IDEAS home Printed from https://ideas.repec.org/a/ers/journl/vxxviy2023i4p865-888.html
   My bibliography  Save this article

Strategic Insights: Navigating Business Intelligence Implementation - Phases, Tasks, and Risks: A Case Study on an International Manufacturing Company

Author

Listed:
  • Klaudia Hillebrandt-Szymanska
  • Dorota Piotrowska
  • Artur Blaszczyk
  • Jakub Statucki

Abstract

Purpose: This article aims to present a comprehensive case study of implementing a business intelligence system in the manufacturing company. Therefore, a comprehensive understanding of key implementation aspects and associated risks is vital for meticulous planning before investing in information systems. Design/Methodology/Approach: Through qualitative research, the study will identify the main implementation phases, assign key tasks, and highlight the potential risks encountered during the process. By examining the case study, readers can gain insights into the effective implementation of a business intelligence system in manufacturing company, enabling them to better navigate similar ventures a significant input for researchers to create an implementation model. Findings: Having accurate and timely information is a crucial asset for businesses, influencing their competitive advantage. Information is essential for decision-making, enabling organizations to identify opportunities, threats, strengths, weaknesses, and changes. Business Intelligence (BI) solutions cater to these needs by automatically transforming data into actionable information. However, due to the wide array of tools available in the market, the implementation process of BI can be complex. Practical Implications: The findings from this article's case study can serve as a foundation for proposing an implementation model for business intelligence systems. Originality/Value: By analyzing the challenges, key phases, and risks identified in the case study, future research can develop a structured framework or model that outlines the necessary steps, considerations, and best practices for implementing BI systems specifically tailored to manufacturing industries.

Suggested Citation

  • Klaudia Hillebrandt-Szymanska & Dorota Piotrowska & Artur Blaszczyk & Jakub Statucki, 2023. "Strategic Insights: Navigating Business Intelligence Implementation - Phases, Tasks, and Risks: A Case Study on an International Manufacturing Company," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 865-888.
  • Handle: RePEc:ers:journl:v:xxvi:y:2023:i:4:p:865-888
    as

    Download full text from publisher

    File URL: https://ersj.eu/journal/3333/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yili Chen & Congdong Li & Han Wang, 2022. "Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)," Forecasting, MDPI, vol. 4(4), pages 1-20, September.
    2. Kanika Chaudhry & Sanjay Dhingra, 2021. "Modeling the Critical Success Factors for Business Intelligence Implementation: An ISM Approach," International Journal of Business Intelligence Research (IJBIR), IGI Global, vol. 12(2), pages 1-21, July.
    3. Adriana Grigorescu & Daniela Baiasu & Razvan Ion Chitescu, 2020. "Business Intelligence, the New Managerial Tool: Opportunities and Limits," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 651-657, August.
    4. Carlos Andrés Tavera Romero & Jesús Hamilton Ortiz & Osamah Ibrahim Khalaf & Andrea Ríos Prado, 2021. "Business Intelligence: Business Evolution after Industry 4.0," Sustainability, MDPI, vol. 13(18), pages 1-13, September.
    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. Sergi, Bruno S. & Ključnikov, Aleksandr & Popkova, Elena G. & Bogoviz, Aleksei V. & Lobova, Svetlana V., 2022. "Creative abilities and digital competencies to transitioning to Business 4.0," Journal of Business Research, Elsevier, vol. 153(C), pages 401-411.
    2. Stavros Kalogiannidis & Dimitrios Kalfas & Efstratios Loizou & Olympia Papaevangelou & Fotios Chatzitheodoridis, 2023. "Smart Sustainable Marketing and Emerging Technologies: Evidence from the Greek Business Market," Sustainability, MDPI, vol. 16(1), pages 1-21, December.
    3. repec:prg:jnlcfu:v:2021:y:2021:i:4:id:569 is not listed on IDEAS
    4. Rafał Trzaska & Adam Sulich & Michał Organa & Jerzy Niemczyk & Bartosz Jasiński, 2021. "Digitalization Business Strategies in Energy Sector: Solving Problems with Uncertainty under Industry 4.0 Conditions," Energies, MDPI, vol. 14(23), pages 1-21, November.
    5. Fatih Gurcan & Ahmet Ayaz & Gonca Gokce Menekse Dalveren & Mohammad Derawi, 2023. "Business Intelligence Strategies, Best Practices, and Latest Trends: Analysis of Scientometric Data from 2003 to 2023 Using Machine Learning," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    6. Hussien Mohamad Alakrash & Norizan Abdul Razak, 2021. "Technology-Based Language Learning: Investigation of Digital Technology and Digital Literacy," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    7. Aneta Zemánková, 2021. "Artificial intelligence in management accounting [Využití umělé inteligence v manažerském účetnictví]," Český finanční a účetní časopis, Prague University of Economics and Business, vol. 2021(4), pages 81-99.

    More about this item

    Keywords

    Business Intelligence software; implementation procedure; decision support systems; manufacturing company management; data-oriented systems.;
    All these keywords.

    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

    Statistics

    Access and download statistics

    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:ers:journl:v:xxvi:y:2023:i:4:p:865-888. 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: Marios Agiomavritis (email available below). General contact details of provider: https://ersj.eu/ .

    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.