International Journal of Industrial Engineering & Production Research
Iran University of Science & Technology
Thu, Dec 26, 2024
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Volume 35, Issue 2 (IJIEPR 2024)
IJIEPR 2024, 35(2): 1-20 |
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ATWANI M, HLYAL M, ALAMI J E. AI-based demand forecasting models: A systematic literature review. IJIEPR 2024; 35 (2) :1-20
URL: http://ijiepr.iust.ac.ir/article-1-1974-en.html
URL: http://ijiepr.iust.ac.ir/article-1-1974-en.html
1- LASTIMI Laboratory, Higher School of Technology in Sale, Mohammed V University, Rabat, Morocco. CELOG, Higher School of Textile and Clothing Industries, ESITH, Casablanca, Morocco , mariam_atwani@um5.ac.ma
2- Logistics Center of Excellence (CELOG), Higher School of Textile and Clothing Industries, ESITH, Casablanca, Morocco
3- LASTIMI Laboratory, Higher School of Technology in Sale, Mohammed V University, Rabat, Morocco
2- Logistics Center of Excellence (CELOG), Higher School of Textile and Clothing Industries, ESITH, Casablanca, Morocco
3- LASTIMI Laboratory, Higher School of Technology in Sale, Mohammed V University, Rabat, Morocco
Abstract: (1151 Views)
In today's dynamic and competitive manufacturing landscape, accurate demand forecasting is paramount for optimizing production processes, reducing inventory costs, and meeting customer demands efficiently. With the advent of Artificial Intelligence (AI), there has been a significant evolution in demand forecasting methods, enabling manufacturers to enhance the accuracy of the forecasts.
This systematic literature review aims to provide a comprehensive overview of the state-of-the-art on demand forecasting models in the manufacturing sector, whether AI-based models or hybrid methods merging both the AI technology and classical demand forecasting methods. The review begins by establishing an overview on demand forecasting methods, it then outlines the systematic methodology used for the literature search.
The review encompasses a wide range of scholarly articles published up to September 2023. A rigorous screening process is applied to select relevant studies. Accordingly, a thorough analysis in the basis of the forecasting methods adopted and data used have been carried out. By synthesizing the existing knowledge, this review contributes to the ongoing advancement of demand forecasting practices in the manufacturing sector providing researchers and practitioners an overview on the advancements on the use of AI models to improve the accuracy of demand forecasting models.
This systematic literature review aims to provide a comprehensive overview of the state-of-the-art on demand forecasting models in the manufacturing sector, whether AI-based models or hybrid methods merging both the AI technology and classical demand forecasting methods. The review begins by establishing an overview on demand forecasting methods, it then outlines the systematic methodology used for the literature search.
The review encompasses a wide range of scholarly articles published up to September 2023. A rigorous screening process is applied to select relevant studies. Accordingly, a thorough analysis in the basis of the forecasting methods adopted and data used have been carried out. By synthesizing the existing knowledge, this review contributes to the ongoing advancement of demand forecasting practices in the manufacturing sector providing researchers and practitioners an overview on the advancements on the use of AI models to improve the accuracy of demand forecasting models.
Keywords: Demand forecasting, Forecasting models, Artificial intelligence, Supply chain, Systematic Literature review
Type of Study: Research |
Subject:
Logistic & Apply Chain
Received: 2024/02/6 | Accepted: 2024/04/28 | Published: 2024/06/21
Received: 2024/02/6 | Accepted: 2024/04/28 | Published: 2024/06/21
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