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Sustainability Analysis and Market Demand Estimation in the Retail Industry through a Convolutional Neural Network

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  • Luyao Wang

    (State Key Lab for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

  • Hong Fan

    (State Key Lab for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

  • Yankun Wang

    (State Key Lab for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

Abstract

The Chinese retail industry is expected to grow dramatically over the next few years, owing to the rapid increase in purchasing power of Chinese consumers. Retail managers should analyze the market demands and avoid dull sales to promote the sustainable development of the retail industry. Economic sustainability in the retail industry, which refers to a suitable return of investment, requires the implementation of precise product allocation strategies in different regions. This study proposed a hybrid model to evaluate economic sustainability in the preparation of goods of retail shops on the basis of market demand evaluation. Through a grid-based convolutional neural network, a regression model was first established to model the relationship between consumer distribution and the potential market demand. Then, another model was proposed to evaluate the sustainability among regions based on their supply-demand analysis. An experiment was conducted based on the actual sales data of retail shops in Guiyang, China. Results showed an immense diversity of sustainability in the entire city and three classes of regions were distinguished, namely, high, moderate, and limited. Our model was proven to be effective in the sustainability evaluation of supply and demand in the retail industry after validation showed that its accuracy reached 92.8%.

Suggested Citation

  • Luyao Wang & Hong Fan & Yankun Wang, 2018. "Sustainability Analysis and Market Demand Estimation in the Retail Industry through a Convolutional Neural Network," Sustainability, MDPI, vol. 10(6), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1762-:d:149306
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    References listed on IDEAS

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    Cited by:

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    2. Guangying Xie & Shengyan Wu & Zhengjiang Song, 2022. "Focus on Innovation or Focus on Sales? The Influences of the Government of China’s Demand-Side Reform during COVID-19 and Their Sustainability Consequences in the Consumer Products Industry," Sustainability, MDPI, vol. 14(20), pages 1-21, October.

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