Our next generation of industry-lndustry 4.0-holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope wit...Our next generation of industry-lndustry 4.0-holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things （IoT）- enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the loT, cyber-physical systems （CPSs）, cloud computing, big data analytics （BDA）, and information and communications technology （ICT） that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.展开更多
The advancement of intelligent manufacturing in Dongguan puts forward new requirements for industrial talents,and the development of new productivity is bound to force enterprises and employees to make adaptive adjust...The advancement of intelligent manufacturing in Dongguan puts forward new requirements for industrial talents,and the development of new productivity is bound to force enterprises and employees to make adaptive adjustments.The upgrading of intelligent manufacturing is not only the upgrading of intelligent machines but also the upgrading of the human brain,which includes the reshaping and cultivation of industrial talents.Based on field research,this study analyzes the different characteristics of the traditional and the new intelligent manufacturing model,as well as summarizes the characteristics of industrial talents and the changing trend of talent demand in view of the intelligent manufacturing model in Dongguan.展开更多
The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world.However,different terminologies,namely smart manufacturing(SM)and intelligent manufact...The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world.However,different terminologies,namely smart manufacturing(SM)and intelligent manufacturing(IM),have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners.While SM and IM are similar,they are not identical.From an evolutionary perspective,there has been little consideration on whether the definition,thought,connotation,and technical development of the concepts of SM or IM are consistent in the literature.To address this gap,the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM.A bibliometric analysis of publication sources,annual publication numbers,keyword frequency,and top regions of research and development establishes the scope and trends of the currently presented research.Critical topics discussed include origin,definitions,evolutionary path,and key technologies of SM and IM.The implementation architecture,standards,and national focus are also discussed.In this work,a basis to understand SM and IM is provided,which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.展开更多
<span style="font-family:Verdana;">The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process...<span style="font-family:Verdana;">The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process of the virus imply to increase health security (patient and personal health). In this context, healthcare logistics flows require a new and safety organization improving the hospital performance. The purpose of this paper consists in optimizing healthcare logistics flows by solving problems associated to the internal logistics such as reduction of the personal health wasting time and the protection of both patients and personal health. Then, the methodology corresponds to the use of the hospital sustainable digital transformation as a response to healthcare flows and safety problems. Indeed, social, societal and environmental aspects have to be considered in addition to new technologies such as artificial intelligence (AI), Internet of Things (IoTs), Big data and analytics. These parameters could be used in the healthcare for increasing doctor, nurse, caregiver performance during their daily operations, and patient satisfaction. Indeed, this hospital digital transformation requires the use of large data associated to patients and personal health, algorithms, a performance measurement tool (actual and future state) and a general approach for transforming digitally the hospital flows. The paper findings show that the healthcare logistics performance could be improved with a sustainable digital transformation methodology and an intelligent software tool. This paper aims to develop this healthcare logistics 4.0 methodology and to elaborate the intelligent support system. After an introduction presenting the common hospital flows and their main problems, a literature review will be detailed for showing how existing concepts could contribute to the elaboration of a structured methodology. The structure of the intelligent software tool for the healthcare digital transformation and the tool development processes will be presented. An example will be given for illustrating the development of the tool.</span>展开更多
Many articles have been published on intelligent manufacturing, most of which focus on hardware, soft-ware, additive manufacturing, robotics, the Internet of Things, and Industry 4.0. This paper provides a dif-ferent ...Many articles have been published on intelligent manufacturing, most of which focus on hardware, soft-ware, additive manufacturing, robotics, the Internet of Things, and Industry 4.0. This paper provides a dif-ferent perspective by examining relevant challenges and providing examples of some less-talked-about yet essential topics, such as hybrid systems, redefining advanced manufacturing, basic building blocks of new manufacturing, ecosystem readiness, and technology scalahility. The first major challenge is to （re-）define what the manufacturing of the future will he, if we wish to： ① raise public awareness of new manufacturing＇s economic and societal impacts, and ② garner the unequivocal support of policy- makers. The second major challenge is to recognize that manufacturing in the future will consist of sys-tems of hybrid systems of human and robotic operators; additive and suhtractive processes; metal and composite materials; and cyher and physical systems. Therefore, studying the interfaces between con- stituencies and standards becomes important and essential. The third challenge is to develop a common framework in which the technology, manufacturing business case, and ecosystem readiness can he eval- uated concurrently in order to shorten the time it takes for products to reach customers. Integral to this is having accepted measures of ＂scalahility＂ of non-information technologies. The last, hut not least, chal-lenge is to examine successful modalities of industry-academia-government collaborations through public-private partnerships. This article discusses these challenges in detail.展开更多
