IDEAS home Printed from https://ideas.repec.org/a/nap/nijbmr/2019p125-134.html
   My bibliography  Save this article

Industry 4.0 Technologies And Operational Performance Of Unilever Kenya And L’Oreal East Africa

Author

Listed:
  • Juma Nasambu Anitah

    (Department of Management Science, University of Nairobi, Nairobi, Kenya)

  • Stephen Onserio Nyamwange

    (Department of Management Science, University of Nairobi, Nairobi, Kenya)

  • Peterson Obara Magutu

    (Department of Management Science, University of Nairobi, Nairobi, Kenya)

  • Michael Chirchir

    (Department of Management Science, University of Nairobi, Nairobi, Kenya)

  • James Mauti Mose

    (Department of Management Science, University of Nairobi, Nairobi, Kenya)

Abstract

Purpose: The purpose of the study was to investigate the impact of Industry 4.0 technologies and applications on FMCGs manufacturers in Kenya, with specific reference to L’Oréal East Africa and Unilever. Design/Research method: The two companies were selected for the study because they are among the largest FMCGs manufacturers in Kenya, thus the size of their operations is almost equal. Therefore, the study was conducted through a case-study design. Data was collected using an interview guide and the information interpreted through descriptive statistics. Finding: The study established that Industry 4.0 technologies (autonomous robots, big data and analytics, augmented reality, cloud computing, and operations) helps in enhancing operational performance in FMCGs specifically in predicting demand, understanding consumer behavioural patterns, minimizing errors and enhancing flexibility for effective decision making. Limitation: There was the lack of previous studies on Industry 4.0 adoption in Kenya to provide a foundation for the research this being a new area of technology adoption in organization since the study was limited to people working in Unilever and L’Oréal East Africa, thus the findings cannot be generalized to all the other manufacturing companies in the country. Implication: The companies can improve the integration of the technologies in their operations by developing an updated industry 4.0 implementation plan and communicate the information to employees to improve their readiness for the change.

Suggested Citation

  • Juma Nasambu Anitah & Stephen Onserio Nyamwange & Peterson Obara Magutu & Michael Chirchir & James Mauti Mose, 2019. "Industry 4.0 Technologies And Operational Performance Of Unilever Kenya And L’Oreal East Africa," Noble International Journal of Business and Management Research, Noble Academic Publsiher, vol. 3(10), pages 125-134, October.
    • Handle: RePEc:nap:nijbmr:2019:p:125-134
    as

    Download full text from publisher

    File URL: https://www.napublisher.org/pdf-files/NIJBMR-3(10)-125-134.pdf
    Download Restriction: no
    File URL: https://www.napublisher.org/?ic=journal&journal=8&month=10-2019&issue=10&volume=3
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Yichuan & Hajli, Nick, 2017. "Exploring the path to big data analytics success in healthcare," Journal of Business Research, Elsevier, vol. 70(C), pages 287-299.
    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. Adilson Carlos Yoshikuni & José Eduardo Ricciardi Favaretto & Alberto Luiz Albertin & Fernando de Souza Meirelles, 2022. "How can Strategy-as-Practice Enable Innovation under the Influence of Environmental Dynamism?," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 26(1), pages 200131-2001.
    2. 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.
    3. Alaassar, Ahmad & Mention, Anne-Laure & Aas, Tor Helge, 2021. "Exploring a new incubation model for FinTechs: Regulatory sandboxes," Technovation, Elsevier, vol. 103(C).
    4. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    5. Yu, Wantao & Zhao, Gen & Liu, Qi & Song, Yongtao, 2021. "Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: An organizational information processing theory perspective," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    6. Sudhanshu Joshi & Manu Sharma & Rashmi Prava Das & Joanna Rosak-Szyrocka & Justyna Żywiołek & Kamalakanta Muduli & Mukesh Prasad, 2022. "Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    7. Claus Zippel & Sabine Bohnet-Joschko, 2021. "Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    8. Ayesha Amjad & Piotr Kordel & Gabriela Fernandes, 2023. "A Review on Innovation in Healthcare Sector (Telehealth) through Artificial Intelligence," Sustainability, MDPI, vol. 15(8), pages 1-24, April.
    9. Thompson, Alex & Stringfellow, Lindsay & Maclean, Mairi & Nazzal, Amal, 2021. "Ethical considerations and challenges for using digital ethnography to research vulnerable populations," Journal of Business Research, Elsevier, vol. 124(C), pages 676-683.
    10. Hani Bani-Salameh & Mona Al-Qawaqneh & Salah Taamneh, 2021. "Investigating the Adoption of Big Data Management in Healthcare in Jordan," Data, MDPI, vol. 6(2), pages 1-16, February.
    11. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    12. Asamoah, Daniel Adomako & Sharda, Ramesh, 2021. "What should I believe? Exploring information validity on social network platforms," Journal of Business Research, Elsevier, vol. 122(C), pages 567-581.
    13. Maryia Zaitsava & Elona Marku & Maria Chiara Guardo & Azar Shahgholian, 2023. "A fine-grained perspective on big data knowledge creation: dimensions, insights, and mechanism from a pilot study," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 547-573, June.
    14. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    15. Li, Huanli & Wu, Yun & Cao, Dongmei & Wang, Yichuan, 2021. "Organizational mindfulness towards digital transformation as a prerequisite of information processing capability to achieve market agility," Journal of Business Research, Elsevier, vol. 122(C), pages 700-712.
    16. Ker, Jun-Ing & Wang, Yichuan & Hajli, Nick, 2018. "Examining the impact of health information systems on healthcare service improvement: The case of reducing in patient-flow delays in a U.S. hospital," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 188-198.
    17. Amit Kumar Gupta & Harshit Goyal, 2021. "Framework for implementing big data analytics in Indian manufacturing: ISM-MICMAC and Fuzzy-AHP approach," Information Technology and Management, Springer, vol. 22(3), pages 207-229, September.
    18. El-Haddadeh, Ramzi & Osmani, Mohamad & Hindi, Nitham & Fadlalla, Adam, 2021. "Value creation for realising the sustainable development goals: Fostering organisational adoption of big data analytics," Journal of Business Research, Elsevier, vol. 131(C), pages 402-410.
    19. Cristina Mele & Tiziana Russo Spena & Marco Tregua & Mariarosaria Coppola & Marialuisa Marzullo, 2018. "What about the Internet of Everything? An exploratory study in E-health," MERCATI & COMPETITIVIT?, FrancoAngeli Editore, vol. 2018(3), pages 87-109.
    20. Huynh, Minh-Tay & Nippa, Michael & Aichner, Thomas, 2023. "Big data analytics capabilities: Patchwork or progress? A systematic review of the status quo and implications for future research," Technological Forecasting and Social Change, Elsevier, vol. 197(C).

    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:nap:nijbmr:2019:p:125-134. 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: Managing Editor (email available below). General contact details of provider: https://www.napublisher.org/?ic=journal&journal=8&info=aims .

    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.