Degree Type

Creative Component

Semester of Graduation

Summer 2018

Department

Industrial and Manufacturing Systems Engineering

First Major Professor

Cameron A. MacKenzie

Second Major Professor

Gary Mirka

Degree(s)

Master of Science (MS)

Major(s)

Industrial Engineering

Abstract

Airplane manufacturing industry is a low-volume high-value industry; however, there is a very high uncertainty associated with it. The industry has long lead times and capacity expansion for such an industry requires huge capital investments. Therefore, capacity planning requires accurate demand forecasting based on the historical data. Various demand forecasting models based on the forecasted demand can serve as an influential tool for the decision making. Based on the profit requirements, cost saving, and the risk attitude of a decision maker, he or she may choose a different strategy. This primary purpose of this research is to model the uncertainty and analyze different decision-making approaches for long-term capacity planning for painting the Boeing 737 airplanes. The first part of the research focusses on identifying the underlying demand trends for the Boeing 737 and Boeing 777 airplane models based on the historical data. Probabilistic models were evaluated for the demand based on model assumptions and statistical analysis. The stochastic processes Brownian motion and a modified geometric Brownian motion were used to predict the demand for the Boeing 737 and Boeing 777 respectively for the next 20 years. The second part of the research focusses on decision making based on the forecasted demand for the Boeing 737 airplanes. The decision is when to construct new hangars to paint new airplanes. Three decision-making approaches were applied to this decision: expected utility, robust decision making, and information gap. Since significant uncertainty exists with the number of airplanes, it is important to compare the decision-making methodologies for different risk tolerances, probabilities, and required profits. The circumstances and assumptions favoring each of the decision-making philosophy under deep uncertainty was discussed and, based on the simulation results, the optimal strategies for the capacity expansion were summarized.

Copyright Owner

Krishna Sai Varun Kotta

File Format

application/pdf

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