IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v8y2024i2p43-d1374778.html
   My bibliography  Save this article

Impact of the Product Master Data Quality on the Logistics Process Performance

Author

Listed:
  • Diana Božić

    (Faculty of Transport and Traffic Science, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia)

  • Margareta Živičnjak

    (Faculty of Transport and Traffic Science, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia)

  • Ratko Stanković

    (Faculty of Transport and Traffic Science, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia)

  • Andrej Ignjatić

    (Faculty of Organization and Informatics, University of Zagreb, Pavlinska 2, 42000 Varaždin, Croatia)

Abstract

Background: The importance of up-to-date product master data in the digital age should not be underestimated. However, companies still struggle to ensure high-quality product data, especially in the field of logistics. Hence, the focus of our research lies in the disregard of the importance of product data quality to the performance of logistics processes. Methods: The analysis of the influence of product data on the performance of logistics processes was carried out using data from two fast-moving consumer goods (FMCG) distribution and retail companies. Data were gathered via interviews, while process activities were timed using a stopwatch, and interruptions were documented. The significance of the impact was determined using inferential statistical procedures based on the variable and the measurement scale type employed. Results: The quality of product master data has a significant impact on the performance of logistics processes; while managers are aware of the complications, they lack the motivation to detect and analyse such inaccuracies. Conclusions: The findings enhance comprehension of the obstacles generated by inadequate product data in logistics, which obstruct optimisation, and offer numerical proof of the impact of product data quality on logistics performance, thus expanding the current body of research.

