Management Science and Financial Engineering

The Korean Operations Research and Management Science Society (한국경영과학회)
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Predicting the Performance of Forecasting Strategies for Naval Spare Parts Demand: A Machine Learning Approach

  • Moon, Seongmin (Integrated Logistics Support Technology Team, Defense Acquisition Program Administration)
  • Received : 2012.05.27
  • Accepted : 2012.06.03
  • Published : 2013.05.31
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Abstract

Hierarchical forecasting strategy does not always outperform direct forecasting strategy. The performance generally depends on demand features. This research guides the use of the alternative forecasting strategies according to demand features. This paper developed and evaluated various classification models such as logistic regression (LR), artificial neural networks (ANN), decision trees (DT), boosted trees (BT), and random forests (RF) for predicting the relative performance of the alternative forecasting strategies for the South Korean navy's spare parts demand which has non-normal characteristics. ANN minimized classification errors and inventory costs, whereas LR minimized the Brier scores and the sum of forecasting errors.

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References

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