Supply chain demand forecasting: A comparison of machine learning techniques and traditional methods

Journal of Applied Sciences

Volumn 9, Issue 3, 2009, Pages 521-527

  Shahrabi, J ^a   Mousavi, Seyed Smaeil ^b   Heydar, M ^b  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Artificial neural networks; Bullwhip effect; Demand forecasting; Support vector machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; DEEP NEURAL NETWORKS; FORECASTING; LEARNING ALGORITHMS; NEURAL NETWORKS; SUPPLY CHAINS; SUPPORT VECTOR MACHINES;

BULLWHIP EFFECTS; DEMAND FORECASTING; EXPONENTIAL SMOOTHING; FORECASTING METHODS; MACHINE LEARNING TECHNIQUES; MOVING AVERAGES; SUPPORT VECTOR MACHINE (SVMS); TIME SERIES FORECASTING;

LEARNING SYSTEMS;

EID: 63049134222     PISSN: 18125654     EISSN: 18125662     Source Type: Journal    
DOI: 10.3923/jas.2009.521.527     Document Type: Article

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