印度尼西亚在优化纱线预测以提高纺织品需求准确性方面取得突破

Lely Lindyawati, I. Sari W, A. S. Cahyana, Tedjo Sukmono
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引用次数: 0

摘要

PT.XY 是一家专门生产纱笼的纺织公司,在伊斯兰宗教庆典期间面临着需求波动,影响了生产。在 2023 年斋月,产量增加了 30%,但经纱供应不足。本研究比较了霍尔特双指数平滑法(DES)和霍尔特-温特指数平滑法(WES),并使用黄金分割法进行了优化,预测了十二个时期的经纱产量。使用 2021 年 1 月至 2023 年 4 月的历史数据,WES 的黄金分割参数(α1 = 0.67387,β1 = 0.08756,γ2 = 0.85408)达到了最佳精度,MAPE 为 5.5437%。建议使用 WES 方法改进 PT.XY 公司的生产计划。XY 公司的生产计划,并建议未来研究探索生产相关性和采购成本。亮点: 需求波动:PT.在伊斯兰宗教庆典期间,XY 公司的需求变化很大。预测方法:比较 DES 和 WES 预测经纱产量的方法。最佳精度:采用黄金分割优化的 WES 实现了最低的 MAPE(5.5437%)。 关键字纺织工业、经纱预测、生产计划、霍尔特-温特方法、黄金分割优化
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Indonesia's Breakthrough in Optimized Yarn Forecasting for Textile Demand Accuracy
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
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