Original scientific paper
A Comparison between Neural Networks and Traditional Forecasting Methods: A Case Study
C. A. Mitrea
; Nanyang Technological University, Singapore
C. K. M. Lee
; Nanyang Technological University, Singapore
Z. Wu
; Nanyang Technological University, Singapore
Abstract
Forecasting accuracy drives the performance of inventory management. This study is to investigate and compare different forecasting methods like Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) with Neural Networks (NN) models as Feed-forward NN and Nonlinear Autoregressive network with eXogenous inputs (NARX). Data used to forecast is acquired from inventory database of Panasonic Refrigeration Devices Company located in Singapore. Results have shown that forecasting with NN offers better performance in comparison with traditional methods.
Keywords
ARIMA; Forecasting; Inventory; Neural Networks; Safety Stock
Hrčak ID:
66717
URI
Publication date:
15.9.2009.
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