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Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked mac...
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Cyber-Physical Systems (CPS) are considered as the emerging components for Industry 4.0, the state-of-the art and standard CPS architecture playing the major role in understanding the nature of the industrial landscape. The key problem with traditional CPS architectures is that they are not up-to-mark and convincing to fulfil the needs of smart ind...
Decreasing batch sizes in production in line with Industrie 4.0 will lead to tremendous changes of the control of logistic processes in future production systems. Intelligent bins are crucial enablers to establish decentrally controlled material flow systems in value chain networks as well as at the intralogistics level. These intelligent bins have...
The advent of the digital industry, also known as Industry 4.0 is a transformation period in manufacturing, where the integration of digital technologies with physical systems is underlined. This transformation is crucial for the pillars of cyber-physical systems (CPS), cyber resilience protection, and workers’ safety, which collectively from the c...
The recently emerged methodologies for interconnected systems such as cyber-physical systems are focused to closely monitor the information and synchronize it between the physical connected systems and cyber computational space. Depending on the physical system being monitored, the approach for designing and implementing the framework for interconn...
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A worldwide trend in advanced manufacturing countries is defining Industrie 4.0, Industrial Internet and Factories of the Future as a new wave that can revolutionize the production and its associated services. Cyber-Physical Systems (CPS) are central to this vision and are entitled to be part of smart machines, storage systems and production facili...
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... em cloud diperlukan untuk menyimpan data besar dan melakukan analisis segera. Sistem fisik siber (CPS) didefinisikan sebagai teknologi transformatif yang mengubah entitas fisik yang terdeteksi melalui sensor menjadi pengetahuan yang dapat dipahami oleh robot dan mengirimkan bagaimana pengetahuan tersebut dapat digunakan oleh robot lain dan manusia (Lee. J et al 2015). ...
Dalam literatur, penelitian yang cukup terbatas mengenai topik Society 5.0. Penelitian ini menguji keberadaan Society 5.0 dan efektivitas Industry 4.0, serta mengevaluasi efisiensi Tujuan Pembangunan Berkelanjutan PBB (SDGs) dalam proses tersebut, terutama di Indonesia. Penelitian ini dilakukan berdasarkan data yang diperoleh melalui kuesioner berisi 30 pertanyaan yang dilakukan kepada 335 akademisi yang bekerja di Universitas X. Data tersebut dianalisis menggunakan analisis faktor eksploratori dengan program SPSS, analisis faktor konfirmatori dengan AMOS, dan pemodelan persamaan struktural dengan Smart PLS. Hasil analisis menunjukkan bahwa SDG9, SDG10, SDG11, SDG12, SDG13, dan SDG14 memiliki pengaruh rendah (yaitu, R2: 0,172) terhadap penerapan Industry 4.0 dan Society 5.0. Selain itu, juga ditemukan bahwa para peserta sangat dipengaruhi oleh situasi yang ada dan memberikan tanggapan sesuai dengan dampak tersebut. Studi ini juga mengungkapkan bahwa Indonesia tidak memiliki filosofi unggulan dalam bidang Society 5.0 dan Industry 4.0, dan kemajuannya terhambat karena terlalu fokus pada proses-proses yang sudah ketinggalan zaman.
... With the transformation to Industry 4.0 (Bär et al. 2018;Bécue et al. 2021;Caiado et al. 2021;Dalenogare et al. 2018;Faller and Feldmüller 2015;Fatorachian and Kazemi 2021;Ghodmare et al. 2021;Gunasekaran et al. 2018;Hamid 2022;Hofmann and Rüsch 2017;Jazdi 2014;Kolberg and Zühlke 2015;Lee et al. 2014Lee et al. , 2015Lezzi et al. 2018;Liao et al. 2017; Ministry of Economy Industry and Competitiveness Accessibility 2015; Müller et al. 2018;Pan et al. 2015;Peasley et al. 2017;Peukert et al. 2020;Radanliev 2019;Radanliev et al. 2021;Reischauer 2018;Rinaldi et al. 2019;Rivas et al. 2018;Schlechtendahl et al. 2014;Shao et al. 2021;Sittón-Candanedo 2020;Sokolov and Ivanov 2015;Stock and Seliger 2016;Sung 2017;Waslo et al. 2017;Weyer et al. 2015), the industrial industry is integrating digital technologies on an unprecedented scale. Intelligent factories with networked machinery, automated processes, and data-driven decision-making exemplify this change. ...
