The Deep Learning Approach In Demand Forecasting In The Supply Chain Process Of Ice Creams At The Grocery Store Firm

Tran Luu Thi, Tran Tien Huy, Ngo Thi Trang Thuy, Phan Tien Thanh, Huu Thuan Thang Nguyen, Nguyễn Thị Dung, Lê Thị Long Châu, Nguyen Nhat Minh, Nguyen Ho Thanh Dat and Hoang Van Hai ()
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Tran Luu Thi: University of Economics, The University of Danang
Tran Tien Huy: University of Economics, The University of Danang
Ngo Thi Trang Thuy: University of Economics, The University of Danang
Phan Tien Thanh: University of Economics, The University of Danang
Huu Thuan Thang Nguyen: University of Economics, The University of Danang
Nguyễn Thị Dung: University of Economics, The University of Danang
Lê Thị Long Châu: Danang Vocational Tourism College
Nguyen Nhat Minh: RMIT University
Nguyen Ho Thanh Dat: University of Economics, The University of Danang
Hoang Van Hai: University of Economics, The University of Danang

A chapter in Proceedings of the International Conference on Emerging Challenges: Sustainable Strategies in the Data-Driven Economy (ICECH 2024), 2025, pp 332-345 from Springer

Abstract: Abstract Research purpose: This study investigates the application of a Deep Feedforward Network (DFN) for demand forecasting and customer willingness-to-pay predictions in the retail sector. Research design, approach, and method: Using real-world data from VinMart and other retail stores, the DFN was tested for its ability to predict order quantities across multiple locations. Main findings: While the model delivered promising results, training separate networks for individual stores proved more effective than using a single network for all stores, due to varying input significance levels. Data limitations, such as the lack of extensive historical records, affected accuracy. Additionally, the DFN was used to predict customer willingness-to-pay, with inputs gathered through quantitative research. While the model showed potential, its success is highly dependent on data quality. Practical/managerial implications: Future research should explore alternative deep learning architectures and incorporate more diverse variables to enhance accuracy. This study underscores the importance of robust data for optimizing supply chain decisions in retail.

Keywords: Deep learning; Supply chain; Ice-cream industry; Inventory level control (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-694-9_23

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