Machine Learning Empowerment in Industry 4.0 – Case Study for Micro and Small Enterprises in Romania

Bogoevici Flavia (), Albu Octavia (), Duță Ruxandra () and Chitca Camelia ()
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Bogoevici Flavia: Bucharest University of Economic Studies, Bucharest, Romania
Albu Octavia: Bucharest University of Economic Studies, Bucharest, Romania
Duță Ruxandra: Bucharest University of Economic Studies, Bucharest, Romania
Chitca Camelia: Bucharest University of Economic Studies, Bucharest, Romania

Proceedings of the International Conference on Business Excellence, 2024, vol. 18, issue 1, 3357-3373

Abstract: In the world in which technology quickly integrates in our daily lives, businesses that incorporate digital innovation throughout their organizational culture, spanning from top-level executives to low-level employees are prone to emerge as industry frontrunners. Supported by Machine Learning, which stands out as a pivotal revolutionary tool, companies can enhance their productivity and operational efficiencies by incorporating remarkable automation capabilities, error reduction, superior predictive analysis, together with gaining valuable insights into future trends. The paper confers an overview of Machine Learning’s capabilities, developed types, provided solutions and built architecture, through a conceptual structure. The paper elaborates these crucial concepts, offering a precise perspective on the topic and adopts a descriptive approach, elucidating the provided terminologies and ideas by referencing the related literature. The paper highlights in the initial part the outcomes resulting from the key advantages of Machine Learning and its impact on organizations, the path towards realizing substantial value through these digital advancements, emphasizing the priority organizations assign to cultivate their digital potential. The research performed in the second part of the paper aims at analyzing the progress of Romanian micro and small enterprises with implemented Machine Learning solutions, with detailed metrics and comprising k-means clustering, having the following objectives: automating repetitive tasks, improving planning and forecasting, increasing net profit, effortlessly discovering new patterns from large, diverse data models.

Keywords: Machine Learning; Clustering; Industry 4.0; Prediction; Optimization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:18:y:2024:i:1:p:3357-3373:n:1047

DOI: 10.2478/picbe-2024-0274

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