base Disruption Base Disruption Air (truck-air) 99 N/A 33% N/A 7,139,305 N/A 39% N/A Rail (truck-rail) 11 39 4% 13% 601,631 2,539,830 3% 13% Truck 189 260 63% 87% 10,578,271 15,779,376 58% 87% Total 299 299 100% 100% 18,319,206 18,319,206 100% 100% Source: FreightSIM model Similar to the table above, Table 8 shows before and after disruption results for pharmaceuticals transported from Miami to Houston area. As expected, truck dominates the mode split before and after the disruption. Moreover, truck is favored over truck-rail to carry the cargo that could not be shipped by air due to the disruption. In both tonnage and value, truck share increases by 23 percent while truck-rail share increases by only 9 percent. Table 8: Pharmaceutical Tonnage and Value between Miami and Houston, Base Case and Disruption Scenarios, One-Way (2007) Mode Tonnage (Ktons) Tonnage % Value ($M) Value % Base Disruption Base Disruption Base Disruption Base Disruption Air (truck-air) 90 N/A 31% N/A 5,049,924 N/A 31% N/A Rail (truck-rail) 11 37 4% 13% 601,631 2,067,908 4% 13% Truck 189 254 65% 87% 10,578,271 14,161,917 65% 87% Source: FreightSIM model The model results indicate that pharmaceutical distributors will primarily use truck for shipping their products if something disrupts the air mode. A few of the shipments are sent to rail. The model outputs are consistent with the results of the industry representative interviews. In the case of pharmaceutical shipments, third-party logistics providers constantly evaluate the decision to transport shipments by truck or air daily and make decisions for mode choice based on cost, time, airport delays, weather, delayed shipments further up the supply chain, and the like. âMore truck capacityâ is not usually a factor; much more emphasis is given to âcost and time.â Supply chain models address long-term planning impacts of changes in the transportation system and have not been developed as short-term operational models. This is one of the primary drawbacks of using a long-term planning model for a short-term disruption, although travel demand models have been used to analyze the network routing impact of short-term disruptions in the network. While the flows that move by air are expected to move to airports, FAF regions may include many airports. The FAA only reports the total tons moving through the airports and not the tonnages by commodity. Therefore, it is necessary to either assume that the commodity flows will utilize airports in proportion to the total tonnage carried, or search for additional and difficult-to-find datasets. The FreightSIM model forecasts the allocation of freight flows among supply chains but does not report âmultiple modesâ as an output. The forecast in the FAF only varies the flows to and from regions by commodity. The allocation among modes and supply chains for each flow is that which is reported in the base year. From the FAF, assuming the flows reported by âmailâ are moving by air, the flows are almost evenly allocated between truck and air supply chains. For the purpose of âvalidatingâ outputs, given the disruption of the pharmaceutical air supply chain between Miami and Houston, we did assume that âmultiple modes and mailâ is in fact âairâ (see Table 9).
71 Table 9: FAF Mode Descriptions for Pharmaceutical Disruption Scenario Mode IDÂ Mode Description Remarks 1 Truck Includes private and for-hire truck. Private trucks are owned or operated by shippers and exclude personal use vehicles hauling over-the-counter purchases from retail establishments. 2 Rail Any common carrier or private railroad. 3 Water Includes shallow draft, deep draft and Great Lakes shipments. 4 Air (include truck-air) Includes shipments typically weighing more than 100 pounds that move by air or a combination of truck and air in commercial or private aircraft. Includes air freight and air express. Shipments typically weighing 100 pounds or less are classified with Multiple Modes and Mail. 5 Multiple modes & mail Includes shipments by multiple modes, parcel delivery services, U.S. Postal Service, and couriers. This category is not limited to containerized or trailer-on-flatcar shipments. 6 Pipeline Includes shipments by pipeline and from offshore wells to land. 7 Other and unknown Any mode not included within the other mode definitions and unknown modes of transport. 8 No domestic mode Applies to some intra-zonal movements of imports Source: FAF3 Documentation Having to make such assumptions is a limitation of the model as applied here, in that it can only model transportation mode shifts. Going deep into the model to change the destinations or origins of a flow would require changes to the model that would be difficult to verify. Another application of a large-scale freight model for the grain supply chain from Illinois to New Orleans case study is found in Appendix C. 5.5 EVALUATING FREIGHT FLUIDITY In addition to examining freight supply chain models, the study team examined the concept of freight fluidity, especially as it was developed as part of a recent I-95 Corridor Coalition Freight Performance White Paper (Cambridge Systematics and WSP, 2016b). Freight fluidity refers to measuring supply chain performance across multiple jurisdictions using travel time, travel time reliability and cost measures. For example, one of the case studies undertaken as part of the I-95 Corridor Coalition Freight Performance White Paper evaluated the soybean supply chain between Peoria, IL and New Orleans, LA and identified data sources and potential freight fluidity performance measures that could be used to monitor the performance characteristics of the trip. The concept of disruption risk was explicitly included in the analysis. The modeling approach from the I-95 Corridor Study was at a much higher level than the modeling described in this final report, but it did provide useful insights about the participants (agents) that would be involved in this supply chain, and the impact on performance of disruptions anywhere along the supply chain. Freight fluidity can be a measure of the supply chain performance of a single or multiple modes of freight transportation. A freight network or freight corridor serving many supply chains can also use freight fluidity as a measure of the performance. The main elements of freight fluidity are travel time and reliability, travel cost, risk and safety. The risk element of freight fluidity consists of five different types of risk---operational risk, institutional risk, disruption risk, acceleration risk and deterioration risk. The analysis described earlier focused on the disruption risk and its implications for grains and pharmaceuticals supply chains, and thus has only covered one of the five risks associated with freight fluidity. However, to illustrate how freight fluidity measures could be formulated for freight flows, the following section describes the approach used in the I-95 Corridor Case Study.
