Russian Law Journal XI(6), pp. 369-376.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: With technological advances and continued economic growth in modern society, this is just one of many implementations that can turn the tide of the supply chain industry. Traditional models of managing the transfer of physical goods have failed to overcome inefficiencies. Machine learning measures offer another way to ensure that goods are delivered to customers faster with fewer delays and damage. clearly there is a huge gap between traditional data monitoring practices and the demands of modern enterprises. In the supply chain space, insights must be delivered instantly to ensure deliveries are made on time. Machine learning will not only help improve visibility of the supply of goods but will also actively improve the transfer of goods from suppliers to customers. The results of this literature survey on supply chains using anomaly detection are that more use of LSTM LSTM Autoencoder and OCSCM algorithms also use methods to optimize hyperparameters for hybrid algorithms to detect anomalies located in time series data.
Bibtex:
@article{,
author = {Lusi Elviani Rangkuti and Farida Khairani Lubis and Iskandar Muda},
title = {Supply Chain Management : A Review Of Anomaly Detection Techniques And The Benford's Law},
year = {2023},
journal = {Russian Law Journal},
volume = {XI},
number = {6},
pages = {369--376},
url = {https://www.russianlawjournal.org/index.php/journal/article/view/3402}
}
Reference Type: Journal Article
Subject Area(s): Accounting