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Analysis Study Research Paper | Science and Technology | United States of America | Volume 13 Issue 10, October 2024 | Rating: 6.1 / 10
Artificial Intelligence Methods for Demand Forecasting
Manikandan Natarajan
Abstract: 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.
Keywords: Demand Forecasting; Supply Chain Management; Artificial Intelligence; Machine Learning; Deep Learning; Review; Analysis
Edition: Volume 13 Issue 10, October 2024,
Pages: 442 - 446