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Analisis Peramalan Permintaan Untuk Penentuan Metode Peramalan Permintaan Terbaik Pada Komoditas Batu Bara: Studi Kasus PT MMSGI

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Coal is the main product for PT MMSGI. As Indonesia's primary source of electrical energy, coal must be considered in terms of supply to prevent losses. This study aims to determine the best method that can be applied to forecast coal demand by comparing four quantitative time series forecasting methods. The four forecasting methods are moving average, weighted moving average, exponential smoothing, and linear regression. Forecasting error is calculated using the Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) to compare the four forecasting methods. The forecasting method with the lowest error value then tested by Tracking Signal (TS) to see whether the demand forecasting results are acceptable. The analysis uses historical coal sales data from January 2021 to June 2022. The data collection process was carried out through interviews both in person and online, field observations, as well as from various supporting sources. The results of this study stipulates that the best method with the lowest error value is linear regression. Based on the tracking signal, the forecasting results are within the control limits, indicates that the forecasting results are acceptable and linear regression is the best forecasting method that can be applied to control coal supply. The best forecasting method is then used to forecast coal demand for the years 2023 – 2025.

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Ketersediaan

Call Number	Location	Available
13896	PSB lt.2 - Karya Akhir	1

Penerbit	Depok Program Studi Manajemen Fakultas Ekonomi dan Bisnis Universitas Indonesia., 2022
Edisi	-
Subjek	Coal Time series analysis Linear regression Demand Forecasting
ISBN/ISSN	-
Klasifikasi	NONE
Deskripsi Fisik	xiv, 76 p. ; diagr. ; 30 cm
Info Detail Spesifik	-
Other Version/Related	Tidak tersedia versi lain
Lampiran Berkas	Tidak Ada Data