Suggested Citation

  • Diana Božić & Margareta Živičnjak & Ratko Stanković & Andrej Ignjatić, 2024. "Impact of the Product Master Data Quality on the Logistics Process Performance," Logistics, MDPI, vol. 8(2), pages 1-19, April.
    • Handle: RePEc:gam:jlogis:v:8:y:2024:i:2:p:43-:d:1374778
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/8/2/43/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2305-6290/8/2/43/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tiwari, Sunil & Sharma, Pankaj & Jha, Ashish Kumar, 2024. "Digitalization & Covid-19: An institutional-contingency theoretic analysis of supply chain digitalization," International Journal of Production Economics, Elsevier, vol. 267(C).
    2. Jorij Abraham, 2014. "Product Information Management," Management for Professionals, Springer, edition 127, number 978-3-319-04885-7, December.
    3. Mirzabeiki, Vahid & Saghiri, Soroosh Sam, 2020. "From ambition to action: How to achieve integration in omni-channel?," Journal of Business Research, Elsevier, vol. 110(C), pages 1-11.
    4. Kai M. Hüner & Andreas Schierning & Boris Otto & Hubert Österle, 2011. "Product data quality in supply chains: the case of Beiersdorf," Electronic Markets, Springer;IIM University of St. Gallen, vol. 21(2), pages 141-154, June.
    5. Jairo R. Montoya-Torres & Diego A. Ortiz-Vargas, 2014. "Collaboration and information sharing in dyadic supply chains: A literature review over the period 2000–2012," Estudios Gerenciales, Universidad Icesi, November.
    6. Zhao, Nanyang & Hong, Jiangtao & Lau, Kwok Hung, 2023. "Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model," International Journal of Production Economics, Elsevier, vol. 259(C).
    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. Luo, Shuangcheng & Xiong, Zhiqiao & Liu, Jianjiang, 2024. "How does supply chain digitization affect green innovation? Evidence from a quasi-natural experiment in China," Energy Economics, Elsevier, vol. 136(C).
    2. Koliousis, Ioannis & Al-Surmi, Abdulrahman & Bashiri, Mahdi, 2024. "Artificial intelligence and policy making; can small municipalities enable digital transformation?," International Journal of Production Economics, Elsevier, vol. 274(C).
    3. Maggie Wang, Yazhu & Matook, Sabine & Dennis, Alan R., 2024. "Unintended consequences of humanoid service robots: A case study of public service organizations," Journal of Business Research, Elsevier, vol. 174(C).
    4. Qiansong Zhang & Yingying Zhang & Taiwen Feng, 2024. "Impacts of paradox cognition and organizational unlearning on supply chain resilience: a perspective of paradox theory," Operations Management Research, Springer, vol. 17(3), pages 1022-1038, September.
    5. Hongqin Tang & Jianping Zhu & Nan Li & Weipeng Wu, 2024. "Impact of Enterprise Supply Chain Digitalization on Cost of Debt: A Four-Flows Perspective Analysis Using Explainable Machine Learning Methodology," Sustainability, MDPI, vol. 16(19), pages 1-27, October.
    6. Tagashira, Takumi, 2022. "Information effects of warehouse automation on sales in omnichannel retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    7. Yanling Yang & Yanling Zheng & Guojie Xie & Yu Tian, 2022. "The Influence Mechanism of Strategic Partnership on Enterprise Performance: Exploring the Chain Mediating Role of Information Sharing and Supply Chain Flexibility," Sustainability, MDPI, vol. 14(8), pages 1-23, April.
    8. Li, Xi & Dahana, Wirawan Dony & Li, Tongmao & Yuan, Jingbo, 2021. "Behavioral changes of multichannel customers: Their persistence and influencing factors," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    9. Xiao, Yu & Lu, Louis Y.Y. & Liu, John S. & Zhou, Zhili, 2014. "Knowledge diffusion path analysis of data quality literature: A main path analysis," Journal of Informetrics, Elsevier, vol. 8(3), pages 594-605.
    10. Li, Dongdong & Gui, Mingxia & Ma, Rui & Feng, Yiwen, 2024. "Public data accessibility and corporate maturity mismatch: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
    11. Liu, Weihua & Yuan, Chaolun & Wang, Jingkun & Lim, Ming K & Hou, Jiahe, 2024. "Digital supply chain announcements and firm’s stock market value: An empirical study from China," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 187(C).
    12. Cricelli, Livio & Greco, Marco & Grimaldi, Michele, 2021. "An investigation on the effect of inter-organizational collaboration on reverse logistics," International Journal of Production Economics, Elsevier, vol. 240(C).
    13. Sun, Fei & Qu, Zhaojun & Wu, Ban & Bold, Sanchir, 2024. "Enhancing global supply chain distribution resilience through digitalization: Insights from natural resource sector of China," Resources Policy, Elsevier, vol. 95(C).
    14. Valeria Belvedere & Elisa Martina Martinelli & Annalisa Tunisini, 2021. "Getting the most from E-commerce in the context of omnichannel strategies," Italian Journal of Marketing, Springer, vol. 2021(4), pages 331-349, December.
    15. Rajan, Rishabh & Rana, Nripendra P. & Parameswar, Nakul & Dhir, Sanjay & Sushil, & Dwivedi, Yogesh K., 2021. "Developing a modified total interpretive structural model (M-TISM) for organizational strategic cybersecurity management," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    16. Chang, Victor & Doan, Le Minh Thao & Ariel Xu, Qianwen & Hall, Karl & Anna Wang, Yuanyuan & Mustafa Kamal, Muhammad, 2023. "Digitalization in omnichannel healthcare supply chain businesses: The role of smart wearable devices," Journal of Business Research, Elsevier, vol. 156(C).
    17. Moktadir, Md Abdul & Ren, Jingzheng, 2024. "Global semiconductor supply chain resilience challenges and mitigation strategies: A novel integrated decomposed fuzzy set Delphi, WINGS and QFD model," International Journal of Production Economics, Elsevier, vol. 273(C).
    18. Wang, Jixiao & Wang, Chong, 2024. "Understanding shoppers’ cross-channel analysis of influencing factors of online and offline channels: Evidence from clothing product," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    19. Esam Salamah & Ahmad Alzubi & Azmiye Yinal, 2023. "Unveiling the Impact of Digitalization on Supply Chain Performance in the Post-COVID-19 Era: The Mediating Role of Supply Chain Integration and Efficiency," Sustainability, MDPI, vol. 16(1), pages 1-28, December.
    20. Neil Henderson & Wesley Niemann, 2024. "Omni-Channel Retailing: Supply Chain Disruption Mitigation And Recovery In The South African Fashion Retail Industry," Eurasian Journal of Business and Management, Eurasian Publications, vol. 12(1), pages 51-76.

    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:gam:jlogis:v:8:y:2024:i:2:p:43-:d:1374778. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.