Open AccessAGILE FORECASTING OF DYNAMIC LOGISTICS DEMAND
Author(s) -
Xin Miao,
Xianwen Bao
Publication year - 2008
Publication title -
transport
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 31
eISSN - 1648-4142
pISSN - 1648-3480
DOI - 10.3846/1648-4142.2008.23.26-30
Subject(s) - agile software development , demand forecasting , computer science , reliability (semiconductor) , supply chain , operations research , kalman filter , artificial neural network , engineering , artificial intelligence , business , power (physics) , physics , software engineering , quantum mechanics , marketing
The objective of this paper is to study the quantitative forecasting method for agile forecasting of logistics demand in dynamic supply chain environment. Characteristics of dynamic logistics demand and relative forecasting methods are analyzed. In order to enhance the forecasting efficiency and precision, extended Kalman Filter is applied to training artificial neural network, which serves as the agile forecasting algorithm. Some dynamic influencing factors are taken into consideration and further quantified in agile forecasting. Swarm simulation is used to demonstrate the forecasting results. Comparison analysis shows that the forecasting method has better reliability for agile forecasting of dynamic logistics demand.