IDEAS home Printed from https://ideas.repec.org/a/osi/bulimm/v23y2023p451-467.html
   My bibliography  Save this article

The Role Of Iot Data Aggregators For Optimising Object Tracking And Kpi Monitoring

Author

Listed:
  • Robert Idlbek

    (Faculty of Tourism and Rural Development, Croatia)

  • Verica Budimir

    (Faculty of Tourism and Rural Development, Croatia)

Abstract

The Internet of Things (IoT) is an innovative technology that has completely transformed how different devices communicate. This includes sensors, actuators, GPS trackers, and other intelligent equipment. Among its many applications, one of the most important is its role in object tracking and monitoring Key Performance indicators (KPI). These functions are particularly crucial for logistics, manufacturing, agriculture, and retail industries. The main objective of this paper is to explore the significance of IoT data aggregators in optimising these business processes. IoT data aggregators have a vital role to play as they gather, process, and analyse data from multiple IoT devices. This comprehensive approach allows a thorough understanding of the monitored objects and their performance. Moreover, the paper investigates how software designed for data aggregation can enhance the accuracy and efficiency of object tracking. This improvement facilitates real-time tracking of objects indoors and outdoors, analysis of past movements and events, and even predictive maintenance. Additionally, the paper examines how data aggregators contribute to improved KPI monitoring by providing real-time performance metrics. These metrics enable proactive decision-making and enhance operational efficiency. However, addressing some technical challenges associated with object monitoring and data aggregation is essential, such as interoperability and vendor-free technology.

Suggested Citation

  • Robert Idlbek & Verica Budimir, 2023. "The Role Of Iot Data Aggregators For Optimising Object Tracking And Kpi Monitoring," Business Logistics in Modern Management, Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Croatia, vol. 23, pages 451-467.
    • Handle: RePEc:osi:bulimm:v:23:y:2023:p:451-467
    as

    Download full text from publisher

    File URL: http://www.efos.unios.hr/repec/osi/bulimm/PDF/BusinessLogisticsinModernManagement23/blimm2325.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fiorenzo Franceschini & Maurizio Galetto & Domenico Maisano, 2019. "Designing a Performance Measurement Systemperformance measurement system," Management for Professionals, in: Designing Performance Measurement Systems, chapter 5, pages 133-205, Springer.
    2. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    3. Fiorenzo Franceschini & Maurizio Galetto & Domenico Maisano, 2019. "Designing Performance Measurement Systems," Management for Professionals, Springer, number 978-3-030-01192-5, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.
    2. Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sánchez-Alonso, Salvador, 2023. "Twitter as a predictive system: A systematic literature review," Journal of Business Research, Elsevier, vol. 157(C).
    3. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    4. Lutfi, Abdalwali & Alrawad, Mahmaod & Alsyouf, Adi & Almaiah, Mohammed Amin & Al-Khasawneh, Ahmad & Al-Khasawneh, Akif Lutfi & Alshira'h, Ahmad Farhan & Alshirah, Malek Hamed & Saad, Mohamed & Ibrahim, 2023. "Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    5. Mohamed Gaber & Edward J. Lusk, 2019. "A Vetting Protocol for the Analytical Procedures Platform for the AP-Phase of PCAOB Audits," Accounting and Finance Research, Sciedu Press, vol. 8(4), pages 1-43, November.
    6. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    7. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    8. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    9. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    10. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    11. Judita Peterlin & Maja Meško & Vlado Dimovski & Vasja Roblek, 2021. "Automated content analysis: The review of the big data systemic discourse in tourism and hospitality," Systems Research and Behavioral Science, Wiley Blackwell, vol. 38(3), pages 377-385, May.
    12. Tiago Carneiro & Winnie Ng Picoto & Inês Pinto, 2023. "Big Data Analytics and Firm Performance in the Hotel Sector," Tourism and Hospitality, MDPI, vol. 4(2), pages 1-13, April.
    13. Gianfranco Marotta & Phillipe Krahnhof & Cam-Duc Au, 2022. "A Critical Analysis of Budgeting Processes from the Pharmaceutical Industry and Beyond," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(3), pages 1-3.
    14. Osama Musa Ali Al-Darras & Cem Tanova, 2022. "From Big Data Analytics to Organizational Agility: What Is the Mechanism?," SAGE Open, , vol. 12(2), pages 21582440221, June.
    15. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    16. Andrea Cappelli & Iacopo Cavallini, 2021. "The Potential of Big Data Analysis in the Shipbuilding Industry: A Way of Increasing Competitiveness," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(suppl. 1), pages 53-74.
    17. Francesco Cappa & Stefano Franco & Federica Rosso, 2022. "Citizens and cities: Leveraging citizen science and big data for sustainable urban development," Business Strategy and the Environment, Wiley Blackwell, vol. 31(2), pages 648-667, February.
    18. Tursunbayeva, Aizhan & Di Lauro, Stefano & Pagliari, Claudia, 2018. "People analytics—A scoping review of conceptual boundaries and value propositions," International Journal of Information Management, Elsevier, vol. 43(C), pages 224-247.
    19. Karolis Matikonis & Matthew Gobey, 2024. "Small Business Property Tax Reductions and Firm Productivity," Small Business Economics, Springer, vol. 62(1), pages 307-324, January.
    20. Chae, Bongsug (Kevin), 2019. "A General framework for studying the evolution of the digital innovation ecosystem: The case of big data," International Journal of Information Management, Elsevier, vol. 45(C), pages 83-94.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osi:bulimm:v:23:y:2023:p:451-467. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Davor Dujak,PhD (email available below). General contact details of provider: https://edirc.repec.org/data/efosihr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.