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Demands and Sales Forecasting for Retailers by Analyzing Google Trends and Historical Data

Demands and Sales Forecasting for Retailers by Analyzing Google Trends and Historical Data
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Author(s): Md Rokon Uddin (Department of Mechanical and Industrial Engineering, Ryerson University, Canada), Saman Hassanzadeh Amin (Department of Mechanical and Industrial Engineering, Ryerson University, Canada) and Guoqing Zhang (Supply Chain and Logistics Optimization Research Center, University of Windsor, Canada)
Copyright: 2021
Pages: 22
Source title: Demand Forecasting and Order Planning in Supply Chains and Humanitarian Logistics
Source Author(s)/Editor(s): Atour Taghipour (Normandy University, France)
DOI: 10.4018/978-1-7998-3805-0.ch003

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Abstract

A supply chain includes several elements such as suppliers, manufacturers, retails, and customers. Forecasting the demands and sales is a challenging task in supply chain management (SCM). The main goal of this research is to create forecasting models for retailers by using artificial neural network (ANN) and to enable them to make accurate business decisions by visualizing future data. Two forecasting models are investigated in this research. One is a sales model that predicts future sales, and the second one is a demand model that predicts future demands. To achieve the mentioned goal, CNN-LSTM model is used for both sales and demand predictions. Based on the obtained results, this hybrid model can learn from very long range of historical data and can predict the future efficiently.

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