Theses (Department of Engineering Management and Enterprise)

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https://hdl.handle.net/2437/360723
Theses collection of the Faculty of Engineering.

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Theses (Department of Engineering Management and Enterprise) Szerző szerinti böngészés "ALHAWATMAH, MOAYAD MAHMOUD BARAKAT"

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    Artificial intelligence, industry 4.0 and Engineering Management implementation
    ALHAWATMAH, MOAYAD MAHMOUD BARAKAT; ZSOLT, BURI; DE--Műszaki Kar
    In this thesis we discuss the Artificial intelligence and industry four revolutions since the beginning and how the Artificial Intelligence affected the industry aspects starting from productivity to advanced planning and quality functions. Before applying any cutting-edge technology there will be few obstacles and concerns such as: Job displacement, ethical concerns, policies and regulations, availability of basic requirements, Knowledge of the technology and cost consideration. The main methodology in this thesis was the case studies in different fields and applications which results in deep understanding how vast can that Artificial Intelligence technology be applied in the present and how much is going to develope and expand in the future . Additionally, this thesis discusses integration of Artificial intelligence with many modern technologies such as Augmented Reality, IOT and Cyber-Physical Systems. The research suggests that industry practitioners should invest in AI-driven process automation and optimization technologies to improve production efficiency and reduce operational costs. Quality-conscious organizations should adopt AI-enabled quality control and defect detection systems to ensure consistent product quality and meet customer expectations. Prioritizing safety and investing in AI-driven collision avoidance and safety monitoring systems can enhance human-robot collaboration. Leveraging AI and AR for workforce training can improve skill acquisition and reduce training time. Ethical considerations should be at the forefront of AI adoption, with reskilling and upskilling programs facilitated by collaboration with educational institutions and industry associations. Data privacy and security in AI-enabled systems should be prioritized. Future research should explore the long-term impact of AI adoption on job roles, explainable AI's role in enhancing transparency, AI-driven sustainability strategies, scalability and adaptability in small and medium-sized enterprises, and the social and economic implications of AI adoption in Industry 4.0.