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Review and analysis of artificial intelligence methods for demand forecasting in supply chain management

  • The proper selection of a demand forecasting method is directly linked to the success of supply chain management (SCM). However, today’s manufacturing companies are confronted with uncertain and dynamic markets. Consequently, classical statistical methods are not always appropriate for accurate and reliable forecasting. Algorithms of Artificial intelligence (AI) are currently used to improve statistical methods. Existing literature only gives a very general overview of the AI methods used in combination with demand forecasting. This paper provides an analysis of the AI methods published in the last five years (2017-2021). Furthermore, a classification is presented by clustering the AI methods in order to define the trend of the methods applied. Finally, a classification of the different AI methods according to the dimensionality of data, volume of data, and time horizon of the forecast is presented. The goal is to support the selection of the appropriate AI method to optimize demand forecasting.

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Metadaten
Author of HS ReutlingenMediavilla, Mario; Dietrich, Fabian; Palm, Daniel
URN:urn:nbn:de:bsz:rt2-opus4-36807
DOI:https://doi.org/10.1016/j.procir.2022.05.119
ISBN:2212-8271
Erschienen in:Procedia CIRP
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Tag:Artificial Intelligence; analysis; deep learning; demand forecasting; machine learning; review; supply chain management
Volume:107
Issue:Leading manufacturing systems transformation – Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022
Page Number:6
First Page:1126
Last Page:1131
DDC classes:600 Technik
Open Access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International