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Forecasting Methods Comparation Based on Seasonal Patterns for Predicting Medicine Needs with ARIMA Method, Single Exponential Smoothing

机译:基于季节性模式的预测方法与ARIMA方法预测药物需要的季节性模式,单指数平滑

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The purpose of this study is to predict the needs of medicines by using forecasting techniques and calculating the value of Economic Order Quantities. Fluctuations in the use of drugs that occur every year is an obstacle for the drug warehouse in planning procurement in hospitals. The method used in this study is ARIMA time series forecasting for the process of prediction and calculation of EOQ. The results of this study in the form of an estimated value of drug needs for one coming period is shown by the smallest forecasting error value, namely ARIMA (1.0.0) with an error value of 13%, and the results of calculations of the Quantity of Economic Order for future drug needs. Forecasting results between ARIMA and the Exponential Smoothing method show that forecasting has the smallest error value, using ARIMA (1.0.0).
机译:本研究的目的是通过使用预测技术来预测药物的需求并计算经济秩序数量的价值。 每年出现的药物的使用波动是医院规划采购中的药物仓库的障碍。 本研究中使用的方法是ARIMA时间序列预测,用于EOQ的预测过程。 该研究的结果以一个未来一段时间的药物需求的估计值的形式表现出最小的预测误差值,即误差值为13%的Arima(1.0.0),以及计算结果 未来药物需求的经济秩序数量。 ARIMA与指数平滑方法之间的预测结果表明,使用ARIMA(1.0.0),预测预测具有最小的误差值。

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