Transformation of Аnalytics of Large Databases in Procurement Management with the Development of Artificial Intelligence

Author:

Postnikov O.O.1ORCID,Smerichevska S. V.2ORCID

Affiliation:

1. National University of Defense of Ukraine named after Ivan Chernyakhovsky, Kyiv

2. National Aviation University, Kyiv

Abstract

The article offers an in-depth examination of the current state, as well as the growth trajectories, of the global market for big data analytics, with a specific focus on the subfield of procurement analytics. It goes beyond mere surface-level statistics to provide a nuanced understanding of market trends and potential future directions. This is explored not just in the context of the European Union but also extends to a detailed case study involving Ukraine, thereby offering a more global perspective. Furthermore, the article scrutinizes the various data sources that can be leveraged for making well-informed management decisions in the realm of procurement. It doesn’t stop at merely listing these sources but goes on to analyze their respective merits and limitations. In addition, the article provides real-world examples from Ukraine, showcasing the practical applications of data analytics in procurement processes, thereby grounding the theoretical discussions in empirical reality. The article also ventures into the burgeoning field of artificial intelligence (AI), outlining its transformative potential in procurement data analytics. It characterizes the myriad benefits that AI can bring to procurement management, from increased efficiency to more nuanced decision-making capabilities. To guide practitioners, the article proposes a detailed algorithmic workflow for employing AI in the analysis of data crucial for procurement decisions. This serves as a practical roadmap for organizations looking to integrate AI into their procurement strategies. However, the article is not blindly optimistic about the role of AI; it also brings to the fore the potential risks associated with employing artificial intelligence for the analysis of large and complex databases. This balanced approach adds a layer of caution to the otherwise optimistic narrative, making the article a comprehensive and nuanced contribution to the literature on procurement analytics and artificial intelligence. In sum, the article serves as a robust academic resource that traverses the landscape of procurement analytics, from market trends and macroeconomic impacts to the practicalities and potential pitfalls of AI integration.

Publisher

Academy of Economic Sciences of Ukraine

Subject

General Medicine

Reference28 articles.

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2. Procurement Analytics Demystified Updated. (2023). Retrieved from https://sievo.com/resources/procurement-analytics-demystified.

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4. Sandford, L. This is how and why you should use AI in procurement: A complete guide for 2023. Retrieved from https://oneflow.com/blog/ai-in-procurement-complete-guide/.

5. Taylor, P. (2022). Global big data analytics market size 2021-2029. Retrieved from https://www.statista.com/statistics/.

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