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All Journal Jurnal Teknoin Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Sistem dan Manajemen Industri Jurnal Sistem Teknik Industri Matrik : Jurnal Manajemen dan Teknik Industri Produksi JURNAL TEKNIK INDUSTRI Jurnal Akademik Pengabdian Masyarakat
Irwan Iftadi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Transportation

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Application of Exponential Smoothing Models for Coal Demand Forecasting in Cement Plants Indra Maulana, Rizky; Irwan Iftadi
Teknoin Vol. 31 No. 1 (2026)
Publisher : Faculty of Industrial Technology Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/teknoin.vol31.iss1.art3

Abstract

Energy forecasting plays an important role in maintaining operational stability and inventory efficiency in the cement manufacturing industry, where coal remains the primary source of thermal energy. This study aims to develop an accurate forecasting model to predict coal demand in the procurement and warehouse division of a cement manufacturing plant in West Java, Indonesia. A quantitative approach was applied using three time-series forecasting methods, namely Moving Average (MA), Single Exponential Smoothing (SES), and Holt’s Double Exponential Smoothing (DES). Monthly coal consumption data from 2022 to 2024 were analyzed and divided into training and testing datasets to evaluate out-of-sample forecasting performance. Several parameter combinations were tested to obtain the optimal forecasting configuration for each model. Forecasting accuracy was assessed using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results show that Holt’s DES achieved the best forecasting performance, with a MAPE of 6.21%, outperforming SES and MA, which had MAPE values of 9.84% and 11.47%, respectively. The selected model also reduced the average deviation between forecasted and actual coal demand to below 500 tons per month, thereby minimizing the risk of overstocking and stockouts. These findings demonstrate that quantitative forecasting can support more effective procurement planning, improve inventory control, and enhance energy management practices in cement manufacturing operations. Nevertheless, this study is limited to a three-year observation period and focuses on a single industrial case, which may limit the generalizability of the results.
Co-Authors Aisyah Latifah Angelica Fatimah Fatmawati Anisa Zahra Naufani Antika Adzary Sekar Fadlilah Antika Adzary Sekar Fadlilah Auldy Setia Mahendra Bela Sindy Amelinda Carinda Adistiara Cyntia Nathania Daffa Thufail Ramadhan Desika Anggi Fitriana Diah Ayu Argiyanti Saputri Fadhilah Al Karimah Fathika Fadhiella Anhar Fathin Kusumo Pramesti Pudjiantoro Florentina Ardiani Wibowo Galuh Qodrina Ghina Nurrahma Giusti Arcibal Hardiana, Fatma Rizki Hubert Christian Kanilod Sitohang Indah Saputri Indira Kusuma Wardani Indra Maulana, Rizky Novi Devita Sari Pringgo Widyo Laksono Rahmaniyah Dwi Astuti Rima Tri Fatmawati Shasya Elyandra Salsabila WAKHID AHMAD JAUHARI Yudha Rahadian Zidan Maulana