PERAMALAN PERMINTAAN PRODUK TIRAI MENGGUNAKAN METODE MOVING AVERAGE DAN EXPONENTIAL SMOOTHING (Studi Kasus pada Meubel Sarira Waitatiri Maluku Tengah)

Grace Fredriksz
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Abstract

This study aims to determine the most effective method of demand forecasting for curtain products at the Sarira furniture company in Waitatiri, Central Maluku, so that the production of the goods is not too many. The demand forecasting method used in this research is the Moving Average method and the Exponential Smoothing method. The methods analyzed are the 3-month and 5-month Moving Average methods, as well as Exponential Smoothing with values of 0.1, 0.5, and 0.9. The results of each method are compared based on the level of error. The calculation of how far a prediction is made is called the forecast error rate. This calculation is carried out for curtain products, and MSE is used to represent the error rate. The demand for curtains with a Mean Squared Error (MSE) is: moving average 3-month = 314.89. 5-month moving average = 222.29. Exponential Smoothing 0.1 = 256.82. Exponential Smoothing 0.5 = 290.09. Exponential Smoothing 0.9 = 393.09. So, the most efficient method for forecasting curtain products is the 5-month moving average method because the error rate or MSE of 222.29 is very small.
本研究旨在确定最有效的需求预测方法,窗帘产品在萨里拉家具公司在怀塔蒂里,中央马鲁古,使生产的商品不是太多。本研究中使用的需求预测方法是移动平均法和指数平滑法。分析的方法是3个月和5个月移动平均法,以及0.1,0.5和0.9的指数平滑法。根据误差水平对每种方法的结果进行比较。预测误差的计算称为预测错误率。对窗帘产品进行此计算,错误率用MSE表示。窗帘的均方误差(MSE)需求为:3个月移动平均= 314.89。5个月移动平均线= 222.29。指数平滑0.1 = 256.82。指数平滑0.5 = 290.09。指数平滑0.9 = 393.09。因此,预测窗帘产品最有效的方法是5个月移动平均法,因为误差率或MSE为222.29非常小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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