This thesis explores the intricate relationship between Lean philosophy, particularly the Kaizen culture, and Artificial Intelligence (AI). The study begins by analyzing the intersection of Lean principles with the digital world, establishing a foundation for understanding how these methodologies can coexist and complement each other. It then delves into the realm of AI, providing a comprehensive overview of its capabilities and potential. The core of the thesis investigates the synergy between AI and Continuous Improvement, highlighting how AI can enhance and streamline Kaizen practices. Through practical applications and case studies, the research demonstrates the effective implementation of AI technologies in supporting and advancing the principles of Kaizen. This investigation underscores the transformative potential of integrating AI with Lean methodologies, paving the way for more efficient, data-driven, and adaptive business processes.

This thesis explores the intricate relationship between Lean philosophy, particularly the Kaizen culture, and Artificial Intelligence (AI). The study begins by analyzing the intersection of Lean principles with the digital world, establishing a foundation for understanding how these methodologies can coexist and complement each other. It then delves into the realm of AI, providing a comprehensive overview of its capabilities and potential. The core of the thesis investigates the synergy between AI and Continuous Improvement, highlighting how AI can enhance and streamline Kaizen practices. Through practical applications and case studies, the research demonstrates the effective implementation of AI technologies in supporting and advancing the principles of Kaizen. This investigation underscores the transformative potential of integrating AI with Lean methodologies, paving the way for more efficient, data-driven, and adaptive business processes.

Artificial Intelligence and its role in supporting the Kaizen Culture in companies: some case studies

CADAMURO, ALESSANDRO
2023/2024

Abstract

This thesis explores the intricate relationship between Lean philosophy, particularly the Kaizen culture, and Artificial Intelligence (AI). The study begins by analyzing the intersection of Lean principles with the digital world, establishing a foundation for understanding how these methodologies can coexist and complement each other. It then delves into the realm of AI, providing a comprehensive overview of its capabilities and potential. The core of the thesis investigates the synergy between AI and Continuous Improvement, highlighting how AI can enhance and streamline Kaizen practices. Through practical applications and case studies, the research demonstrates the effective implementation of AI technologies in supporting and advancing the principles of Kaizen. This investigation underscores the transformative potential of integrating AI with Lean methodologies, paving the way for more efficient, data-driven, and adaptive business processes.
2023
Artificial Intelligence and its role in supporting the Kaizen Culture in companies: some case studies
This thesis explores the intricate relationship between Lean philosophy, particularly the Kaizen culture, and Artificial Intelligence (AI). The study begins by analyzing the intersection of Lean principles with the digital world, establishing a foundation for understanding how these methodologies can coexist and complement each other. It then delves into the realm of AI, providing a comprehensive overview of its capabilities and potential. The core of the thesis investigates the synergy between AI and Continuous Improvement, highlighting how AI can enhance and streamline Kaizen practices. Through practical applications and case studies, the research demonstrates the effective implementation of AI technologies in supporting and advancing the principles of Kaizen. This investigation underscores the transformative potential of integrating AI with Lean methodologies, paving the way for more efficient, data-driven, and adaptive business processes.
AI
Kaizen
Lean
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/80924