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Optimization of Vegetable Restocking and Pricing Strategies for Innovating Supermarket Operations Utilizing a Combination of ARIMA, LSTM, and FP-Growth Algorithms

Author

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  • Haoyang Ping

    (School of Future Science and Engineering, Soochow University, Suzhou 215299, China)

  • Zhuocheng Li

    (School of Future Science and Engineering, Soochow University, Suzhou 215299, China)

  • Xizhu Shen

    (School of Future Science and Engineering, Soochow University, Suzhou 215299, China)

  • Haizhen Sun

    (School of Mechanical and Electric Engineering, Soochow University, Suzhou 215299, China
    Jiangsu Provincial Key Laboratory of Advanced Robotics, School of Mechanical and Electric Engineering, Soochow University, Suzhou 215123, China)

Abstract

In the dynamic environment of fresh food supermarkets, managing the short shelf life and varying quality of vegetable products presents significant challenges. This study focuses on optimizing restocking and pricing strategies to maximize profits while accommodating the diverse and time-sensitive nature of vegetable sales. We analyze historical sales, pricing data, and loss rates of six vegetable categories in Supermarket A from 1 July 2020 to 30 June 2023. Using advanced data analysis techniques like K-means++ clustering, non-normal distribution assessments, Spearman correlation coefficients, and heat maps, we uncover significant correlations between vegetable categories and their sales patterns. The research further explores the implications of cost-plus pricing, revealing a notable relationship between pricing strategies and sales volumes. By employing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models, we forecast sales and determine optimal restocking volumes. Additionally, we use price elasticity theories and a comprehensive model to predict net profit changes, aiming to enhance profit margins by 47%. The study also addresses space constraints in supermarkets by proposing an effective assortment of salable items and individual product restocking plans, based on FP-Growth algorithm analysis and market demand. Our findings offer insightful strategies for sustainable and economic growth in the supermarket industry, demonstrating the impact of data-driven decision-making on operational efficiency and profitability.

Suggested Citation

  • Haoyang Ping & Zhuocheng Li & Xizhu Shen & Haizhen Sun, 2024. "Optimization of Vegetable Restocking and Pricing Strategies for Innovating Supermarket Operations Utilizing a Combination of ARIMA, LSTM, and FP-Growth Algorithms," Mathematics, MDPI, vol. 12(7), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:1054-:d:1368059
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    References listed on IDEAS

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    3. Rabbani, Masoud & Pourmohammad Zia, Nadia & Rafiei, Hamed, 2016. "Joint optimal dynamic pricing and replenishment policies for items with simultaneous quality and physical quantity deterioration," Applied Mathematics and Computation, Elsevier, vol. 287, pages 149-160.
    4. Li Jia & Jing Zhang & Guanghua Qiao, 2022. "Scale and Environmental Impacts of Food Loss and Waste in China—A Material Flow Analysis," IJERPH, MDPI, vol. 20(1), pages 1-21, December.
    5. Tamíris Pacheco da Costa & James Gillespie & Xavier Cama-Moncunill & Shane Ward & Joan Condell & Ramakrishnan Ramanathan & Fionnuala Murphy, 2022. "A Systematic Review of Real-Time Monitoring Technologies and Its Potential Application to Reduce Food Loss and Waste: Key Elements of Food Supply Chains and IoT Technologies," Sustainability, MDPI, vol. 15(1), pages 1-27, December.
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