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All Journal Indonesian Journal of Innovation Studies
Lely Lindyawati
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Computer Science & IT Education Engineering Law, Crime, Criminology & Criminal Justice

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Indonesia's Breakthrough in Optimized Yarn Forecasting for Textile Demand Accuracy: Terobosan Indonesia dalam Peramalan Benang yang Dioptimalkan untuk Akurasi Permintaan Tekstil Lely Lindyawati; Indah Apriliana Sari W; Atikha Sidhi Cahyana; Tedjo Sukmono
Indonesian Journal of Innovation Studies Vol. 25 No. 3 (2024): July
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i3.1164

Abstract

PT. XY, a textile company specializing in woven sarongs, faces fluctuating demand during Islamic religious celebrations, impacting production. In Ramadhan 2023, production increased by 30%, but warp yarn availability was insufficient. This study forecasts warp yarn production over twelve periods, comparing Double Exponential Smoothing Holt’s (DES) and Holt-Winter’s Exponential Smoothing (WES) methods, optimized using the golden section method. Using historical data from January 2021 to April 2023, WES with golden section parameters (α1 = 0.67387, β1 = 0.08756, γ2 = 0.85408) achieved the best accuracy with a MAPE of 5.5437%. The WES method is recommended for improving production planning at PT. XY, with future research suggested to explore production correlations and procurement costs. Highlight: Demand Fluctuation: PT. XY experiences significant demand changes during Islamic religious celebrations. Forecasting Methods: Comparing DES and WES methods for predicting warp yarn production. Optimal Accuracy: WES with golden section optimization achieved the lowest MAPE of 5.5437%. Keywoard: Textile Industry, Warp yarn forecasting, Production Planning, Holt-Winter's method, Golden section optimization
Co-Authors Atikha Sidhi Cahyana Indah Apriliana Sari W Tedjo Sukmono