Industrial Metaverse Dynamics : Metaverse Strategies for Material Flow Optimization in Industry Evolution
Lignell, Anni (2024)
2024
Sähkötekniikan DI-ohjelma - Master's Programme in Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2024-07-30
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202407267763
https://urn.fi/URN:NBN:fi:tuni-202407267763
Tiivistelmä
The generalization of the Fourth Industrial Revolution and the emerging concept of the Industry 5.0 paradigm are challenging industries to re-evaluate and rethink their operational frameworks. Amidst this transformation, the intricate relationships between innovative technologies and human-centric work principles becomes increasingly pivotal. This thesis explores the industrial metaverse and its role in optimizing material flow within industry settings, with the particular focus on warehousing processes in a lifting business.
The challenge posed by the thesis relates to the limited integration of human-centric models within rapidly advancing technological landscape of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), collectively used in the industrial metaverse. This thesis describes a definition of the industrial metaverse, acutely tailored to align with operational dynamics of industry services and investigates how the incorporation of immersive metaverse technologies can increase efficiency, productivity, as well as sustainability in industrial warehousing procedures.
A qualitative research methodology, including a comprehensive literature review and comparative analyses, was used to investigate the essence and efficacy of metaverse technologies. The exploration resulted in a conceptual framework that defines the industrial metaverse's architecture and reflects on technological enablers and presents a pragmatic viewpoint on the integration of AR wearables and Machine Learning algorithms. The overall goal of these is to improve the role of humans in material flow processes in warehouse operations.
The positive findings of the thesis showed that AR wearables can significantly reduce human errors and accelerate task completion, underscoring the transformative potential of the metaverse in operational contexts. Furthermore, the thesis highlights the particular advantages and challenges experienced by warehouse operators when adopting these metaverse technologies, emphasizing the necessity for comprehensive training and adaptation to new ways of working.
The principal contribution of this thesis is the compilation of insights into the definitional scope and practical applicability of metaverse technologies within an industrial setting. It proposes an industrial metaverse framework that optimizes material flow with a resolute emphasis on human-centric principles and introduces a speculative vision for a metaverse-augmented warehouse. The findings reveal critical avenues for potential enhancements in industrial processes, implicating a more efficient, sustainable, and human-centered future for industrial operations.
The challenge posed by the thesis relates to the limited integration of human-centric models within rapidly advancing technological landscape of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), collectively used in the industrial metaverse. This thesis describes a definition of the industrial metaverse, acutely tailored to align with operational dynamics of industry services and investigates how the incorporation of immersive metaverse technologies can increase efficiency, productivity, as well as sustainability in industrial warehousing procedures.
A qualitative research methodology, including a comprehensive literature review and comparative analyses, was used to investigate the essence and efficacy of metaverse technologies. The exploration resulted in a conceptual framework that defines the industrial metaverse's architecture and reflects on technological enablers and presents a pragmatic viewpoint on the integration of AR wearables and Machine Learning algorithms. The overall goal of these is to improve the role of humans in material flow processes in warehouse operations.
The positive findings of the thesis showed that AR wearables can significantly reduce human errors and accelerate task completion, underscoring the transformative potential of the metaverse in operational contexts. Furthermore, the thesis highlights the particular advantages and challenges experienced by warehouse operators when adopting these metaverse technologies, emphasizing the necessity for comprehensive training and adaptation to new ways of working.
The principal contribution of this thesis is the compilation of insights into the definitional scope and practical applicability of metaverse technologies within an industrial setting. It proposes an industrial metaverse framework that optimizes material flow with a resolute emphasis on human-centric principles and introduces a speculative vision for a metaverse-augmented warehouse. The findings reveal critical avenues for potential enhancements in industrial processes, implicating a more efficient, sustainable, and human-centered future for industrial operations.