The deployment of smart manufacturing technologies like communication, computing, sensors, cyber-physical systems, simulation, and data sciences has enabled the operational transformations of the ...factory ecosystem. Therefore, it is essential to monitor the alterations commenced by the business due to smart manufacturing implementation. Smart Manufacturing Performance Measures (SMPMs) are used to evaluate operational transformations realized through digitalization of systems. However, there is no reported work focusing on the quantification of SMPMs. This study addresses the research gap by defining potential indicators to quantify SMPMs referred to as smart manufacturing performance indicators (SMPIs) identified through literature review methodology. The SMPIs are discussed in detail besides their mathematical expression advocated through in-depth literature study. Further, a conceptual framework for decision-making in smart manufacturing environment based on SMPIs is proposed. The conceptual framework provides guidelines to plan and select the preferred focused manufacturing output and the relevant set of SMPIs contributing to the outputs for expediting effective smart manufacturing implementation. The research findings are beneficial for the managers and consultants to gauge the potential structure of measurement that can be used to evaluate smart manufacturing systems' performance.
•The most important habilitating technologies for Industry 4.0 and Smart Manufacturing are presented.•Trends are discussed.•Basic concepts are defined to contextualize further discussion.
Industry ...4.0 refers to the integration of a multiplicity of technologies and agents for the common goal of improving the efficiency and responsiveness of a production system. This integration has the potential to revolutionize the manner in which business are planned and conducted. Smart Manufacturing represents the implementation of Industry 4.0 on the manufacturing floor. The Internet of Things, Big Data, Cyber Physical Systems, Machine Learning, Additive Manufacturing, and Robotics are only some of the elements that are associated with this revolution. This article discusses trends in some of the habilitating technologies of Industry 4.0.
The recent White House report on Artificial Intelligence (AI) (Lee, 2016) highlights the significance of AI and the necessity of a clear roadmap and strategic investment in this area. As AI emerges ...from science fiction to become the frontier of world-changing technologies, there is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Within the 5C architecture previously proposed in Lee et al. (2015), this paper provides an insight into the current state of AI technologies and the eco-system required to harness the power of AI in industrial applications.
The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient ...manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.
•A new topic on multi-stakeholder sustainable value view of SMSs is put forward.•A new SMSs-SVN is proposed to illustrate sustainable relationship between multi-stakeholder and SMSs.•Integrated fuzzy ...Kano, DSM and simple graph theory is applied to get SMSs-R list.•Showed the comprehensive method’s feasibility, accuracy and potentials with an Air Conditioning Compressor-SMSs.
Sustainable development is of great significance to all Smart Manufacturing Systems (SMSs). Complex multi-stakeholder requirements influence the design and implementation of SMSs. Capturing multi-stakeholder requirements of SMSs are very important to manufacturer for construction or business process reengineering (BPR) phase of SMSs. However, there is a lack of the research on SMSs requirements (SMSs-R) list from the sustainable value view of multi-stakeholder and a lack of illustrating the complex relationship between SMSs and multi-stakeholder. Therefore, this study provided a systematic method to capture the SMSs-R list and explain the complex relationship between the SMSs and their multi-stakeholder. The contribution of this research study offers the comprehensive approach to capture SMSs-R based on stakeholder salience model and stakeholder value network. Secondly, the quantification analysis of SMS-R is proposed to determine the urgency and importance through the comprehensive fuzzy Kano model. Thirdly, SMSs-R list is obtained through the systematic evaluation methods, including graph theory, dependency relationship matrix, and network statistical. Finally, a case study in Chinese company is used to check the feasibility of the proposed approach. The results show that our method can provide a visual and effective analysis for traditional manufacturing enterprises, and improve the operability and accuracy of SMSs-R list in the preliminary design or BPR stage.
One of the significant trends in smart manufacturing is the idea of industrial digitalization, which is enabled through the use of new information technologies, such as the Internet of Things, big ...data, cloud computing, and artificial intelligence. However, manufacturing industries can only be achieved by combining the physical manufacturing world and digital world, to realize a series of smart manufacturing activities, such as active perception, real-time interaction, automatic processing, intelligent control, and real-time optimization, etc. In this paper, a digital twin-driven approach combines with agent-based decision-making for real-time optimization of motion planning in robotic cellular is proposed, with optimizing the physical and virtual layer at the manufacturing facility. Accordingly, an architecture of the digital twin-driven facility is design, and its operational mechanisms and implementation methods are explained in detail. Moreover, qualitative analysis and a quantitative comparison based on a real robotic cell are provided. Several key findings and observations are generated relating to managerial implications, which are valuable for various users to make manufacturing decisions under the digital twin-driven environment.
The fourth industrial revolution and the underlying digital transformation, known as Industry 4.0, is progressing exponentially. The digital revolution is reshaping the way individuals live and work ...fundamentally, and the public remains optimistic regarding the opportunities Industry 4.0 may offer for sustainability. The present study contributes to the sustainability literature by systematically identifying the sustainability functions of Industry 4.0. In doing so, the study first reviews the fundamental design principles and technology trends of Industry 4.0 and introduces the architectural design of Industry 4.0. The study further draws on the interpretive structural modelling technique to model the contextual relationships among the Industry 4.0 sustainability functions. Results indicate that sophisticated precedence relationships exist among various sustainability functions of Industry 4.0. ‘Matrice d’Impacts Croisés Multiplication Appliquée àun Classement’ (MICMAC) analysis reveals that economic sustainability functions such as production efficiency and business model innovation tend to be the more immediate outcome of Industry 4.0, which pays the way for development of more remote socioenvironmental sustainability functions of Industry 4.0 such as energy sustainability, harmful emission reduction, and social welfare improvement. This study can serve Industry 4.0 stakeholders – leaders in the public and private sectors, industrialists, and academicians – to better understand the opportunities that the digital revolution may offer for sustainability, and work together more closely to ensure that Industry 4.0 delivers the intended sustainability functions around the world as effectively, equally, and fairly as possible.
•Principles, technologies, and architectural design of Industry 4.0 were described.•Critical sustainability functions of Industry 4.0 were identified and described.•Interrelationships among Industry 4.0 sustainability functions were modelled.•The dependence-driver power Industry 4.0 sustainability functions were analysed.
Data-driven smart manufacturing Tao, Fei; Qi, Qinglin; Liu, Ang ...
Journal of manufacturing systems,
07/2018, Letnik:
48
Journal Article
Recenzirano
•The evolution of manufacturing data was reflected in accordance with four ages.•The lifecycle of manufacturing big data was illustrated as a series of phases.•A framework of data driven smart ...manufacturing is proposed, and the characteristics are discussed.•Several application scenarios of the proposed framework are outlined.•A case is given out to illustrate the implementation of the proposed framework.
The advances in the internet technology, internet of things, cloud computing, big data, and artificial intelligence have profoundly impacted manufacturing. The volume of data collected in manufacturing is growing. Big data offers a tremendous opportunity in the transformation of today’s manufacturing paradigm to smart manufacturing. Big data empowers companies to adopt data-driven strategies to become more competitive. In this paper, the role of big data in supporting smart manufacturing is discussed. A historical perspective to data lifecycle in manufacturing is overviewed. The big data perspective is supported by a conceptual framework proposed in the paper. Typical application scenarios of the proposed framework are outlined.
Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. ...First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning, deep learning techniques automatically generate features, which saves feature engineering costs, but in return may require larger amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research.
Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity ...of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area.
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