[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119416-en":3,"doc-seo-119416-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119416,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","The Design Of A Demand Forecasting Model Of Glass Bottled Tea Products With Machine Learning Approach","Accurate sales forecasting is essential for maximizing profits because it directly supports inventory management and production planning reliability. Using computational intelligence, machine learning and AI can continuously detect patterns in historical data to reduce demand uncertainty. This study compares a machine learning forecasting model with PT XYZ’s existing method for glass-bottled tea. Weekly demand is modeled with RNN, incorporating calendar holidays, temperature, and population data, yielding 99.47% accuracy and a 0.53% error rate.","Jurnal Sistem Teknik Industri  \nJournal homepage: [https://talenta.usu.ac.id/jsti](https://talenta.usu.ac.id/jsti)  \nThe Design Of A Demand Forecasting Model Of Glass Bottled Tea Products With Machine Learning Approach  \nSaid Munal Akid 1 , Aulia Ishak*1 , Sukaria Sinulingga 1  \n1Department of Industrial Engineering, Faculty of Engineering, Universitas Sumatera Utara, Jl. Dr. T. Mansur No.9, Medan, 20155, Indonesia  \n*[Corresponding Author:](Corresponding Author: aulia.ishak@usu.ac.id)[ aulia.ishak@usu.ac.id](Corresponding Author: aulia.ishak@usu.ac.id)  \nARTICLE INFO  \n ABSTRACT   \nArticle history:  \nReceived 30 June 2024  \nRevised 2 December 2024 Accepted 2 January 2025 Available online 29 April 2025  \nE-ISSN: 2527-9408  \nP-ISSN:  1411-5247  \nHow to cite:  \nAkid, S. M., Ishak, A., & Sinulingga, S. (2025) . The Design Of A Demand Forecasting Model Of Glass Bottled Tea Products With Machine Learning Approach. Jurnal Sistem Teknik Industri, 27(2), 57-65.  \nAn accurate sales forecasting is crucial to the profits earned because it affects the company's stock management. With computational support, machine learning and artificial intelligence can continuously and automatically recognize patterns in data, thereby reducing the risk of demand unpredictability. PT XYZ is one of the companies in industrial sector that produces various beverage products. The factory in Medan. One of the products is the tea glass bottle. At PT XYZ, there are frequent differences between forecasting data and sales data, causing high error rates in production planning accuracy. This study aims to analyze the most effective model for forecasting future sales by comparing the accuracy of a Machine Learning-based forecasting model with the existing forecasting method currently employed at PT XYZ. This research was conducted using the Recurrent Neural network (RNN) method as part of the Machine Learning approach. The data that was inputted to the programme was weekly demand data, calendar day off data, temperature data, and population data. The forecasted data is weekly demand. Based on the company's historical data, a demand graph is obtained which has a cyclical pattern. From the results of forecasting using Machine Learning, an accuracy value of 99.47% is obtained with an error rate of 0.53%, which is still below the tolerance limit set by the company. The error rate shows a decrease of 14.72% compared to the error value in the previous company model. This decrease is expected to help control inventory more effectively.  \n Keyword: Forecasting, Machine Learning, Demand, Recurrent Neural Network   ABSTRAK  Peramalan penjualan yang akurat sangat penting bagi keuntungan perusahaankarena berpengaruh langsung terhadap manajemen persediaan barang. Machine  \nThis work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International.  \n[http://doi.org/10.26594/register.v6i1.idarticle](http://doi.org/10.26594/register.v6i1.idarticle)  \nLearning merupakan kecerdasan buatan yang dapat mengurangi resikoketidakpastian permintaan dengan cara melakukan pengenalan data secara terusmenerus dan otomatis dengan bantuan komputasi. PT. XYZ adalah perusahaan besar yang memproduksi berbagai jenis minuman di pabriknya yang berlokasi di Medan. Salah satu produk yang diproduksi adalah teh botol kaca. Pada PT. XYZ sering terjadi selisih data peramalan dengan data penjualan sehingga menyebabkan tingginya niai error pada akurasi perencanaan produksi. Penelitian ini bertujuan untuk menganalisis model terbaik untuk memprediksi penjualan kedepan dengan membandingkan nilai akurasi model forecasting Machine Learning dengan model forecasting existing pada PT. XYZ. Penelitian ini dilakukan menggunakan metode Recurrent Neural network (RNN) sebagai bagian dari pendekatan Machine Learning. Data yang dimasukan pada program adalah data permintaan mingguan, data hari libur kalender, data suhu, dan data penduduk. Data yang diprediksi adalah permintaan mingguan. Berdasarkan data historis","cbCainoUwqNoBOIm","https://ap.wps.com/l/cbCainoUwqNoBOIm","pdf",489359,1,9,"English","en",105,"# Introduction\n## Problem Background\n## Research Objective and Approach\n# Methods\n## Data Used\n## RNN Forecasting Model\n# Results\n## Forecasting Accuracy and Error Rate\n## Comparison With Existing Method\n# Conclusion\n## Implications for Inventory Control","[{\"question\":\"Why is accurate demand forecasting important for PT XYZ?\",\"answer\":\"It affects inventory management and production planning accuracy, where mismatches between forecasting and sales data increase error rates.\"},{\"question\":\"What machine learning method is used in the study?\",\"answer\":\"The study uses a Recurrent Neural Network (RNN) approach to forecast weekly demand.\"},{\"question\":\"What inputs are used to generate the weekly demand forecast?\",\"answer\":\"Weekly demand history, calendar day-off data, temperature data, and population data are used as inputs.\"}]","The Design Of A Demand Forecasting Model Of Glass Bottled Tea Products With Machine Learning Approach | PDF",1785724174,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-design-of-a-demand-forecasting-model-of-glass-bottled-tea-products-with-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-design-of-a-demand-forecasting-model-of-glass-bottled-tea-products-with-machine-learning-approach/119416/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is accurate demand forecasting important for PT XYZ?","Question",{"text":75,"@type":76},"It affects inventory management and production planning accuracy, where mismatches between forecasting and sales data increase error rates.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning method is used in the study?",{"text":80,"@type":76},"The study uses a Recurrent Neural Network (RNN) approach to forecast weekly demand.",{"name":82,"@type":73,"acceptedAnswer":83},"What inputs are used to generate the weekly demand forecast?",{"text":84,"@type":76},"Weekly demand history, calendar day-off data, temperature data, and population data are used as inputs.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]