Industry 4.0 concepts have brought about a wind of renewal in the organization of companies and their production methods. However, this integration is subject to obstacles when it comes to Small and Medium sized Enter...Industry 4.0 concepts have brought about a wind of renewal in the organization of companies and their production methods. However, this integration is subject to obstacles when it comes to Small and Medium sized Enterprises—SMEs: the costs of new technologies to be acquired, the level of maturity of the company regarding its level of digitization and automation, human aspects such as training employees to master new technologies, reluctance to change, etc. This article provides a new framework and presents an intelligent support system to facilitate the digital transformation of SMEs. The digitalization is realized through physical, informational, and decisional points of view. To achieve the complete transformation of the company, the framework combines the triptych of performance criteria (cost, quality, time) with the notions of sustainability (with respect to social, societal, and environmental aspects) and digitization through tools to be integrated into the company’s processes. The new framework encompasses the formalisms developed in the literature on Industry 4.0 concepts, information systems and organizational methods as well as a global structure to support and assist operators in managing their operations. In the form of a web application, it will exploit reliable data obtained through information systems such as Enterprise Resources Planning—ERP, Manufacturing Execution System—MES, or Warehouse Management System—WMS and new technologies such as artificial intelligence (deep learning, multi-agent systems, expert systems), big data, Internet of things (IoT) that communicate with each other to assist operators during production processes. To illustrate and validate the concepts and developed tools, use cases of an electronic manufacturing SME have been solved with these concepts and tools, in order to succeed in this company’s digital transformation. Thus, a reference model of the electronics manufacturing companies is being developed for facilitating the future digital transformation of these domain companies. The realization of these use cases and the new reference model are growing up and their future exploitation will be presented as soon as possible.展开更多
Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advan...Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation.In this study,we propose a new model to measure the intelligent manufacturing readiness for the process industry,which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation.Although some models have already been reported to measure Industry 4.0 readiness and maturity,there are no models that are aimed at the process industry.This newly proposed model has six levels to describe different development stages for intelligent manufacturing.In addition,the model consists of four races,nine species,and 25 domains that are relevant to the essential businesses of companies’daily operation and capability requirements of intelligent manufacturing.Furthermore,these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail.A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis.Using the new method,a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model.Overall,the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies.展开更多
文摘Our next generation of industry-lndustry 4.0-holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things （IoT）- enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the loT, cyber-physical systems （CPSs）, cloud computing, big data analytics （BDA）, and information and communications technology （ICT） that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.
文摘The advancement of intelligent manufacturing in Dongguan puts forward new requirements for industrial talents,and the development of new productivity is bound to force enterprises and employees to make adaptive adjustments.The upgrading of intelligent manufacturing is not only the upgrading of intelligent machines but also the upgrading of the human brain,which includes the reshaping and cultivation of industrial talents.Based on field research,this study analyzes the different characteristics of the traditional and the new intelligent manufacturing model,as well as summarizes the characteristics of industrial talents and the changing trend of talent demand in view of the intelligent manufacturing model in Dongguan.
基金supported by the International Postdoctoral Exchange Fellowship Program(20180025)National Natural Science Foundation of China(51703180)+2 种基金China Postdoctoral Science Foundation(2018M630191,2017M610634)Shaanxi Postdoctoral Science Foundation(2017BSHEDZZ73)Fundamental Research Funds for the Central Universities(xpt012020006,xjj2017024).
文摘The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world.However,different terminologies,namely smart manufacturing(SM)and intelligent manufacturing(IM),have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners.While SM and IM are similar,they are not identical.From an evolutionary perspective,there has been little consideration on whether the definition,thought,connotation,and technical development of the concepts of SM or IM are consistent in the literature.To address this gap,the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM.A bibliometric analysis of publication sources,annual publication numbers,keyword frequency,and top regions of research and development establishes the scope and trends of the currently presented research.Critical topics discussed include origin,definitions,evolutionary path,and key technologies of SM and IM.The implementation architecture,standards,and national focus are also discussed.In this work,a basis to understand SM and IM is provided,which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.
