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

Haoyang Ping, Zhuocheng Li, Xizhu Shen and Haizhen Sun ()
Additional contact information
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

Mathematics, 2024, vol. 12, issue 7, 1-17

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.

Keywords: optimizing restocking and pricing strategies; K-means++ clustering; ARIMA model; LSTM model; FP-Growth algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/7/1054/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/7/1054/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:7:p:1054-:d:1368059

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().