Abstract
The AA manufacturing factory is a form of mass-producing and selling products in order to respond to customer’s needs. This means an excessive amount of material supply and demand for companies to reduce losses associated with short inventory. This results in products that fail to respond to demand accumulating in managed warehouses, resulting in higher inventory maintenance costs. In this paper, as a measure to reduce costs and inventory shortage to complement these problems, we propose a plan that predicts future demand. In order to solve the problem, ARIMA model, which is a time series analysis technique, is used to predict demand in the temporal variability or seasonal factor, and to develop a demand-forecasting model based on the EOQ model. We also ran simulation to evaluate the effectiveness of the model, and in future research, we will apply it to small and medium enterprises, to demonstrate the effectiveness of the model.
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References
Dhandayuthapani, S.P., Susmitha, G.: Inventory management at Arignar Anna Sugar mills, Kurungulam. IJSRD - Int. J. Sci. Res. Dev. 5(09), 1080–1082 (2017)
Harris, F.W.: How many parts to make at once. Int. J. Prod. Econ. 155, 8–11 (2014)
Wang, Y., Geng, X., Zhang, F., Ruan, J.: An immune genetic algorithm for multi-echelon inventory cost control of IOT based supply chains. IEEE Access 6, 8547–8555 (2018)
Soliman, A.M., Zaki, A.M., El-Shafei, A.M., Mahgoub, O.A.: Application of grey jump model to forecast fluctuating inventory demand. In: 2008 IEEE International Conference on Service Operations and Logistics, and Informatics, vol. 1, pp. 1209–1214 (2008)
Okay Akyuz, A., Uysal, M., Atak Bulbul, B., Ozan Uysal, M.: Ensemble approach for time series analysis in demand forecasting. In: 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 7–12 (2017)
Liao, S., Zhou, L., Di, X., Yuan, B., Xiong, J.: Large-scale short-term urban taxi demand forecasting using deep learning. In: 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 428–433 (2018)
Dongardive, J., Abraham, S.: Reaching optimized parameter set: protein secondary structure prediction using neural network. Neural Comput. Appl. 28(8), 1947–1974 (2016)
Efrilianda, D.A., Mustafid, R., Isnanto, R.: Inventory control systems with safety stock and reorder point approach. In: 2018 International Conference on Information and Communications Technology (ICOIACT), pp. 844–847 (2018)
Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis; Forecasting and Control, 8th edn. Wiley, Oxford (2008)
Persson, F., Axelsson, M., Edlund, F., Lanshed, C., Lindström, A., Persson, F.: Using simulation to determine the safety stock level for intermittent demand. In: 2017 Winter Simulation Conference (WSC), pp. 3768–3779 (2017)
Liu, G., Zong, X.: Research of second-hand real estate price forecasting based on data mining. In: IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, pp. 1675–1679 (2017)
Yuniarti, T., Surjandari, I., Muslim, E., Laoh, E.: Data mining approach for short term load forecasting by combining wavelet transform and group method of data handling (WGMDH). In: 2017 3rd International Conference on Science in Information Technology (ICSITech), Bandung, pp. 53–58 (2017)
Alsudan, R.S.A., Liu, J.: The use of some of the information criterion in determining the best model for forecasting of Thalassemia cases depending on Iraqi patient data using ARIMA model. J. Appl. Math. Phys. 5(3), 667–679 (2017)
Makridakis, S., Wheelwrigt, S.C., Hyndman, R.J.: Forecasting: Methods and Applications, 3rd edn. Wiley, New York (1998)
Acknowledgment
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03933828). Corresponding author: Prof. Jongpil Jeong.
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Kim, JA., Jeong, J. (2018). Simulation Evaluation for Efficient Inventory Management Based on Demand Forecast. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1_44
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DOI: https://doi.org/10.1007/978-3-319-95162-1_44
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