Research on Vegetable Replenishment and Pricing Based on Cost-Plus Pricing Method and ARIMA Model

Authors

  • Xuanying Han
  • Ranran Zhang
  • Ziheng Li

DOI:

https://doi.org/10.54097/eefg0z92

Keywords:

Vegetable Replenishment, Vegetable Pricing, Cost-Plus Pricing, ARIMA Model.

Abstract

Replenishment and pricing of vegetables are the focus of commercial supermarkets. Accurately predicting the future vegetable demand and pricing of commercial supermarkets is of great significance for commercial supermarkets to increase revenue. To make accurate predictions and maximize profits, this paper predicts the mean and variance of the demand and pricing of various vegetable categories in the future based on the cost-plus pricing method and the ARIMA time series model, and obtains the best demand and pricing for the next seven days. The results show that the model has passed the residual independent test and normal test, and R2 is greater than 0.7, which has a good fitting effect. The model is suitable for the prediction of vegetable replenishment and pricing, and can provide a reference for vegetable management in supermarkets.

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References

McLaughlin E W. The dynamics of fresh fruit and vegetable pricing in the supermarket channel [J]. Preventive Medicine, 2004, 39: 81 - 87.

Nahmias S. Perishable inventory systems [M]. Springer Science & Business Media, 2011.

Sainathan A. Pricing and replenishment of competing perishable product variants under dynamic demand substitution [J]. Production and Operations Management, 2013, 2 2 (5): 1157 - 1181.

Chen C Y, Lee W I, Kuo H M, et al. The study of a forecasting sales model for fresh food [J]. Expert Systems with Applications, 2010, 37 (12): 7696 - 7702.

Fan T, Xu C, Tao F. Dynamic pricing and replenishment policy for fresh produce [J]. Computers & Industrial Engineering, 2020, 139: 106127.

Bi Liyuan, Wu Sai, Chen Gang, Shou Li Dan, Chen Ke, Hu Tianlei. Database query cost prediction based on recurrent neural network[J]. Journal of Software, 2018, 29 (03): 799 - 810.

Dutta S, Maiti S. Price forecasting of agricultural products using arima models [J]. Indian Journal of Agricultural Marketing, 2021, 35 (2): 149 - 164.

Guilding C, Drury C, Tayles M. An empirical investigation of the importance of cost‐plus pricing [J]. Managerial Auditing Journal, 2005, 20 (2): 125 - 137.

Barusman A R P, Yuliana T M, Mirfazli E. Analysis of implementation cost plus pricing method in the decision on the determination of product sales Prices [J]. Intern J Adv Sci Techn, 2020, 29 (06): 1832 - 1838.

Risvanti I. Cost plus pricing method in determining the selling price of the banana chips [J]. JOSAR (Journal of Students Academic Research), 2018, 3 (2): 50 - 60.

Nelson B K. Time series analysis using autoregressive integrated moving average (ARIMA) models [J]. Academic emergency medicine, 1998, 5 (7): 739 - 744.

Zhai M, Li W, Tie P, et al. Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis [J]. BMC Infectious Diseases, 2021, 21 (1): 1 - 12.

Ariyo A A, Adewumi A O, Ayo C K. Stock price prediction using the ARIMA model [C]//2014 UKSim-AMSS 16th international conference on computer modelling and simulation. IEEE, 2014: 106 - 112.

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Published

08-05-2024