ISSN:
0957-6061
Source:
Emerald Fulltext Archive Database 1994-2005
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
,
Economics
Notes:
Discusses the development and evaluation of a forecasting model for inventory management in an advanced technology batch production environment. Traditional forecasting and inventory management do not adequately address issues relating to a short life cycle and to non-seasonal products with a relatively long lead time. Limited historical data (fewer than 100 observations) is also a problem in predicting short-term dynamic or unstable time series. A Bayesian dynamic linear time series model is proposed as an alternative technique for forecasting demand in a dynamically changing environment. Provides details of the important characteristics and development process of the forecasting model. A case study is then presented to illustrate the application of the model based on data from a multinational company in Singapore. It also compares the Bayesian dynamic linear time series model with a classical forecasting model (auto-regressive integrated moving average (ARIMA) model).
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1108/09576060010335609
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