TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Angos Mediavilla, Mario
A1  - Dietrich, Fabian
A1  - Palm, Daniel
T1  - Review and analysis of artificial intelligence methods for demand forecasting in supply chain management
JF  - Procedia CIRP
N2  - 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.
KW  - demand forecasting
KW  - supply chain management
KW  - Artificial Intelligence
KW  - machine learning
KW  - deep learning
KW  - review
KW  - analysis
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-36807
SN  - 2212-8271
SB  - 2212-8271
U6  - https://doi.org/10.1016/j.procir.2022.05.119
DO  - https://doi.org/10.1016/j.procir.2022.05.119
VL  - 107
IS  - Leading manufacturing systems transformation – Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022
SP  - 1126
EP  - 1131
S1  - 6
PB  - Elsevier
CY  - Amsterdam
ER  -