Long-range demand planning and capacity management play an important role for policy makers and airline managers alike. Each makes decisions regarding allocating appropriate levels of funds to align capacity with forecasted demand. Decisions today can have long lasting effects. Reducing forecast errors for long-range range demand forecasting will improve resource allocation decision making. This research paper will focus on improving long-range demand planning and forecasting errors of passenger traffic in the U.S. domestic airline industry. This paper will look to build upon current forecasting models being used for U.S. domestic airline passenger traffic with the aim of improving forecast errors published by Federal Aviation Administration (FAA). Using historical data, this study will retroactively forecast U.S. domestic passenger traffic and then compare it to actual passenger traffic, then comparing forecast errors. Forecasting methods will be tested extensively in order to identify new trends and causal factors that will enhance forecast accuracy thus increasing the likelihood of better capacity management and funding decisions.
If this is your Honors thesis, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu
Bachelor of Science in Business Administration (B.S.B.A.)
College of Business Administration
Business Administration -- Dissertations, Academic;Dissertations, Academic -- Business Administration
Length of Campus-only Access
Honors in the Major Thesis
Nizam, Anisulrahman, "Improving long range forecast errors for better capacity decision making" (2013). HIM 1990-2015. 1784.