The Role of Artificial Intelligence in Circular Economy Strategies: Predictive Analysis for SMEs

• Autorzy: Klimecka-Tatar Dorota , Kapustka Katarzyna

Identyfikatory

DOI

10.2478/mspe-2025-0020

• Języki publikacji: EN

Abstrakty

EN

This study explored the role of Circular Economy (CE) strategies in small and medium-sized enterprises (SMEs), with a particular focus on integrating Artificial Intelligence (AI) to optimize CE performance. The research aimed to identify the key determinants influencing CE indicators by using Principal Component Analysis (PCA) and regression modeling. The findings revealed that factors such as employment in CE sectors, resource productivity, and effective waste management practices significantly impact circularity outcomes. These factors were found to be crucial for SMEs striving to enhance sustainability and reduce environmental impact through circular economy practices. The study primarily focused on general Circular Economy strategies, meaning the results may vary across different industries, particularly those with varying waste streams and resource challenges. For instance, certain sectors might face specific hurdles in waste management or resource efficiency, making the application of CE strategies more complex. Additionally, the study uncovered the complexity of systemic interactions within CE implementation, such as the negative correlation between municipal recycling rates and circular material use, which requires further exploration. These findings suggest that understanding the broader systemic factors affecting CE is essential to fully realizing its potential. Moreover, the integration of AI in CE strategies emerged as a promising avenue for optimizing resource management, improving waste reduction, and enhancing productivity. AI can play a critical role in identifying inefficiencies, predicting trends, and streamlining operations in SMEs. This study contributes to the growing body of knowledge on CE in SMEs, emphasizing the importance of AI in advancing sustainability and efficiency in circular practices. Further research is needed to explore industry-specific challenges and systemic interactions in greater detail.

Słowa kluczowe

EN

circular economy; artificial intelligence; circular economy indicators; SMEs; circular economy predictive analysis

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2025
• Tom: nr 2 (33)
• Strony: 212--219
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Klimecka-Tatar Dorota

• Częstochowa University of Technology Faculty of Management Department of Production Engineering and Safety ul. Dąbrowskiego 69, 42-201 Częstochowa, Poland

• https://orcid.org/0000-0001-6212-6061

autor

Kapustka Katarzyna

• Hochschule Koblenz – University of Applied Sciences Faculty of Engineering Department of Mechanical Engineering Koblenz, Germany

• https://orcid.org/0000-0003-4890-2807

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).