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Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods

Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods
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Author(s): Asma Husna (Department of Mechanical and Industrial Engineering, Ryerson University, Canada), Saman Hassanzadeh Amin (Department of Mechanical and Industrial Engineering, Ryerson University, Canada) and Bharat Shah (Ted Rogers School of Management, Ryerson University, Canada)
Copyright: 2021
Pages: 31
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.ch005

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Abstract

Supply chain management (SCM) is a fast growing and largely studied field of research. Forecasting of the required materials and parts is an important task in companies and can have a significant impact on the total cost. To have a reliable forecast, some advanced methods such as deep learning techniques are helpful. The main goal of this chapter is to forecast the unit sales of thousands of items sold at different chain stores located in Ecuador with holistic techniques. Three deep learning approaches including artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) are adopted here for predictions from the Corporación Favorita grocery sales forecasting dataset collected from Kaggle website. Finally, the performances of the applied models are evaluated and compared. The results show that LSTM network tends to outperform the other two approaches in terms of performance. All experiments are conducted using Python's deep learning library and Keras and Tensorflow packages.

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