72 5.5.1 Freight Fluidity Measures In general, freight fluidity describes how well a physical supply chain performs in a freight transportation network. Different transportation performance measures are considered in order to answer questions such as: how well are the links and nodes on the network operating? Where are the bottlenecks in the supply chain or freight network? How reliable is the network in terms of transportation costs? How well do supply chains and the freight system react to disruptions? What are the chances of the cargo damage or cargo lost on the network? To answer such questions freight fluidity is measured in terms of travel time, travel reliability, travel cost, travel risk and safety. Each is discussed in more detail below. Travel Time and Travel Reliability: The I-95 Corridor Freight Performance Measurement study found that the first three of these measures, i.e., travel time, travel time reliability and travel cost, can be measured using available public and private data. Travel time is defined as how long it will take to ship cargo from origin to destination. Roadway travel time data is available through the National Performance Management Research Data Set (NPMRDS), American Transportation Research Institute (ATRI) GPS data and Google Maps. Commercial data vendors such as Transcore and RSI Logistics provide rail travel time data. Railinc also has rail travel time data but it is not a commercial data vendor. The Nationwide Automatic Information System (NAIS) is a high-quality, public source of travel time on inland and coastal waterways collected by the U.S. Coast Guard and made available through the US Army Corps of Engineers. FHWA defines travel time reliability as the extent of unexpected delay, or âthe consistency or dependability in travel times, as measured from day-to-day and/or across different times of the day.â Once travel time is available, travel time reliability can be calculated using different formulas, including planning time, planning time index, buffer time and buffer time index. The use of each formula is subjective and case dependent. Travel Cost: Travel cost is an integral component of freight fluidity since it reflects not only the shipping fees but also delay, unreliability, and wasted fuel on the network. Cost data can be purchased from private suppliers. In almost all cases, the cost data provided by shippers, receivers, and freight carriers are aggregated by a third party so that confidential shipper and carrier information is protected. Truck shipping rates can be purchased from Chainalytics, which is a consortium of Fortune 100-sized shippers. Regarding rail cost data, Chainalytics and STB Rail Carload Waybill Sample provide data for purchase. The U.S. Department of Agriculture (USDA) Marketing Services can be used for barge cost data. Other publicly available data sets include: 1. A CD containing Uniform Railroad Costing System (URCS) software can be downloaded for free and used to estimate rail carload costs. Go to: https://www.stb.gov/stb/industry/urcs.html. 2. Detailed representative long-haul trucking costs (O&M, fuel, labor, etc.) are reported annually by ATRI See: http://atri-online.org/ 3. The US DOTâs Intermodal Transportation and Inventory Costing Model State Tool (ITIC-ST) is another publicly available software tool. It can be used to examine shipment details between specific origins and destinations, also considering intermodal transfers between truck and rail. See: ITIC-ST, https://www.fhwa.dot.gov/policy/otps/061012/iticst_info.cfm These may all be âmodel-derivedâ costs but are all based on large empirical datasets that can, and have been, used in modeling flows.
