IMPROVING EFFICIENCY THROUGH THE DIGITALIZATION AND FORECASTING OF MEDICINAL PLANT PRODUCT SALES
DOI:
https://doi.org/10.55640/Keywords:
Medicinal plants, sales mechanism, digitalization, forecasting, ARIMA model, sales efficiency, economic analysis, marketing, time series.Abstract
This article investigates the issues of improving the sales mechanism of medicinal plant products through digitalization and forecasting. During the research process, the current state of the sales system was analyzed, and its main challenges were identified, particularly the insufficient digitalization of sales processes, weaknesses in customer relationship management, and the underdevelopment of demand forecasting mechanisms.
Within the research methodology, a systematic approach, economic-statistical analysis, and econometric modeling methods were employed. In particular, sales volume forecasting was carried out using the ARIMA model.
The practical results demonstrated that the implementation of digitalization and forecasting approaches in the sales of medicinal plant products significantly enhances business performance. Specifically, sales volume increased from 40 million UZS to 65 million UZS, representing a growth of 62.5%.
Based on the research findings, practical recommendations were developed to improve the sales of medicinal plant products. These recommendations include the automation of sales processes, the systematization of customer relationship management, and the adoption of forecasting-based managerial decision-making.
References
1.World Health Organization. WHO global report on traditional medicine. – Geneva, 2019.
2.Kala C.P. Medicinal plants and their role in rural development // Journal of Medicinal Plants, 2015.
3.Hamilton A.C. Medicinal plants conservation and livelihoods // Biodiversity and Conservation, 2004.
4.Porter M.E. Competitive advantage of nations. – New York: Free Press, 1990.
5.Brynjolfsson E., McAfee A. The second machine age. – New York, 2014.
6.Kotler P., Keller K. Marketing management. – Pearson, 2016.
7.Chaffey D. Digital marketing. – Pearson, 2019.
8.Saik R. Food 5.0: How we feed the future. – 2021.
9.FAO. The State of Agricultural Commodity Markets. – Rome, 2020.
10.OECD. Digital transformation in agriculture. – Paris, 2019.
11.Box G.E.P., Jenkins G.M. Time series analysis: forecasting and control. – 2015.
12.Gujarati D.N. Basic econometrics. – McGraw-Hill, 2012.
13.Kala C.P. Medicinal plants and rural livelihoods // Journal of Medicinal Plants, 2015.
14.Porter M.E. Competitive strategy. – New York: Free Press, 2008.
15.OECD. Agricultural policy and digitalisation. – Paris, 2021.
16.Enders W. Applied econometric time series. – 2014.
17.Porter M.E. Competitive advantage. – 2008.
18.Hyndman R.J., Athanasopoulos G. Forecasting principles and practice. – 2018.
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