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All Journal Jurnal Sistem Teknik Industri
Sri Baginda Dalimunthe
Teknik Industri USU
Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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The Implementation Of Machine Learning In Demand Forecasting : A Review Of Method Used In Demand Forecasting With Machine Learning Sri Baginda Dalimunthe; Rosnani Ginting; Sukaria Sinulingga
Jurnal Sistem Teknik Industri Vol. 25 No. 1 (2023): JSTI Volume 25 Number 1 January 2023
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v25i1.9290

Abstract

Demand Forecasting is essentials in making production decisions. Demand forecasting accuracy affects supply chain management and can reduce its costs. The development of information technology, especially artificial intelligence, has many benefits in many industrial sectors. The development of artificial intelligence is also applied to demand forecasting. The development of Artificial Intelligence technology in forecasting can produce better accuracy than conventional methods that do not use Artificial Intelligence. The use of machine learning in demand forecasting is in various industrial sectors ranging from small-scale industry to large-scale industry. This article will discuss research on the use of machine learning in demand forecasting for the things discussed, including machine learning models, data processing methods, and research variables. The purpose of this review is to see a comparison of the accuracy of each model, method, and variable used in demand forecasting using machine learning. The results of the review show that the characteristics of different product fluctuations require a different demand forecasting model approach. An appropriate approach can produce higher forecasting accuracy.  Mistake in choosing a demand forecasting model can reduce the accuracy of demand forecasting. The demand forecasting model must also need to be updated to improve accuracy.
Co-Authors Rosnani Ginting Sukaria Sinulingga