Different Perspectives of a Factory of the Future: An Overview

Salierno, Giulio; Cabri, Giacomo; Leonardi, Letizia

doi:10.1007/978-3-030-20948-3_10

Cited by 4 publications

(2 citation statements)

References 24 publications

(29 reference statements)

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“…However, the journey towards realizing the Factories of the Future is fraught with challenges, including the need for substantial investments in digital infrastructure [19], the right technical and ethical use of IA [20], the development of new skill sets among the workforce, and the formulation of supportive policy frameworks that encourage innovation while safeguarding workers' rights and environmental sustainability [20][21][22][23][24]. This paper aims to explore these challenges and opportunities in the context of digitizing industrial urban areas, drawing upon recent research, case studies, and policy discussions to provide a comprehensive overview of the current landscape and future prospects of Factories of the Future.…”

Section: Introductionmentioning

confidence: 99%

Factories of the Future in Digitization of Industrial Urban Areas

Bolshakov,

Celani,

Badenko

et al. 2024

Urban Science

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This paper delves into the integration of Factories of the Future (FoF) and digital twin technologies within urban contexts, marking a significant leap in Smart Cities development. We present a thorough exploration of the principles and a scientifically grounded framework designed for seamlessly blending advanced manufacturing systems with the urban environment’s digital and physical aspects. Our detailed analysis has identified core principles crucial for this integration, focusing on interoperability, sustainability, adaptability, stakeholder collaboration, and strong data governance. We propose a structured framework that puts these principles into action, outlining strategic routes for incorporating digital twin and Building Information Modeling (BIM) technologies into FoF, establishing public-private partnerships, enhancing education and workforce development, and setting up mechanisms for ongoing evaluation and enhancement. The potential of this integration to transform urban development is vast, providing a model for boosting operational efficiency, driving economic growth, and enhancing urban livability. Although challenges exist in realizing this vision, our research offers practical insights and strategies for cities and industries to effectively navigate the complexities of the digital era. This contribution enriches the growing field of urban science, advocating for a harmonious integration of industrial production with urban development in the Smart Cities framework.

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Section: Introductionmentioning

confidence: 99%

Factories of the Future in Digitization of Industrial Urban Areas

Bolshakov,

Celani,

Badenko

et al. 2024

Urban Science

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“…These goals can be achieved by utilizing recently introduced artificial intelligence (AI) technologies and data collected from manufacturing sites [7,8]. Data are particularly essential for the successful application of AI technologies, from achieving a specific goal to realizing complete smart manufacturing [9].…”

Section: Introductionmentioning

confidence: 99%

Real-World Failure Prevention Framework for Manufacturing Facilities Using Text Data

Park

Choi²,

Choi

2021

Processes

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In recent years, manufacturing companies have been continuously engaging in research for the full implementation of smart factories, with many studies on methods to prevent facility failures that directly affect the productivity of the manufacturing sites. However, most studies have only analyzed sensor signals rather than text manually typed by operators. In addition, existing studies have not proposed an actual application system considering the manufacturing site environment but only presented a model that predicts the status or failure of the facility. Therefore, in this paper, we propose a real-world failure prevention framework that alerts the operator by providing a list of possible failure categories based on a failure pattern database before the operator starts work. The failure pattern database is constructed by analyzing and categorizing manually entered text to provide more detailed information. The performance of the proposed framework was evaluated utilizing actual manufacturing data based on scenarios that can occur in a real-world manufacturing site. The performance evaluation experiments demonstrated that the proposed framework could prevent facility failures and enhance the productivity and efficiency of the shop floor.

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