This paper scrutinises the evolving digital security landscape, encompassing technological advancements, regulatory frameworks, and industry-specific challenges. It explores the influence of technologies like AI, quantum computing, and blockchain on security paradigms whilst identifying emergent threats. The study analyses the interplay between digital security and legislative policies, underlining their impact on industry practices and individual behaviours. Sector-specific examinations are conducted, pinpointing unique security concerns in sectors such as healthcare and finance and advocating bespoke solutions. The study highlights discrepancies between security intentions and actions, proposing strategies to bridge this divide. Projecting into the future, we anticipate shifts in technology and regulation, culminating in pragmatic recommendations for stakeholders. This article offers an informed perspective on digital security, laying the groundwork for proactive approaches in a dynamic digital environment.
... Diagnostics significantly impact the development, operation, and maintenance phases of system lifecycle management. During the development phase, diagnostics tools and techniques are used to validate design assumptions, verify system functionality, and ensure that the system meets all specified requirements [7]. Once the system is operational, diagnostics facilitate ongoing maintenance and troubleshooting, enabling the timely detection and resolution of issues that may arise. ...
This chapter explores the evolution and significance of advanced diagnostics tools in complex system management, emphasizing the shift toward integrated, intelligent, and predictive diagnostics. It covers the application of artificial intelligence (AI) and machine learning (ML) for predictive maintenance, real-time monitoring, and the integration of sensor technologies with the Internet of Things (IoT). The text examines the use of diagnostic tools in various fields, including mechanical, electrical, software, and network systems, with specific attention to industry applications in automotive, aerospace, and healthcare sectors. These examples illustrate how diagnostics are transforming these industries by enhancing efficiency, safety, and reliability. The chapter also discusses the role of diagnostics in system design, the importance of built-in diagnostics, and the challenges of managing and interpreting the large volumes of data these tools generate. It highlights innovative data analysis and visualization techniques and looks ahead to future trends in diagnostics, such as the potential impact of quantum computing and concerns over cybersecurity and data privacy. Overall, the chapter provides a comprehensive overview of the current state and future directions of advanced system diagnostics.
... By using anomaly detection algorithms and predictive models, companies can identify potential equipment failures before they occur, reducing downtime and maintenance costs. These ML solutions contribute to higher operational efficiency and better resource management (Lee et al., 2015). 3. ML-driven supply chain optimization has enhanced inventory management and demand forecasting. ...
This paper explores the application of machine learning (ML) in solving complex problems within enterprises across various industries. By leveraging ML, businesses can enhance operational efficiency, customer experience, and risk management. The study reviews existing literature to develop a theoretical model that integrates ML applications into business processes. Key findings indicate that ML significantly improves quality control and predictive maintenance in manufacturing, leading to reduced costs and increased productivity. Additionally, ML-driven personalized marketing and customer support enhance customer satisfaction and loyalty. In financial management, ML enhances fraud detection and credit risk assessment, contributing to financial stability and security. The paper provides suggestions for effectively implementing ML strategies to optimize business performance and addresses the implications for future business operations in a rapidly evolving technological landscape.
... For example, digital stock checks, transactions, and revenue data analysis will provide asset efficiency. Asset efficiency can be shown by lower asset downtime, capacity optimization, changeover time efficiency [11], and increased productivity and profitability [12]. ...
