A study of fresh produce pricing and replenishment strategy based on FCM cluster analysis and ARIMA-GARCH time series analysis modelling

Miao Wang

doi:10.54097/2kmm6k79

Authors

Miao Wang

DOI:

https://doi.org/10.54097/2kmm6k79

Keywords:

Isolated Forest, Genetic Algorithm, FCM Cluster Analysis, ARIMA-GARCH.

Abstract

Aiming at the shortcomings of poor robustness and weak applicability in the formulation of pricing and replenishment strategies of fresh food superstores and the limitations of ARIMA model in pricing strategies, this paper is based on maximizing superstores' revenue, and proposes to establish an FCM cluster analysis model based on the elbow rule to analyse the correlation of different single products, and concludes that the sales volume of edible mushrooms and aquatic roots and tubers have a strong correlation, and spinach and choy sum have a strong correlation. Then the total sales volume of each category was negatively correlated with cost-plus pricing through least squares fitting. Finally, based on the ARIMA-GARCH model, a daily replenishment model was established, and it was predicted that the maximum daily replenishment of foliage and flowers would reach 213.908kg on 2 July, and the minimum daily replenishment of eggplant would reach 8.415kg on 4 July in the coming week.

Downloads

Download data is not yet available.

References

[1] Zhu Xiaojing. Research on the development of cold chain logistics of fresh agricultural products under the strategy of rural revitalization [J]. China Storage and Transportation, 2024, (07):52-53.

[2] He Koon. Research on Cost Control of Fresh Food Cold Chain Logistics Based on New Retail Environment of E-commerce [J]. Shanxi Agricultural Economics, 2023, (18):133-135.

[3] Zeng Jiayun,Zhou Taizi. Risk evaluation of fresh food e-commerce supply chain based on community group purchase [J]. China Storage and Transportation, 2024, (05):117.

[4] Li Zheng. Optimization of cold chain logistics of fresh agricultural products [J]. China Logistics and Purchasing, 2023, (15):85-87.

[5] Qin Zhi-dan, Fu Zhi-Yan, LI Jiao. Fresh agricultural supply chain decision considering options and spot market under epidemic risk [J]. Logistics Technology, 2019, 47(13):103-107.

[6] Zhenna Z, Bin Z, Ruijin Y, et al. Recent Developments in the Preservation of Raw Fresh Food by Pulsed Electric Field [J].Food Reviews International, 2022, 38(S1):247-265.

[7] Jinjin H, Min Z, S A M, et al. Technological innovations enhance postharvest fresh food resilience from a supply chain perspective. [J].Critical reviews in food science and nutrition, 2023, 21-23.

[8] Jiang Yingmei, Mou Jinjin. Joint inventory and pricing decision of fresh processed products based on perceived freshness [J]. Journal of Highway and Transportation Science and Technology, 2019, 37(03):151-158.

[9] Zhao J. Pricing and replenishment decision of supermarket vegetables based on optimal profit planning model. National Circulation Economy, 2024, (09):8-12.

[10] Nie Yuxuan. Research on automatic pricing and replenishment strategy of fresh commodities based on ARIMA prediction optimisation model--taking vegetable commodities as an example [J]. Commercial Exhibition Economy, 2024, (05):19-22.

[11] Yang Shuai, HUANG Xiangmeng, WANG Junbin. Research on joint optimization strategy of fresh food shelf distribution and pricing [J]. Supply Chain Management,2022,3(08):49-59

[12] Wang Shaohan, Han Yi, Wang Xiangyu, et al. Two-step ship type classification mapping based on K-Means and XG-Boost algorithms [J]. Journal of Shanghai Marine Transport Research Institute, 2019, 46(03):28-34.

[13] Li Kaian, Zhang Yu, Ren Xuemei, et al. Effect of different grape varieties on aroma of melon-grape complex wine [J/OL]. Journal of Gansu Agricultural University, 2024, 1-16.

[14] Nie Y X. Research on automatic pricing and replenishment strategy of fresh commodities based on ARIMA predictive optimization model -- A case study of vegetable commodities [J]. Trade Fair Economics, 2024, (05):19-22.

[15] ZHAO Yongzhi. Research on the prediction method of tunnel surrounding rock deformation based on LSTM-ARIMA model [J]. National Defence Transportation Engineering and Technology, 2024, 22(04):21-26.