Analysis of Scientific Production on the Use of Big Data Analytics in Performance Measurement Systems

被引:0
作者
Assandre, Junior Aparecido [1 ]
Martins, Roberto A. [1 ]
机构
[1] Univ Fed Sao Carlos UFSCar, Dept Engn Prod, Sao Carlos, SP, Brazil
关键词
Big Data; Software; Production; Measurement; Internet of Things; IEEE transactions; Fourth Industrial Revolution; Bibliometric Analysis; Performance Measurement Systems; Performance Measures; Industry; 4; 0; Big Data Analytics; SUPPLY CHAIN MANAGEMENT; PREDICTIVE ANALYTICS; BIBLIOMETRIC ANALYSIS; INDUSTRY; 4.0; DATA SCIENCE; BUSINESS; DESIGN; IMPACT; EVOLUTION; INFORMATION;
D O I
10.1109/TLA.2023.10068840
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Performance measurement systems have a critical role in organizations management, transforming data into relevant information for decision makers. In recent decades, the amount of data and information generated and shared has increased immensely, providing unprecedented opportunities and challenges for such systems. Faced with this scenario, this article aims to analyze the use of big data analytics in performance measurement systems to clarify the nexus between them. Furthermore, the aim is also to identify the trends and opportunities for future research. To achieve that, we carried a scientific map out using bibliometric analysis. The major results of the research show that the use of big data analytics in PMS has increased in recent years without considering the performance measurement systems characteristics. Incorporating artificial intelligence technologies such as machine learning and deep learning could improve the domain, creating opportunities for empirical works such as the use of unstructured data and applications in Industry 4.0.
引用
收藏
页码:367 / 380
页数:14
相关论文
共 124 条
  • [1] A decision theory perspective on complexity in performance measurement and management
    Alexander, Anthony
    Kumar, Maneesh
    Walker, Helen
    [J]. INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT, 2018, 38 (11) : 2214 - 2244
  • [2] A review of data-driven building energy consumption prediction studies
    Amasyali, Kadir
    El-Gohary, Nora M.
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 81 : 1192 - 1205
  • [3] Towards a generic benchmarking platform for origin-destination flows estimation/updating algorithms: Design, demonstration and validation
    Antoniou, Constantinos
    Barcelo, Jaume
    Breen, Martijn
    Bullejos, Manuel
    Casas, Jordi
    Cipriani, Ernesto
    Ciuffo, Biagio
    Djukic, Tamara
    Hoogendoorn, Serge
    Marzano, Vittorio
    Montero, Lidia
    Nigro, Marialisa
    Perarnau, Josep
    Punzo, Vincenzo
    Toledo, Tomer
    van Lint, Hans
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2016, 66 : 79 - 98
  • [4] Impact of business analytics and enterprise systems on managerial accounting
    Appelbaum, Deniz
    Kogan, Alexander
    Vasarhelyi, Miklos
    Yan, Zhaokai
    [J]. INTERNATIONAL JOURNAL OF ACCOUNTING INFORMATION SYSTEMS, 2017, 25 : 29 - 44
  • [5] A bibliometric analysis of research on Big Data analytics for business and management
    Ardito, Lorenzo
    Scuotto, Veronica
    Del Giudice, Manlio
    Petruzzelli, Antonio Messeni
    [J]. MANAGEMENT DECISION, 2019, 57 (08) : 1993 - 2009
  • [6] Aria M., 2019, BIBLIOSHINY BIBLIOME
  • [7] Aria M., 2020, Package Bibliometrix. the Comprehensive R Archive Network
  • [8] bibliometrix: An R-tool for comprehensive science mapping analysis
    Aria, Massimo
    Cuccurullo, Corrado
    [J]. JOURNAL OF INFORMETRICS, 2017, 11 (04) : 959 - 975
  • [9] Measuring supply chain performance
    Beamon, BM
    [J]. INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT, 1999, 19 (3-4) : 275 - 292
  • [10] Humanitarian supply chain management: a thematic literature review and future directions of research
    Behl, Abhishek
    Dutta, Pankaj
    [J]. ANNALS OF OPERATIONS RESEARCH, 2019, 283 (1-2) : 1001 - 1044