Aim: To investigate the awareness of using digital technology in Food MSMEs and understand its benefits. Study Design: This research uses a quantitative approach with hypothesis testing. Place and Duration of Study: The research location is in the Borobudur area, Borobudur District, Magelang Regency, Central Java, one of Indonesia's priority tourist destinations. The duration of the research study is approximately 6 months, from June to December 2023. Methodology: This study's dependent or bound variable (Y) is Food MSME's Scaling Up in the Borobudur area. Independent or independent variables (X) are Asset Efficiency (X1), Lower Cost (X2), Quality (X3), and Safety & Sustainability (X4). Data collection was done through surveys, questionnaires, and structured observations. The number of respondents in this study was 145 respondents of local food MSMEs are in the Borobudur area, Borobudur District, Magelang Regency, Central Java. Data is tested with the Partial Least Squares Structural Equation Model (PLS-SEM) using SmartPLS version 3. Results: The results obtained in terms of quality (QT), lower cost (LC), safety, and Sustainability (SAS) were found to have a significant impact on food MSME's scaling up (FMS). However, asset efficiency (AE) was found to have no significant impact on food MSME scaling up (FMS). This research can contribute valuable insights to guide policymakers, industry stakeholders, and MSMEs toward more effective technology adoption strategies that drive growth and resilience in an increasingly digital business landscape. Conclusion: Quality (QT), Lower Cost (LC), Safety And Sustainability (SAS) were found to have a significant impact on food MSME's scaling up (FMS). However, asset efficiency (AE) was found to have no significant impact on food MSME's scaling up (FMS). Overcoming challenges associated with digital adoption is crucial for these businesses to thrive in a rapidly evolving business landscape.
... Within the context of the Fourth Industrial Revolution, the concept of a "Digital Twin" has significantly impacted urban planning by providing a virtual model that can simulate and analyze real-world behaviors. The technologies of the digital twin, operating at various levels of detail, enable the prediction of performance, process optimization, and cost reduction across sectors such as manufacturing, transportation, and energy [1][2][3]. Effective urban management and modeling are essential for fostering sustainable smart city development [4]. Therefore, it is critical to analyze the implications of urbanization and devise innovative solutions for the development of cities [5,6]. ...
... The integrated standardized data are securely stored in the 3D City Database, while the sensor data are kept separate to ensure the integrity of the geographic attribute processing [53,54]. (2) To facilitate the correlation between the geographic information of the sensor data and the 3D building data, an ontology of the data is built so that they can be extracted and represented as RDF graphs. Open-source platforms such as TerriaJs, Cesium, Neo4j, or Unreal Engine can be used to perform contextual visualization. ...
The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard’s levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study’s results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning.
... IoT, for instance, allows for the connection of physical devices to the internet, enabling real-time monitoring and data collection. This has transformed manufacturing processes, leading to the development of smart factories where machines communicate and make autonomous decisions, optimizing production and reducing downtime (Lee et al., 2014). ...
Published by: This study aims to examine the digital transformation in industrial technology and its social impact on online public transportation. The primary focus is on analyzing how advancements in digital technology are reshaping the public transportation sector and the resultant social implications. A qualitative research method was employed, using case studies to provide an in-depth understanding of the phenomena. Data collection was conducted through comprehensive interviews with industry experts, public transportation operators, and users of online transportation services. Additionally, relevant documents and reports related to digital transformation and its impact on public transportation were analyzed. The results of the study indicate that digital transformation has significantly improved the efficiency, accessibility, and convenience of public transportation services. Innovations such as mobile applications, real-time tracking, and automated scheduling have enhanced the user experience and operational efficiency. From a social perspective, the adoption of digital technology in public transportation has facilitated greater inclusivity and mobility for various demographic groups, including the elderly and people with disabilities. However, the study also highlights several challenges, including digital divide issues, cybersecurity concerns, and the need for continuous technological upgrades and training. In conclusion, the digital transformation in industrial technology has a profound positive impact on online public transportation, enhancing service delivery and social inclusivity. The study recommends ongoing investment in digital infrastructure and comprehensive stakeholder engagement to address the challenges and maximize the benefits of digital transformation in the public transportation sector.
