Commodity Pricing and Replenishment Decision Strategy Based on the Seasonal ARIMA Model

Liu, Jiaying; Liu, Bin

doi:10.3390/math11244921

Cited by 3 publications

(1 citation statement)

References 18 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So our work used K-means++ clustering to reclassify vegetables. Liu, J's team used the ARIMA model, which can reflect seasonal features better, but the prediction accuracy needs to be improved [22]. Yin, H's work used STL-Attention-based LSTM model, which wants to achieve seasonal feature extraction along with prediction accuracy [23].…”

Section: Introductionmentioning

confidence: 99%

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

Ping,

Li,

Shen

et al. 2024

Mathematics

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Ping,

Li,

Shen

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

Genetic Algorithms Application for Pricing Optimization in Commodity Markets

Li,

Xu,

Wang

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

The perishable nature of vegetable commodities poses challenges for superstores, as reselling them is often unfeasible due to their short freshness period. Reliable market demand analysis is crucial for boosting revenue. This study simplifies the pricing and replenishment decision-making process by making reasonable assumptions about the selling time, wastage rate, and replenishment time for vegetable commodities. A single-objective planning model with the objective of profit maximization was constructed by fitting historical data using the nonparametric method of support vector regression (SVR). The study reveals a specific relationship between sales volume and cost-plus pricing for each category and predicts future cost changes using an LSTM model. Combining these findings, we substitute the relationship between sales volume and pricing as well as the LSTM prediction data into the model, and solve it using genetic algorithms in machine learning to derive the optimal replenishment volume and pricing strategy. Practical results show that the method can provide reasonable pricing and replenishment strategies for vegetable superstores, and after careful accounting, we arrive at an expected profit of RMB 22,703.14. The actual profit of the supermarket was RMB 19,732.89. The method, therefore, increases the profit of the vegetable superstore by 13.08%. By optimizing inventory management and pricing decisions, the superstore can better meet the challenges of vegetable commodities and achieve sustainable development.

show abstract

Sistema web para la dinamización de la gestión de inventario y estrategias de marketing en supermercados peruanos

Reyes-Riveros,

Castillo-Sarmiento,

Santos-Fernández

et al. 2024

Rev Cient Sist Inform

View full text Add to dashboard Cite

Frente a la demanda de enfoques innovadores en supermercados, donde la administración eficiente de inventarios y estrategias de marketing es crucial para la pertinencia y competitividad, se resalta la importancia de un sistema web respaldado por la metodología ágil Scrum. El objetivo central fue explorar la importancia de este sistema para dinamizar la gestión de inventarios y estrategias de marketing en supermercados, demostrando su capacidad para abordar desafíos actuales y ofrecer flexibilidad futura. Se implementó la metodología Scrum, con el diseño del sistema basado en tecnologías como HTML, CSS, PHP, Node.js, Laravel y SQL Server Express, garantizando un desarrollo ágil y adaptable. Los resultados incluyen roles definidos, priorización del Product Backlog, planificación y estimación mediante puntos de historia, evaluación positiva de indicadores económicos y eficiencia en pruebas de rendimiento utilizando JMETER. La discusión destaca la necesidad de alinear la elección del sistema con tácticas de sistemas de información, enfatizando la valoración de indicadores económicos y pruebas de carga y estrés. En conclusión, el sistema web respaldado por Scrum ha tenido éxito al mejorar la gestión de inventarios y estrategias de marketing, elevando la experiencia del usuario con indicadores económicos positivos y pruebas de carga eficientes.

show abstract

Commodity Pricing and Replenishment Decision Strategy Based on the Seasonal ARIMA Model

Cited by 3 publications

References 18 publications

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

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

Genetic Algorithms Application for Pricing Optimization in Commodity Markets

Sistema web para la dinamización de la gestión de inventario y estrategias de marketing en supermercados peruanos

Contact Info

Product

Resources

About