Research on Vegetable Pricing and Replenishment Based on LSTM Time Series Prediction Method
DOI:
https://doi.org/10.54097/qhtpah57Keywords:
Fresh vegetables; Vegetable pricing and replenishment; Dynamic pricing; LSTM time series.Abstract
In the operation of fresh supermarkets, vegetable products exhibit significant price volatility compared to other commodities due to their perishability, freshness, and susceptibility to losses. This study utilizes historical sales and pricing data from supermarkets and employs the LSTM neural network forecasting model to conduct predictive analysis on the replenishment decisions and pricing strategies in fresh supermarkets. The results demonstrate that the proposed forecasting model achieves favorable predictive accuracy, revealing a certain correlation and regularity between the replenishment decisions in fresh supermarkets and historical time series data. This research offers positive economic benefits in guiding the variations of prices and replenishment quantities.
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Pan Lin,Xu Xiajing,Zhou Rongting.Research on Dual Channel Dynamic Pricing of Community Fresh Food Supply Chain from the Perspective of Competition[J/OL].Chinese Journal of Management Science,2021,1506:1-12.
Yu Z .Vegetable income insurance pricing: Based on semi-parametric Copula method[J].Research of Agricultural Modernization, 2019,40(2): 308-315.
McLaughlin E W. The dynamics of fresh fruit and vegetable pricing in the supermarket channel[J]. Preventive Medicine, 2004, 39: 81-87
Qin Y, Wang J, Wei C. Joint pricing and inventory control for fresh produce and foods with quality and physical quantity deteriorating simultaneously[J]. International Journal of Production Economics, 2014, 152: 42-48.
Shukla M , Jharkharia S .Applicability of ARIMA Models in Wholesale Vegetable Market: An Investigation[J].International Journal of Information Systems and Supply Chain Management (IJISSCM), 2013, 6(3):105-119.
Bi Wenjie,Zhou Yubing.Research on inventory control and dynamic princing of fresh produce based on deep reinforcement learning[J].Application Research of Computers, 2022,39(09): 2660-2664.
Kartika E , Bakhtiar M R .Penentuan Cost of Good Sold dan Penerapan Cost Plus Pricing Method dalam Menentukan Harga Jual: Study Penggilingan Padi UD Budi Luhur[J]. 2020,10(2):67-73.
Han Junhua,Gan shengdao.Review of Cost-Plus Pricing Method[J].Finance and Accounting Monthly, 2012, 22(No.626):76-77.
Wang X , Wu J , Liu C ,et al.Exploring LSTM based recurrent neural network for failure time series prediction[J].Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4):772-784.
Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural computation, 2019, 31(7): 1235- 1270.
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