73 Travel Risk: Quantifying travel risk is more challenging. Risks affecting product integrity, loss, damage and theft, as well as incorrect item counts are routinely monitored by freight shippers, receivers, carriers and their insurers, but this data generally are not made public and may not be compiled outside the respective organizations. Five different types of risks are identified: Operational Risk: this category encompasses immediate challenges to daily supply chain performance brought about by traffic congestion, weather, work zones, customs hold-ups, truck shortages, etc. Institutional Risk: This category covers risk to supply chain performance from uncertainties in the implementation of improvements, brought about by delayed action by federal, state, and local agencies. Disruption Risk: This category describes infrequent but possibly serious supply chain disruptions, such as those caused by major storms, tornadoes, earthquakes, infrastructure failure, labor disputes, political actions and wars. Analytical tools were used to forecast the reaction of grains and pharmaceutical supply chains to this type of risk. Acceleration Risk: This category encompasses conditions that may grow much worse rapidly. The typical example is a phase transition or state change in traffic flow, where a roadway incident turns slow moving traffic into gridlock. Deterioration Risk: This category covers conditions that gradually grow worse, causing performance to decline over time. Worsening congestion on roadways is the obvious example, imposing steadily lower reliability and higher buffering costs. Safety: Safety data are for the most part not readily accessible. The federally-supported Fatality Analysis Reporting System (FARS) data is the most accurate and readily accessible data on transportation-related fatalities. State DOTs maintain records of fatalities, injuries and property damage on state and local roads, but the data are usually reported by class of roadway or by type and severity of accident, rather than by highway corridor or roadway segment. 5.5.2 Freight Fluidity Case Study The case study in the I-95 Corridor Report is very similar to the cereal grain corridor modelled between Chicago and New Orleans (found in appendix C). While the scenario simulated with the CMAP freight models was for cereal grains (excluding soybeans) between northeastern Illinois and New Orleans LA, the case study in the I-95 Report examined soybean transportation between Peoria, IL and New Orleans, LA. In the case study, the factors of freight fluidity measured were travel time, travel time reliability, and travel cost. Due to data limitations, travel risk and safety were not measured. Despite the minor difference in the type of grain, and the focus on a farm in central Illinois rather than Northeastern Illinois, the corridors are otherwise identical. The freight fluidity calculated in the case study can be used as a proxy for understanding freight fluidity for cereal grains between Chicago and New Orleans. The path examined in the I-95 Corridor Report case study was, 1) truck delivery between a farm in El Paso, IL and a river port in Peoria, IL with access to the Illinois River, and then 2) by barge along the Illinois and Mississippi Rivers to a New Orleans export terminal. For the truck segment between El Paso and Peoria, travel time was calculated with data from Google Maps. Where available by Google Maps, the peak/off-peak or historical travel data can be used to estimate travel time reliability. Since this data was not available for the trip between El Paso and Peoria, a travel time index published in the Urban Mobility Report by the Texas Transportation Institute (TTI) was used to approximate the Truck Travel Time Reliability (TTTR) index with a travel-time multiplier.9 According to the Urban Mobility Report, for a small metropolitan area, the 95th percentile travel time is typically 11 percent more than free-flow travel time. The 11 percent converted into an index multiplier of 1.11 and was applied to the travel time calculated with Google Maps 9 Texas Transportation Institute, â2012 Urban Mobility Report,â http://d2dtl5nnlpfr0r.cloudfront.net/
74 to approximate the TTTR for roads in the Peoria area. Truck cost data for the case study was obtained from Chainalytics. For the barge segment, travel time and reliability were calculated with data from the USACE NAIS data. The barge travel cost was calculated with data from the USDA and other industry data such as nominal rates for tariffs. The tariffs permitted an estimate of travel costs. Discount estimates were used to approximate shipment discounts for bulk shippers. Table 10 summarizes the results of the freight fluidity measured in the I-95 study. The results show that the travel time for soybeans by truck and barge between Illinois and New Orleans is on average 8.23 days, and with the variability in shipments and travel time, the 95th percentile of shipments is delivered in 14.6 days. This means that shipments can take on average 8 days but can also take upwards of 15 days. The costs of delivery come out to be $26 per ton. Table 10: Soybean Export Supply Chain Performance, Agriculture Case Study Truck from El Paso, IL to Peoria, IL Barge from Peoria, IL to New Orleans, LA Total Sources (Truck; Barge) Travel Time (Days, Hours) 0.8 hours 8.2 days 8.23 days NPMRDS, Google Maps; NAIS Travel Reliability (95% Travel Time) 1.7 hours 14.5 days 14.6 days NPMRDS, Google Maps; NAIS Travel Cost (2013$) $11 per ton ($205 per trip, assuming 17 to 20 tons per trip) $15 per ton (1,580 tons per trip) $26 per ton Chainalytics; USDA and industry sources Source: I-95 Corridor Coalition FREIGHT PERFORMANCE MEASUREMENT Measuring the Performance of Supply Chains across Multistate Jurisdictions White Paper, March 2016 5.6 CONCLUSION A literature review of freight supply chain modeling for public planning purposes revealed that such models have been developed for only a few U.S. transportation planning agencies. This chapter has demonstrated how the use of two of these modeling systems, CMAP and FreightSIM, can be used to simulate and assess the impacts of transportation network disruptions by identifying alterative modal supply chain options. In the case of the CMAP modeling of grain flows from Illinois to New Orleans, the model results matched the expected outcome---when waterways are not available, railways would be needed to continue the transportation from farm to export. In the modeling of pharmaceutical shipments from Miami to Houston, the FreightSIM was manipulated to reflect the changes in mode split due to a hypothetical terrorist attack on air travel. Again, as expected the model moves the freight in this corridor from the air to the truck mode. As one of five types of risk associated with product supply chains, disruption risk can be usefully assessed using freight fluidity measures. To this end supply chain models can be used to simulate commodity and O-D-specific performance, including inter-modal freight fluidity-based measures associated with travel times and monetary costs, and how these are impacted by major transportation system disruptions. To demonstrate this, a recent I-95 case study was examined in terms of its inter-modal freight fluidity, as measured in terms of travel time, travel cost, and travel time reliability. Before and after measures of fluidity such as these can shed useful light on the freight transportation systemâs resiliency to major disruption events. tti.tamu.edu/documents/mobility-report-2012.pdf