MATEC Web Conf.
Volume 200, 2018International Workshop on Transportation and Supply Chain Engineering (IWTSCE’18)
|Number of page(s)||7|
|Published online||14 September 2018|
Machine Learning for Supply Chain’s Big Data: State of the art and application to Social Networks’ data
Ibn Tofail University, Department of Mathematics, Kenitra, Morocco
2 Mohamed V University, Department of Mathematics, Rabat, Morocco
a Corresponding author: firstname.lastname@example.org
In the context of today ’s pattern of globalization and a huge amount of information, a smart supply management chain is required. Naturally, statistics and operations research are used for optimizing supply and demand objectives. However, the new context brings out new opportunities at descriptive, predictive and prescriptive levels for supply chain network design, logistics and distribution and strategic sourcing. The key question is still how to capture and to use information. One striking example can be taken from social media, where their use allow to gain insight into the perception of consumers and to capture a real time overview of consumer reactions, regarding one or more specific events. In this regard, different modern approaches, such as IoT or Quantum neural network, are developed. In the same line of thought, we propose an analytic approach, based on KNN, Logistic Regression and SVM with the use of Twitter data in chicken supply chain management. Results identify the main concerns related to chicken products and allow to the development of a consumer-centric supply chain. The proposed approach can be extended to other topics such as anomaly detection and codification of customer intelligence.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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