Forecasting of jansen's rice inventory control using monte carlo and markov chain techniques
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Abstract
Rice is an essential commodity in Indonesia because of its role as a staple food, which most Indonesians consume daily as a carbohydrate intake. In its development to meet these needs, many things affect the stability of the availability and price of this rice. They are starting from climatic conditions, logistics systems, and the state of the domestic market and the international rice market. On the other hand, the increase in national rice consumption from year to year will continue to grow along with the rise in population. This research aims to apply the Monte Carlo and Markov Chain method to control Jansen rice supplies at the Jansen Rice Mill, Paluh Wave Street, Percut Sei Tuan District, Deli Serdang Regency, North Sumatra Province. The data used is data on rice demand from 2016 to 2021. Monte Carlo forecasts for the next few years, and Markov Chain provides what percentage of opportunities for rice demand to increase or decrease.
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How to Cite
Sari, H. I., Dur, S. ., & Cipta, H. . (2022). Forecasting of jansen’s rice inventory control using monte carlo and markov chain techniques. International Journal of Basic and Applied Science, 11(2), 76–85. https://doi.org/10.35335/ijobas.v11i2.99
References
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[27] L. Zhang, J. Ruiz-Menjivar, Q. Tong, J. Zhang, and M. Yue, “Examining the carbon footprint of rice production and consumption in Hubei, China: a life cycle assessment and uncertainty analysis approach,” J. Environ. Manage., vol. 300, p. 113698, 2021.
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[30] G. D. Hachtel, E. Macii, A. Pardo, and F. Somenzi, “Symbolic algorithms to calculate steady-state probabilities of a finite state machine,” in Proceedings of European Design and Test Conference EDAC-ETC-EUROASIC, 1994, pp. 214–218.
[2] W. Wawan, M. Zuniati, and A. Setiawan, “Optimization of National Rice Production with Fuzzy Logic using Mamdani Method,” J. Multidiscip. Appl. Nat. Sci., 2021.
[3] D. Dawe and C. P. Timmer, “Why stable food prices are a good thing: Lessons from stabilizing rice prices in Asia,” Glob. Food Sec., vol. 1, no. 2, pp. 127–133, 2012.
[4] W. A. N. Wijesooriya and W. H. D. Priyadarshana, “Structure, Conduct and Peformance of Rice Milling Industry in Polonnaruwa and Hambantota Districts of Sri Lanka,” Research Report, 2013.
[5] M. Hossain, F. Naher, and Q. Shahabuddin, “Food security and nutrition in Bangladesh: progress and determinants,” eJADE Electron. J. Agric. Dev. Econ., vol. 2, no. 853-2016–56126, pp. 103–132, 2005.
[6] L. R. Brown, Outgrowing the Earth: the food security challenge in an age of falling water tables and rising temperatures. Routledge, 2012.
[7] I. M. Faisal and S. Parveen, “Food security in the face of climate change, population growth, and resource constraints: implications for Bangladesh,” Environ. Manage., vol. 34, no. 4, pp. 487–498, 2004.
[8] V. Balasubramanian, M. Sie, R. J. Hijmans, and K. Otsuka, “Increasing rice production in sub-Saharan Africa: challenges and opportunities,” Adv. Agron., vol. 94, pp. 55–133, 2007.
[9] M. A. Quasem, “Conversion of agricultural land to non-agricultural uses in Bangladesh: extent and determinants,” Bangladesh Dev. Stud., pp. 59–85, 2011.
[10] P. Dubale, “Soil and water resources and degradation factors affecting productivity in Ethiopian highland agro-ecosystems,” Northeast Afr. Stud., pp. 27–51, 2001.
[11] H. Andersson, A. Hoff, M. Christiansen, G. Hasle, and A. Løkketangen, “Industrial aspects and literature survey: Combined inventory management and routing,” Comput. Oper. Res., vol. 37, no. 9, pp. 1515–1536, 2010.
