Supply chain-based models attempt to replicate the process followed by private-sector companies when making mode/vehicle decisions. These models consider in great detail all the different cost components that influence logistic cost (the summation of transportation and inventory costs); as well as detailed demand data such as the amount of cargo that a shipper needs to deliver to each customer. Although at first sight, the notion of replicating the decisions of individual companies seems like a good idea, the data requirements are so difficult to meet that these models cannot be reliably be used in practice. The most widely used model in this family is the Intermodal Transportation and Inventory Cost Model (ITIC) (Federal Railroad Administration 2005, Federal Highway Administration 2011).
The fundamental weakness of these models is the tremendous challenge of collecting the data needed. The onerous cost of collecting the data needed by ITIC is a consequence of the confidential nature of private-sector data, particularly cost data, and the profound heterogeneity of business conditions, where different industry sectors and companies have different transportation and inventory costs. It is worth mentioning that ITIC’s developers were undoubtedly aware of the immense challenge associated with securing the input data as revealed in the “Data Overview” section (page 17) of Federal Railroad Administration (2005):
“While these are the data needs of the model, the problem is that publicly available sources of disaggregate data are difficult to find. This is true in spite of the fact that hundreds of thousands of shipments are made every day by manufacturing companies and product distributors throughout this country as well as overseas. Each of these shipments is fully documented, the movement is billed for, and the transportation charges paid to trucking companies, railroads, airlines, barge lines, pipeline companies, and other freight carriers. The data collection problem is caused by the fact that it is against the law for carriers to reveal the names of shippers and receivers, the product amounts that are shipped and the origins and destinations of individual movements without that shipper’s individual approval.”
The chief implication is that using average values for the numerous cost items that determine transportation and logistics costs does not provide much insight into mode/vehicle choice behavior. Only in very rare circumstances, like in the “Southern California Association of County Governments Port and Mode Diversion Model” (Leachman et al. 2005, Leachman 2008), can supply chain models be used to meaningfully analyze policy alternatives. In this case, the data needed–detailed records of import containers–were available. However, for studies of mode/vehicle choice in domestic cargo, such records are not readily available and are extremely hard to assemble.
The challenge of collecting the data needed to run the ITIC issue has been recognized by ITIC users who, sometimes, have elected to use it with aggregate estimates of demand, e.g., from the Freight Analysis Framework; and with default values of the input data cost items. Since the ITIC was not designed to use aggregate data, using aggregate estimates that combine thousands of individual shipments leads to an incorrect calculation of the logistic costs and freight mode/vehicle choices. Moreover, assuming that all of the data items required by the ITIC are equal for all industry sectors neglects to consider the heterogeneous nature of business activity in the US.