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.
| Original language | English |
|---|---|
| Pages (from-to) | 26-30 |
| Number of pages | 5 |
| Journal | Transport |
| Volume | 23 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2008 |
| Externally published | Yes |
Keywords
- Agility
- Computer simulation
- Dynamic influencing factors
- Forecasting
- Hybrid algorithm
- Logistics
- Supply chain management
- Swarm
Fingerprint
Dive into the research topics of 'Agile forecasting of dynamic logistics demand'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver