Standard

Big Data Analysis in Social Networks for Managing Risks in Clothing Industry. / Blekanov, Ivan; Krylatov, Alexander; Ivanov, Dmitri; Bubnova, Yulia.

In: IFAC-PapersOnLine, Vol. 52, No. 13, 2019, p. 1710-1714.

Research output: Contribution to journal › Article › peer-review

Harvard

Blekanov, I, Krylatov, A, Ivanov, D & Bubnova, Y 2019, 'Big Data Analysis in Social Networks for Managing Risks in Clothing Industry', IFAC-PapersOnLine, vol. 52, no. 13, pp. 1710-1714. https://doi.org/10.1016/j.ifacol.2019.11.447

APA

Blekanov, I., Krylatov, A., Ivanov, D., & Bubnova, Y. (2019). Big Data Analysis in Social Networks for Managing Risks in Clothing Industry. IFAC-PapersOnLine, 52(13), 1710-1714. https://doi.org/10.1016/j.ifacol.2019.11.447

Vancouver

Blekanov I, Krylatov A, Ivanov D, Bubnova Y. Big Data Analysis in Social Networks for Managing Risks in Clothing Industry. IFAC-PapersOnLine. 2019;52(13):1710-1714. https://doi.org/10.1016/j.ifacol.2019.11.447

Author

Blekanov, Ivan ; Krylatov, Alexander ; Ivanov, Dmitri ; Bubnova, Yulia. / Big Data Analysis in Social Networks for Managing Risks in Clothing Industry. In: IFAC-PapersOnLine. 2019 ; Vol. 52, No. 13. pp. 1710-1714.

BibTeX

@article{571bf7445c5745e1b6dd8439a3951355,
title = "Big Data Analysis in Social Networks for Managing Risks in Clothing Industry",
abstract = "This paper demonstrates the power of big data analysis of social media for the identification and prioritization of supply chain risks in clothing industry. Due to their high volume, data from social media can be considered a good source for obtaining customer feedback. On the other hand, the high volume of social media data could be efficiently processed solely by special tools for big data analysis. Results of such an analysis include clusters of words, topics and sentiments which allow decision makers to gather negative customer feedback and to manage risks in the distribution of products. A case study in the segment of footwear supply chain was analyzed, where one month of data from Twitter was used. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.",
keywords = "Risk, Supply chain, Big Data, Twitter, Social network, ANALYTICS, RECOVERY",
author = "Ivan Blekanov and Alexander Krylatov and Dmitri Ivanov and Yulia Bubnova",
year = "2019",
doi = "10.1016/j.ifacol.2019.11.447",
language = "Английский",
volume = "52",
pages = "1710--1714",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "Elsevier",
number = "13",
note = "null ; Conference date: 28-08-2019 Through 30-08-2019",

}

RIS

TY - JOUR

T1 - Big Data Analysis in Social Networks for Managing Risks in Clothing Industry

AU - Blekanov, Ivan

AU - Krylatov, Alexander

AU - Ivanov, Dmitri

AU - Bubnova, Yulia

PY - 2019

Y1 - 2019

N2 - This paper demonstrates the power of big data analysis of social media for the identification and prioritization of supply chain risks in clothing industry. Due to their high volume, data from social media can be considered a good source for obtaining customer feedback. On the other hand, the high volume of social media data could be efficiently processed solely by special tools for big data analysis. Results of such an analysis include clusters of words, topics and sentiments which allow decision makers to gather negative customer feedback and to manage risks in the distribution of products. A case study in the segment of footwear supply chain was analyzed, where one month of data from Twitter was used. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

AB - This paper demonstrates the power of big data analysis of social media for the identification and prioritization of supply chain risks in clothing industry. Due to their high volume, data from social media can be considered a good source for obtaining customer feedback. On the other hand, the high volume of social media data could be efficiently processed solely by special tools for big data analysis. Results of such an analysis include clusters of words, topics and sentiments which allow decision makers to gather negative customer feedback and to manage risks in the distribution of products. A case study in the segment of footwear supply chain was analyzed, where one month of data from Twitter was used. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

KW - Risk

KW - Supply chain

KW - Big Data

KW - Twitter

KW - Social network

KW - ANALYTICS

KW - RECOVERY

U2 - 10.1016/j.ifacol.2019.11.447

DO - 10.1016/j.ifacol.2019.11.447

M3 - статья

VL - 52

SP - 1710

EP - 1714

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 13

Y2 - 28 August 2019 through 30 August 2019

ER -

ID: 50945697