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Implementing AI Collaborative Robots in Manufacturing – Modeling Enterprise Challenges in Industry 5.0 with Fuzzy Logic
 
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1
Department of Enterprise Organization, Faculty of Management, Lublin University of Technology, Nadbystrzycka 38d, 20-618 Lublin, Poland
 
2
Faculty of Economics, Poland, Maria Curie-Skłodowska University, ul. Marii Curie-Skłodowskiej 5, 20-031 Lublin, Poland
 
These authors had equal contribution to this work
 
 
Corresponding author
Michał Cioch   

Department of Enterprise Organization, Faculty of Management, Lublin University of Technology, Nadbystrzycka 38d, 20-618 Lublin, Poland
 
 
Adv. Sci. Technol. Res. J. 2024; 18(7):229-238
 
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ABSTRACT
The purpose of this article is to propose a fuzzy logic system as a tool for automated risk identification of potential technical challenges and social barriers during the implementation of artificial intelligence-based co-bots on workstations in manufacturing enterprises. On the basis of an extensive literature review, as well as industry reports and expert consultations, the basic challenges and enterprise barriers occurring during the implementation of changes in enterprises, especially during the implementation of the latest technologies, were selected. A fuzzy logic model was then developed that, based on the values of the input factors, generates an answer as to whether there is a risk of technical or social challenges in an enterprise when implementing the latest technologies. The results generated by the developed model, when confronted with expert knowledge, experience and subjective assessments, showed that the model works as expected. The results of the study suggest that the use of fuzzy logic can effectively support companies in detecting challenges and obstacles, thereby facilitating decision-making in reducing the risk of their occurrence. Adaptation to the conditions currently prevailing in the company allows for dynamic adjustment of co-bot deployment strategies, which in turn can lead to more effective management of technological changes and minimization of potential operational disruptions.
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