Development of Big Data Analytics in a Multi-site Enterprise on the Example of Supply Chain Management

被引:0
|
作者
Pyda, Pawel [1 ]
Stefaniak, Pawel [2 ]
Dudycz, Helena [3 ]
Jachnik, Bartosz [2 ]
机构
[1] KGHM Polish Copper SA, KGHM Polska Miedz SA, COPI, Lublin, Poland
[2] KGHM CUPRUM Res & Dev Ctr Ltd, Gen W Sikorskiego St 2-8, PL-53659 Wroclaw, Poland
[3] Wroclaw Univ Econ & Business, Dept Informat Technol, Wroclaw, Poland
关键词
Supply chain management; Big data; KPI; Industrial enterprise; PREDICTIVE ANALYTICS; INTEGRATION; IMPLEMENTATION; LOGISTICS; RESOURCES; MODEL;
D O I
10.1007/978-3-030-80847-1_12
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Currently, advanced data analytics in large multi-site industrial enterprises is a strategic element in making management decisions. Integrated supply chain management (SCM), machinery park management, or data analysis from industrial devices (including using Industrial Internet of Things - IIoT) requires the organization of appropriate analytical platform architecture, the selection of the analytical tools for Big Data, the implementation of advanced algorithms based on machine learning and the development of management dashboards for ongoing tracking the KPI's of assets. This article presents the issues related to the acquisition, analysis, and management of large amounts of data from various enterprise departments. These data come from multiple systems, and they are indifferent data recording standards. They are essential because they form the basis of advanced data analysis in supply chain management in multi-site enterprises. This article discusses the proposal of an analytical platform for SCM and the development of analytical processing for SCM in the multi-site industrial enterprise.
引用
收藏
页码:177 / 192
页数:16
相关论文
共 50 条
  • [1] Big Data Analytics for Supply Chain Management
    Leveling, Jens
    Edelbrock, Matthias
    Otto, Boris
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM), 2014, : 918 - 922
  • [2] Big data analytics in operations and supply chain management
    Samuel Fosso Wamba
    Angappa Gunasekaran
    Rameshwar Dubey
    Eric W. T. Ngai
    [J]. Annals of Operations Research, 2018, 270 : 1 - 4
  • [3] Big data analytics in operations and supply chain management
    Wamba, Samuel Fosso
    Gunasekaran, Angappa
    Dubey, Rameshwar
    Ngai, Eric W. T.
    [J]. ANNALS OF OPERATIONS RESEARCH, 2018, 270 (1-2) : 1 - 4
  • [4] Exploring Big Data Analytics for Supply Chain Management
    Cheng, Otto K. M.
    Lau, Raymond Y. K.
    [J]. 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT, ECONOMICS AND SOCIAL DEVELOPMENT (ICMESD 2016), 2016, : 1111 - 1117
  • [5] Big data analytics in logistics and supply chain management
    Wamba, Samuel Fosso
    Gunasekaran, Angappa
    Papadopoulos, Thanos
    Ngai, Eric
    [J]. INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT, 2018, 29 (02) : 478 - 484
  • [6] The impact of big data and business analytics on supply chain management
    Ittmann, Hans W.
    [J]. JOURNAL OF TRANSPORT AND SUPPLY CHAIN MANAGEMENT, 2015, 9 (01)
  • [7] Big Data Analytics in Supply Chain Management: A Qualitative Study
    Aljabhan, Basim
    Abeyie, Melese
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [8] Big Data Analytics on The Supply Chain Management: A Significant Impact
    Handanga, Suilety
    Bernardino, Jorge
    Pedrosa, Isabel
    [J]. PROCEEDINGS OF 2021 16TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI'2021), 2021,
  • [9] An Analytical Study on Big Data Management for Supply Chain Analytics
    Kumar, Sundeep
    Rathore, Vikram Singh
    Mathur, Alok
    [J]. RECENT ADVANCES IN INDUSTRIAL PRODUCTION, ICEM 2020, 2022, : 333 - 341
  • [10] Big data and predictive analytics applications in supply chain management
    Gunasekaran, Angappa
    Tiwari, Manoj Kumar
    Dubey, Rameshwar
    Wamba, Samuel Fosso
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2016, 101 : 525 - 527