Date of Award
Master of Science
Department of Operational Sciences
Jeffery D. Weir, PhD.
The United States military heavily relies on rail freight operations to meet many of its logistical needs during both peacetime and wartime efforts. As the head organization responsible for managing and overseeing all modes of military transportation, United States Transportation Command depends on timely accurate railcar demand forecasts to drive critical decisions on distribution and placement of railcar assets. However, the intermittent nature of railcar demands based on location and commodity make it a challenging task for forecasters. Furthermore, these “lumpy” demands often come without any obvious trends or seasonality. This study explores the utility of both traditional forecasting methods and newer techniques designed specifically for handling intermittent demands. All forecasting parameters for each method are optimized based on specific cost functions. Accuracy metrics are then applied to all forecasts for analysis. The results indicate that for the Department of Defense’s railcar demands, optimizing basic forecasting methods such as Simple Moving Averages and Simple Exponential Smoothing outperform more popular methods for sparse demands such as Croston’s method and its variants. Despite its theoretical superiority, applying Croston’s method to railcar demands was found questionable and consistently produced poor forecasts compared to other methods. Analysis provides valuable insight in future strategies for forecasting intermittent demands.
DTIC Accession Number
Park, James M., "Optimizing Forecasting Methods for USTRANSCOM Railcar Demands" (2016). Theses and Dissertations. 375.