The Implementation Of Machine Learning In Demand Forecasting : A Review Of Method Used In Demand Forecasting With Machine Learning

Authors

  • Sri Baginda Dalimunthe Teknik Industri USU
  • Rosnani Ginting Universitas Sumatera Utara
  • Sukaria Sinulingga Universitas Sumatera Utara

DOI:

https://doi.org/10.32734/jsti.v25i1.9290

Keywords:

Machine Learning, Demand Forecasting

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.

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Published

2023-01-30

How to Cite

Dalimunthe, S. B., Ginting, R. ., & Sinulingga, S. (2023). The Implementation Of Machine Learning In Demand Forecasting : A Review Of Method Used In Demand Forecasting With Machine Learning. Jurnal Sistem Teknik Industri, 25(1), 41-49. https://doi.org/10.32734/jsti.v25i1.9290