Proceedings of XIV. International Conference on Logistics in Agriculture.
ISSN/ISBN: Not available at this time. DOI: 10.18690/978-961-286-406-4.4
Abstract: Food supply chains are becoming increasingly more complex, contributing to emergence of new threats and risks for the involved stakeholders. Additionally, the information technology accelerated development of new and more productive ways of collaboration among organizations (members of supply chains) and helped to optimize their processes. Tighter collaboration among those companies is only possible if sufficient level of trust is established among them, which is often an obstacle that is not easily overcome. Since individual companies (which are part of supply chain) are unable to verify and rely on the data that is provided by third parties, the potential advantages are not fully realized. In this article we try to identify a possibility to remove one important element of this obstacle by using Benford’s law as the basis for general-purpose verification tool that is additionally enhanced by statistics based methods of machine learning algorithms that can be implemented in IT supported business operations. The potential usefullness of those methods lies in the fact that they are able to identify the patterns and correlations without explicit users’ input.
Bibtex:
@inproceedings{,
author = {Levičar, Stanislav},
year = {2020},
month = {11},
pages = {39-50},
title = {Potential of Benford's Law and Machine Learning Based Verification in Agricultural Logistics},
booktitle = {Proceedings of XIV. International Conference on Logistics in Agriculture},
doi = {10.18690/978-961-286-406-4.4},
}
Reference Type: Conference Paper
Subject Area(s): Accounting, Computer Science