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A Study on the Supermarket Replenishment and Pricing Strategies Based on SARIMA Model

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DOI: 10.23977/ferm.2023.061128 | Downloads: 5 | Views: 256

Author(s)

Qifan Wang 1

Affiliation(s)

1 College of Science, China University of Petroleum (East China), Qingdao, Shandong, 266580, China

Corresponding Author

Qifan Wang

ABSTRACT

In the realm of fresh produce supermarkets, the expedited turnover of vegetable products necessitates routine replenishment and pricing adjustments by supermarkets. Nonetheless, formulating effective strategies for these aspects presents a substantial challenge to merchants. To address this issue, this study employs the SARIMA prediction model to anticipate future replenishment volumes and pricing for vegetable products in supermarkets. The cost-plus pricing method serves as the foundation for pricing in this predictive analysis. The predictive findings highlight a high accuracy in foreseeing both the upcoming month's replenishment volume and pricing. These forecasts reveal a fluctuating trend, displaying consistent periodicity akin to previous years, indicative of a robust predictive capacity. Consequently, this study culminates in devising a supermarket replenishment and pricing strategy grounded in the SARIMA model. This strategy aims to facilitate improved planning of future restocking and pricing by merchants, thereby fostering enhanced sales of vegetable products within supermarkets.

KEYWORDS

Vegetable Products, Merchandise Replenishment, Merchandise Pricing, SARIMA Forecasting, Cost Plus Pricing Method

CITE THIS PAPER

Qifan Wang, A Study on the Supermarket Replenishment and Pricing Strategies Based on SARIMA Model. Financial Engineering and Risk Management (2023) Vol. 6: 193-200. DOI: http://dx.doi.org/10.23977/ferm.2023.061128.

REFERENCES

[1] Lin Feng, Wan-Chih Wang, Jinn-Tsair Teng, Leopoldo Eduardo Cárdenas-Barrón. Pricing and lot-sizing decision for fresh goods when demand depends on unit price, displaying stocks and product age under generalized payments. European Journal of Operational Research [J].2022, 296(3):940-952.
[2] Cenying Yang, Yihao Feng, Andrew Whinston. Dynamic Pricing and Information Disclosure for Fresh Produce: An Artificial Intelligence Approach. Production and operations management [J].2022, 31(1):155-171.
[3] Rayner Alfred, Christylyn Leikson, B. Boniface, G. Tanakinjal, A. Kamu, Mori Kogid, S. Sondoh, Nolila Mohd Nawi, N. Arumugam, R. Andrias. Modelling and Forecasting Fresh Agro-Food Commodity Consumption Per Capita in Malaysia Using Machine Learning. Hindawi [J].2022, 2022(1):17.
[4] Lobna Nassar, Ifeanyi Emmanuel Okwuchi, Muhammad Saad, Fakhri Karray, Kumaraswamy Ponnambalam. Deep Learning Based Approach for Fresh Produce Market Price Prediction. International Joint Conference on Neural Networks (IJCNN) [J].2020:1-7.
[5] Islam Nasr, L. Nassar, F. Karray. Transfer Learning Framework for Forecasting Fresh Produce Yield and Price. International Joint Conference on Neural Networks (IJCNN) [J].2022, (3):1-8.
[6] Manish Shukla, Sanjay Jharkharia. Applicability of ARIMA Models in Wholesale Vegetable Market: An Investigation. International Journal of Information Systems and Supply Chain Management [J].2013, 6(3):105-119.
[7] Xuping Wang, Dongping Lin, Wenping Fan, Tianteng Wang. Research on Sales Forecast of Fresh Produce Considering Weather Factors. International Conference on Electronic Business (ICEB) [J].2018:541-548.
[8] Siti Nurhaliza, Wan Junita Raflah. The Application of a Cost-Plus Pricing Method in Determining the Selling Price of Dried Lomek Products (Case Study at Bumdes Kuala Alam). Inovbiz Jurnal Inovasi Bisnis Seri Manajemen Investasi dan Kewirausahaan (INOVBIZ) [J].2022:154-161.
[9] E. Kartika, M. R. Bakhtiar. Penentuan Cost of Goods Sold dan Penerapan Cost Plus Pricing Method dalam Menentukan Harga Jual: Study Penggilingan Padi UD Budi Luhur. Media Akuntansi Universitas Muhammadiyah Semarang [J]. 2020, 10(2):67-73.
[10] Nari Sivanandam Arunraj, Diane Ahrens, Michael Fernandes. Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry. International Journal of Operations Research and Information Systems (IJORIS )[J]. 2016, 7(2):21.

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