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Forecasting a logistic service demand based on neural network

 
: Zhou, L.; Heimann, B.; Clausen, U.

:

University of Technology -UTT-, Troyes; Institute of Electrical and Electronics Engineers -IEEE-:
ICSSSM '06, 2006 International Conference on Service Systems and Service Management. Proceedings. Vol.1 : University of Technology of Troyes, October 25 to 27, 2006, France
Piscataway, NJ: IEEE, 2007
ISBN: 1-424-40450-9
ISBN: 978-142440451-3
pp.530-534
International Conference on Service Systems and Service Management (ICSSSM) <2006, Troyes>
English
Conference Paper
Fraunhofer IML ()
neural network; logistic service demand; forecasting

Abstract
Forecasting facilitates cutting down operational and management costs while ensuring service level for a logistics service provider. Our case study here is to investigate how to forecast logistic demand for a LTL carrier. First only time series forecasting is employed as no suitable explanatory indicators can be found for the regression approach. Among the times series forecasting methods, NN is adopted considering its feasibility and applicability. The simulation results verified its advantage over other two conventional time series approaches. The work done in the paper helps manager to select prediction method in practice.

: http://publica.fraunhofer.de/documents/N-171251.html