Abstract:
The supplier selection process is one of the main components of the Freight Activity Microsimulation Estimator (FAME), which is a disaggregated and comprehensive framework that simulates the freight movements for all industries and all commodities in the U.S. However, the supplier selection and supplier evaluation models in the FAME face computational issues. Using the result of a nationwide establishment survey, this study analyzes the supplier selection problem by evaluating the potential suppliers. The buyer’s behavior on selecting the distance range in which the trade forms is analyzed using both machine-learning and statistical approaches. A decision-tree model and an ordered probit model are estimated and compared to better comprehend the supplier evaluation process. The results indicate that several factors such as the type of the business, commodity type, number of orders, and the value of orders are significant factors. In addition, the decision-tree model is reliable in forecasting the consumer’s behavior.
