DEMAND FORECASTING IN A SUPPLY CHAIN USING MACHINE LEARNING ALGORITHMS

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SHAFIEI NIKABADI MOHSEN; AZIMI ALI

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Journal: Modeling in Engineering Year:2015 | Volume:13 | Issue:41 Page(s): 127-136

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Information Journal Paper

Title

DEMAND FORECASTING IN A SUPPLY CHAIN USING MACHINE LEARNING ALGORITHMS

Author(s)

SHAFIEI NIKABADI MOHSEN | AZIMI ALI | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKS (ANN)Q1

BULLWHIP EFFECTQ2

DEMAND FORECASTINGQ2

SUPPORT VECTOR MACHINE (SVM)Q2

Abstract

the purpose of this paper is to compare two artificial intelligence algorithms for forecasting supply chain demand. In first step data are prepared for entering into forecasting models. In next step, the modeling step, an artificial neural network and support vector machine is presented. The structure of artificial neural network is selected based on previous researchers' results. For measuring errors we use Mean squared error and we use another index for time which is used running the algorithms. The results show that artificial neural network can forecast more accurate meanwhile support vector machine is faster.

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APA: Copy

SHAFIEI NIKABADI, MOHSEN, & AZIMI, ALI. (2015). DEMAND FORECASTING IN A SUPPLY CHAIN USING MACHINE LEARNING ALGORITHMS. JOURNAL OF MODELING IN ENGINEERING, 13(41), 127-136. SID. https://sid.ir/paper/173827/en

Vancouver: Copy

SHAFIEI NIKABADI MOHSEN, AZIMI ALI. DEMAND FORECASTING IN A SUPPLY CHAIN USING MACHINE LEARNING ALGORITHMS. JOURNAL OF MODELING IN ENGINEERING[Internet]. 2015;13(41):127-136. Available from: https://sid.ir/paper/173827/en

IEEE: Copy

MOHSEN SHAFIEI NIKABADI, and ALI AZIMI, “DEMAND FORECASTING IN A SUPPLY CHAIN USING MACHINE LEARNING ALGORITHMS,” JOURNAL OF MODELING IN ENGINEERING, vol. 13, no. 41, pp. 127–136, 2015, [Online]. Available: https://sid.ir/paper/173827/en

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