... For Industry 4.0, since Kagermann et al. [7] created the concept, with the study of related technologies and different application scenarios, Lee et al. [8] in their paper explored the implementation of information physical systems (CPS), which provide a framework for the connection between physical devices and networks. In addition to CPS, Monostori [9] has developed another system called the Information Physical Production System (CPPS), which can track the specific process of the manufacturing system. ...
The Fourth Industrial Revolution, known as Industry 4.0, represents a transformative period in industrial production, characterized by the seamless integration of cutting-edge technologies into manufacturing and supply chain operations. This research delves into the preparedness of China's burgeoning industrial Internet sector to embrace the principles of Industry 4.0, with a particular focus on revolutionizing supply chain management practices. Through a comprehensive analysis encompassing strategic, legal, technological, and organizational dimensions, this study elucidates the significant challenges and promising opportunities confronting the industry. Despite encountering hurdles at both the technical and organizational fronts, China's industrial Internet landscape exhibits remarkable strides forward, bolstered by proactive governmental policies and robust inter-industry collaborations. To capitalize on this momentum, the study advocates for the adoption of an innovative supply chain model that seamlessly integrates cloud computing and Radio-Frequency Identification (RFID) technology. This pioneering model promises to revolutionize supply chain dynamics, enabling real-time assessment, heightened operational efficiency, and more informed decision-making processes, thereby catalyzing the industry's evolution towards a digitally-driven future.
... IoT, for instance, allows for the connection of physical devices to the internet, enabling real-time monitoring and data collection. This has transformed manufacturing processes, leading to the development of smart factories where machines communicate and make autonomous decisions, optimizing production and reducing downtime (Lee et al., 2014). ...
This study aims to examine the digital transformation in industrial technology and its social impact on online public transportation. The primary focus is on analyzing how advancements in digital technology are reshaping the public transportation sector and the resultant social implications. A qualitative research method was employed, using case studies to provide an in-depth understanding of the phenomena. Data collection was conducted through comprehensive interviews with industry experts, public transportation operators, and users of online transportation services. Additionally, relevant documents and reports related to digital transformation and its impact on public transportation were analyzed. The results of the study indicate that digital transformation has significantly improved the efficiency, accessibility, and convenience of public transportation services. Innovations such as mobile applications, real-time tracking, and automated scheduling have enhanced the user experience and operational efficiency. From a social perspective, the adoption of digital technology in public transportation has facilitated greater inclusivity and mobility for various demographic groups, including the elderly and people with disabilities. However, the study also highlights several challenges, including digital divide issues, cybersecurity concerns, and the need for continuous technological upgrades and training. In conclusion, the digital transformation in industrial technology has a profound positive impact on online public transportation, enhancing service delivery and social inclusivity. The study recommends ongoing investment in digital infrastructure and comprehensive stakeholder engagement to address the challenges and maximize the benefits of digital transformation in the public transportation sector.
... Compared with classical production systems, CPS can realize a more flexible manufacturing process to improve efficiency and productivity, providing significant real-time advantages, costs, and resources [55]. The development and implementation of CPS in the manufacturing environment will realize the vertical and horizontal integration of IT systems and interconnect the whole supply chain, which will transform today's factories into smart ones [56]. ...
The word “Industry 4.0” was first introduced in 2011 at the Hannover Fair and created a wave of revolution in a wide variety of industries. Many organizations and companies want to improve manufacturing efficiency and competitiveness by employing some technologies of Industry 4.0. However, the relevance of those technologies to the goal of Industry 4.0 is often unclear, and the concept of Industry 4.0 is vague to many people. The paper clarifies the concept and driving factors of Industry 4.0 by reviewing the history of industrial revolutions. Then, some key technologies of Industry 4.0 and the state of smart factories are introduced. Besides, the steps to implement a smart factory are presented. Finally, this paper gives a case study from semiconductor manufacturing to demonstrate the benefits and application of a smart factory.
