Comparison of Sales Models in the Post-epidemic Era

Research Article
Open access

Comparison of Sales Models in the Post-epidemic Era

Xiaoyu Zhang 1* , Ruibo Chen 2
  • 1 Department of Mathematics, University of Washington, Seattle, United States; Schmitz Hall, Box 355852, 1410 NE Campus Parkway, Seattle, WA, 98195    
  • 2 Department of Engineering, University of Washington, Seattle, United States; Schmitz Hall, Box 355852, 1410 NE Campus Parkway, Seattle, WA, 98195    
  • *corresponding author Rayne6537@gmail.com
Published on 24 April 2023 | https://doi.org/10.54254/2754-1169/5/20220069
AEMPS Vol.5
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-21-8
ISBN (Online): 978-1-915371-22-5

Abstract

Compared with the traditional sales forecast based on human experience calculation and intuition prediction, combining existing sales data and model predictions can often accurately determine the market trend and reduce the probability of judgment error. A high degree of precision in market forecasting is essential for business leaders to develop policies and plans, increase store turnover and reduce operating costs. Based on the analysis of the historical sales data of China’s liquor retail industry in the post-epidemic era, this study compared two existing forecasting models for forecast accuracy to determine the most suitable model for long-term sales forecasting. In this study, the sales data were collected from Suhe Bar Chain under the company of Alliance Art Group. The raw data set was screened and cleaned to suit each model, and then applied to Linear Regression Model (LRM) and Autoregressive Integrated Moving Average Model (ARIMA) to generate new forecast data. The predicted data were compared with the real sales data, and Root Mean Square Error (RMSE) was used to judge the accuracy of the model prediction. Finally, ARMIA is the better model to predict China’s liquor retail in the post-epidemic era. According to the model predictions, reducing inventory, maintaining efficient cash flow, improving the turnover efficiency of goods, and strengthening the ability to adjust market strategy are the more suitable strategies for current liquor sales enterprises.

Keywords:

Linear Regression Model, Autoregressive Integrated Moving Average Model, Sales Forecasting, Liquor Retail, Post-pandemic Era

Zhang,X.;Chen,R. (2023). Comparison of Sales Models in the Post-epidemic Era. Advances in Economics, Management and Political Sciences,5,109-119.
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References

[1]. D. Liu, W. Sun, and X. Zhang. “Is the Chinese economy well positioned to fight the COVID-19 pandemic? The Financial Cycle Perspective,” Emerging Markets Finance and Trade, vol. 56, no. 10, pp. 2259–2276. 2020.

[2]. J. Zhao, F. Xiong, and P. Jin. “Enhancing short-term sales prediction with microblogs: A case study of the movie Box Office,” Future Internet, vol. 14, no. 5, p. 141. 2022

[3]. D. Bloznelis. “Short-term Salmon Price forecasting,” Journal of Forecasting, vol. 37, no. 2, pp. 151–169. 2017.

[4]. J. A. Hoyle, R. Dingus, and J. H. Wilson. “An exploration of sales forecasting: Sales manager and salesperson perspectives,” Journal of Marketing Analytics, vol. 8, no. 3, pp. 127–136. 2020.

[5]. D. Wong. “China’s city-tier classification: How does it work?,” China Briefing News. [Online]. https://www.china-briefing.com/news/chinas-city-tier-classification-defined/. 19-Apr-2021

[6]. T. L. Nguyen, N. T. Nguyen, and V. C. Nguyen. “Identifying factors influencing on the profitability of Tourist Enterprises: Evidence from Vietnam,” Management Science Letters, pp. 1933–194. 2019

[7]. Satyavishnumolakala. “Linear regression -Pros & Cons,” Medium [Online]. Available: https://medium.com/@satyavishnumolakala/linear-regression-pros-cons-62085314aef0. 12-Jun-2020

[8]. S. Barak and S. S. Sadegh. “Forecasting energy consumption using ensemble Arima–ANFIS hybrid algorithm,” International Journal of Electrical Power & Energy Systems, vol. 82, pp. 92–104. Nov. 2016

[9]. “RMSE: Root mean square error,” Statistics How To [Online]. Available: https://www.statisticshowto.com/probability-and-statistics/regression-analysis/rmse-root-mean-square-error/. 31-May-2021.

[10]. J. Moody, “What does RMSE really mean? - towards data science,” Towards Data Science. [Online]. Available: https://towardsdatascience.com/what-does-rmse-really-mean-806b65f2e48e. 05-Sep-2019.

Cite this article

Zhang,X.;Chen,R. (2023). Comparison of Sales Models in the Post-epidemic Era. Advances in Economics, Management and Political Sciences,5,109-119.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2022 International Conference on Financial Technology and Business Analysis (ICFTBA 2022), Part 1

ISBN：978-1-915371-21-8(Print) / 978-1-915371-22-5(Online)
Editor：Javier Cifuentes-Faura, Canh Thien Dang
Conference website: http://www.icftba.org
Conference date: 16 December 2022
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.5
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. D. Liu, W. Sun, and X. Zhang. “Is the Chinese economy well positioned to fight the COVID-19 pandemic? The Financial Cycle Perspective,” Emerging Markets Finance and Trade, vol. 56, no. 10, pp. 2259–2276. 2020.

[2]. J. Zhao, F. Xiong, and P. Jin. “Enhancing short-term sales prediction with microblogs: A case study of the movie Box Office,” Future Internet, vol. 14, no. 5, p. 141. 2022

[3]. D. Bloznelis. “Short-term Salmon Price forecasting,” Journal of Forecasting, vol. 37, no. 2, pp. 151–169. 2017.

[4]. J. A. Hoyle, R. Dingus, and J. H. Wilson. “An exploration of sales forecasting: Sales manager and salesperson perspectives,” Journal of Marketing Analytics, vol. 8, no. 3, pp. 127–136. 2020.

[5]. D. Wong. “China’s city-tier classification: How does it work?,” China Briefing News. [Online]. https://www.china-briefing.com/news/chinas-city-tier-classification-defined/. 19-Apr-2021

[6]. T. L. Nguyen, N. T. Nguyen, and V. C. Nguyen. “Identifying factors influencing on the profitability of Tourist Enterprises: Evidence from Vietnam,” Management Science Letters, pp. 1933–194. 2019

[7]. Satyavishnumolakala. “Linear regression -Pros & Cons,” Medium [Online]. Available: https://medium.com/@satyavishnumolakala/linear-regression-pros-cons-62085314aef0. 12-Jun-2020

[8]. S. Barak and S. S. Sadegh. “Forecasting energy consumption using ensemble Arima–ANFIS hybrid algorithm,” International Journal of Electrical Power & Energy Systems, vol. 82, pp. 92–104. Nov. 2016

[9]. “RMSE: Root mean square error,” Statistics How To [Online]. Available: https://www.statisticshowto.com/probability-and-statistics/regression-analysis/rmse-root-mean-square-error/. 31-May-2021.

[10]. J. Moody, “What does RMSE really mean? - towards data science,” Towards Data Science. [Online]. Available: https://towardsdatascience.com/what-does-rmse-really-mean-806b65f2e48e. 05-Sep-2019.

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