[12] Y. Yu, Z. Hong, L. L. Zhang, L. Liang, and C. Chu, “Optimal selection of retailers for a manufacturing vendor in a vendor managed inventory system,” Eur. J. Oper. Res., vol. 225, no. 2, pp. 273–284, 2013.
[13] N. Abd Karim, A. Nawawi, and A. S. A. P. Salin, “Inventory management effectiveness of a manufacturing company–Malaysian evidence,” Int. J. Law Manag., 2018.
[14] I. Lazaridis and D. Tryfonidis, “Relationship between working capital management and profitability of listed companies in the Athens stock exchange,” J. Financ. Manag. Anal., vol. 19, no. 1, 2006.
[15] D. C. Bose, Inventory management. PHI Learning Pvt. Ltd., 2006.
[16] D. A. R. Nirmala, V. Kannan, M. Thanalakshmi, S. J. P. Gnanaraj, and M. Appadurai, “Inventory management and control system using ABC and VED analysis,” Mater. Today Proc., vol. 60, pp. 922–925, 2022.
[17] G. C. Souza, “Supply chain analytics,” Bus. Horiz., vol. 57, no. 5, pp. 595–605, 2014.
[18] K. Rezaie, M. S. Amalnik, A. Gereie, B. Ostadi, and M. Shakhseniaee, “Using extended Monte Carlo simulation method for the improvement of risk management: Consideration of relationships between uncertainties,” Appl. Math. Comput., vol. 190, no. 2, pp. 1492–1501, 2007.
[19] S. Raychaudhuri, “Introduction to monte carlo simulation,” in 2008 Winter simulation conference, 2008, pp. 91–100.
[20] W. Li, J. Zhou, K. Xie, and X. Xiong, “Power system risk assessment using a hybrid method of fuzzy set and Monte Carlo simulation,” IEEE Trans. Power Syst., vol. 23, no. 2, pp. 336–343, 2008.
[21] D. Ozturk, “Urban growth simulation of Atakum (Samsun, Turkey) using cellular automata-Markov chain and multi-layer perceptron-Markov chain models,” Remote Sens., vol. 7, no. 5, pp. 5918–5950, 2015.
[22] G. King and L. Zeng, “Explaining rare events in international relations,” Int. Organ., vol. 55, no. 3, pp. 693–715, 2001.
[23] K. Gallagher, K. Charvin, S. Nielsen, M. Sambridge, and J. Stephenson, “Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems,” Mar. Pet. Geol., vol. 26, no. 4, pp. 525–535, 2009.
[24] J. Runge et al., “Inferring causation from time series in Earth system sciences,” Nat. Commun., vol. 10, no. 1, pp. 1–13, 2019.
[25] A. Endo, E. Van Leeuwen, and M. Baguelin, “Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers,” Epidemics, vol. 29, p. 100363, 2019.
[26] B. Shahbaba, L. M. Lomeli, T. Chen, and S. Lan, “Deep markov chain monte carlo,” arXiv Prepr. arXiv1910.05692, 2019.
[27] L. Zhang, J. Ruiz-Menjivar, Q. Tong, J. Zhang, and M. Yue, “Examining the carbon footprint of rice production and consumption in Hubei, China: a life cycle assessment and uncertainty analysis approach,” J. Environ. Manage., vol. 300, p. 113698, 2021.
[28] M. Kuffmeier, S. Frimann, S. S. Jensen, and T. Haugbølle, “Episodic accretion: the interplay of infall and disc instabilities,” Mon. Not. R. Astron. Soc., vol. 475, no. 2, pp. 2642–2658, 2018.
[29] M. Boninsegni, N. V Prokof’Ev, and B. V Svistunov, “Worm algorithm and diagrammatic Monte Carlo: A new approach to continuous-space path integral Monte Carlo simulations,” Phys. Rev. E, vol. 74, no. 3, p. 36701, 2006.
[30] G. D. Hachtel, E. Macii, A. Pardo, and F. Somenzi, “Symbolic algorithms to calculate steady-state probabilities of a finite state machine,” in Proceedings of European Design and Test Conference EDAC-ETC-EUROASIC, 1994, pp. 214–218.
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