Document Type : بحث
Authors
Abstract
Sales forecasting is the process of predicting how much money someone will make over a certain period of time. It is used to determine how many products you need to make, how much inventory you need to keep on hand, and when you will need to order more. The aim of this study is to forecast and determine the market share of rice using Markov chains model. The Markov chains model is one of the most important quantitative models that decision makers need to know, and its importance arises from its broad application areas, in which it has been successfully applied to predict a system over a given period of time. The results of this study showed that the market share is divided into three-time period. In the first period, the consumption ascending of the rice is as follows: other rice varieties, golden roster rice, ration card rice, Mahmood rice, and Kurdish rice, where as in the second period the consumption ascending of rice is as follows: other rice varieties, golden rooster rice, Mahmood rice and ration card rice, and Kurdish rice In the third period, the consumption ascending of the rice is as follows: other types of rice, Mahmood rice and golden rooster rice, Kurdish rice and ration card rice
Keywords
- A.F. Al-Ani, A.D.K. Alhiyali (2021), “Using Markov Chains to Predict Productivity of Maize in Iraq for the Period (2019-2025)”, Applied Economics and Finance, Vol. 8, No. 5; pp.1-9
- Ahmed, D. A. (2009), “Using Markov Chains to Predict Consumer Price Index Numbers in Iraq”, The first conference of the College of Science, University of Diyala, 6(1), 1-18
- Ritonga, H. Siahaan, A. Suginam (2017), “Marketing Strategy through Markov Optimization to Predict Sales on Specific Periods”, International journal for innovative research in multidisciplinary field, Vol. 3, No. 8, pp.184-190
- Yutong, X. (2021), “Applications of Markov Chain in Forecast”, Journal of Physics: Conference Series, 1848- 012061, pp.1-4
- Permana, D. Pasaribu, U. Indranto, S. Suprayogi, S. (2018), “Convergence of Transition Probability Matrix in CLV- Markov Models”, IOP Conf. Series: Materials Science and Engineering 335-012046. pp.1-4
- Chen, H. Chen, H. Zhang, W. Yang, C. Cui, H. (2021),” Research on Marketing Prediction Model Based on Markov Prediction”, Wireless Communications and Mobile Computing, 2021, Article ID 4535181, pp. 1-9 pages
- Padma and C. Vijayalakshmi, "An Analysis of Continuous Time Markov Chains using Generator Matrices," International Journal of Computer Applications, vol. 35, no. 10, pp. 20-24, 2011.
- Singh, A. P., Manoj Kumar Gaur, Dinesh Kumar Kasdekar, and Sharad Agrawal, “A Study of Time Series Model for Forecasting of Boot in Shoe Industry”, International Journal of Hybrid Information Technology, vol. 8, no. 8, pp. 143 – 152, 2015
- Cardoso, G. and F. Gomide(2007), “Newspaper Demand Prediction and Replacement Model based on Fuzzy Clustering and Rules”, International Journal of Information Sciences, pp. 4799 – 4809.
- Aras J. M., Renas A.A., Mohammad M. F.(2018), “Use the Markov chains to evaluate and predict the Probabilistic Waiting Time for Asia Cell Customers Company for the period (1/9/2014 to 1/6/2015)”, journal of Kirkuk University For Administrative and Economic Sciences
Volume 8, Issue 1, Pages 495-507. - Coolen ACC. (2009),“Markov Chains” ,Compact Lecture Notes and Exercises, Department of Mathematics, King's College London.
- Chan, K. C. (2015),” Market share modelling and forecasting using Markov chains and alternative models”, International Journal of Innovative Computing, Information and Control, 11(4), 1205-1218.
- Tsiliyannis, C. A. (2018),” Markov chain modeling and forecasting of product returns in remanufacturing based on stock mean-age”, European Journal of Operational Research, 271(2), 474-489.
- Chen, H., Chen, H., Zhang, W., Yang, C., & Cui, H. (2021),” Research on marketing prediction model based on Markov prediction”, Wireless Communications and Mobile Computing, 2021, 1-9.
- Yutong, X. (2021, April),” Applications of Markov chain in forecast”, Journal of Physics: Conference Series (Vol. 1848, No. 1, p. 012061). IOP Publishing.
- “Probability distribution “, https://www.probabilitycourse.com/chapter11/11_2_3_probability_distributions.php , Retrieved 2023-04-02
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