Forecasting demand and inventory management using Bayesian time series

Authors

T A. Spedding, University of WollongongFollow
K K. Chan, Nanyang Technological University, Singapore

RIS ID

57370

Publication Details

Spedding, T. A. & Chan, K. K. (2000). Forecasting demand and inventory management using Bayesian time series. Integrated Manufacturing Systems, 11 (5), 331-339.

Abstract

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 (autoregressive integrated moving average (ARIMA) model).