@phdthesis{Brzoska2020,
  author    = {Brzoska, Jan},
  title     = {Market forecasting in China: An Artificial Neural Network approach to optimize the accuracy of sales forecasts in the Chinese automotive market},
  doi       = {10.25972/OPUS-20315},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203155},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {Sales forecasts are an essential determinant of operational planning in entrepreneurial organizations. However, in China, as in other emerging markets, monthly sales forecasts are particularly challenging for multinational automotive enterprises and suppliers. A chief reason for this is that conventional approaches to sales forecasting often fail to capture the underlying market dynamics. To that end, this dissertation investigates the application of Artificial Neural Networks with an implemented backpropagation algorithm as a more "unconventional" sales forecasting method. A key element of statistical modelling is the selection of superior leading indicators. These indicators were collected as part of the researcher's expert interviews with multinational enterprises and state associations in China. The economic plausibility of all specified indicators is critically explored in qualitative-quantitative pre-selection procedures. The overall objective of the present study was to improve the accuracy of monthly sales forecasts in the Chinese automotive market. This objective was achieved by showing that the forecasting error could be lowered to a new benchmark of less than 10\% in an out-of-sample forecasting application.},
  subject      = {China},
  language  = {en}
}