AGILE FORECASTING OF DYNAMIC LOGISTICS DEMAND

Author:

Miao Xin1,Xi Bao1

Affiliation:

1. National Center of Technology, Policy and Management, School of Management, Harbin Institute of Technology, 150001 Harbin, China

Abstract

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.

Publisher

Vilnius Gediminas Technical University

Subject

Mechanical Engineering,Automotive Engineering

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