文摘<span style="font-family:Verdana;">The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process of the virus imply to increase health security (patient and personal health). In this context, healthcare logistics flows require a new and safety organization improving the hospital performance. The purpose of this paper consists in optimizing healthcare logistics flows by solving problems associated to the internal logistics such as reduction of the personal health wasting time and the protection of both patients and personal health. Then, the methodology corresponds to the use of the hospital sustainable digital transformation as a response to healthcare flows and safety problems. Indeed, social, societal and environmental aspects have to be considered in addition to new technologies such as artificial intelligence (AI), Internet of Things (IoTs), Big data and analytics. These parameters could be used in the healthcare for increasing doctor, nurse, caregiver performance during their daily operations, and patient satisfaction. Indeed, this hospital digital transformation requires the use of large data associated to patients and personal health, algorithms, a performance measurement tool (actual and future state) and a general approach for transforming digitally the hospital flows. The paper findings show that the healthcare logistics performance could be improved with a sustainable digital transformation methodology and an intelligent software tool. This paper aims to develop this healthcare logistics 4.0 methodology and to elaborate the intelligent support system. After an introduction presenting the common hospital flows and their main problems, a literature review will be detailed for showing how existing concepts could contribute to the elaboration of a structured methodology. The structure of the intelligent software tool for the healthcare digital transformation and the tool development processes will be presented. An example will be given for illustrating the development of the tool.</span>
文摘Many articles have been published on intelligent manufacturing, most of which focus on hardware, soft-ware, additive manufacturing, robotics, the Internet of Things, and Industry 4.0. This paper provides a dif-ferent perspective by examining relevant challenges and providing examples of some less-talked-about yet essential topics, such as hybrid systems, redefining advanced manufacturing, basic building blocks of new manufacturing, ecosystem readiness, and technology scalahility. The first major challenge is to （re-）define what the manufacturing of the future will he, if we wish to： ① raise public awareness of new manufacturing＇s economic and societal impacts, and ② garner the unequivocal support of policy- makers. The second major challenge is to recognize that manufacturing in the future will consist of sys-tems of hybrid systems of human and robotic operators; additive and suhtractive processes; metal and composite materials; and cyher and physical systems. Therefore, studying the interfaces between con- stituencies and standards becomes important and essential. The third challenge is to develop a common framework in which the technology, manufacturing business case, and ecosystem readiness can he eval- uated concurrently in order to shorten the time it takes for products to reach customers. Integral to this is having accepted measures of ＂scalahility＂ of non-information technologies. The last, hut not least, chal-lenge is to examine successful modalities of industry-academia-government collaborations through public-private partnerships. This article discusses these challenges in detail.
文摘Industry 4.0 concepts have brought about a wind of renewal in the organization of companies and their production methods. However, this integration is subject to obstacles when it comes to Small and Medium sized Enterprises—SMEs: the costs of new technologies to be acquired, the level of maturity of the company regarding its level of digitization and automation, human aspects such as training employees to master new technologies, reluctance to change, etc. This article provides a new framework and presents an intelligent support system to facilitate the digital transformation of SMEs. The digitalization is realized through physical, informational, and decisional points of view. To achieve the complete transformation of the company, the framework combines the triptych of performance criteria (cost, quality, time) with the notions of sustainability (with respect to social, societal, and environmental aspects) and digitization through tools to be integrated into the company’s processes. The new framework encompasses the formalisms developed in the literature on Industry 4.0 concepts, information systems and organizational methods as well as a global structure to support and assist operators in managing their operations. In the form of a web application, it will exploit reliable data obtained through information systems such as Enterprise Resources Planning—ERP, Manufacturing Execution System—MES, or Warehouse Management System—WMS and new technologies such as artificial intelligence (deep learning, multi-agent systems, expert systems), big data, Internet of things (IoT) that communicate with each other to assist operators during production processes. To illustrate and validate the concepts and developed tools, use cases of an electronic manufacturing SME have been solved with these concepts and tools, in order to succeed in this company’s digital transformation. Thus, a reference model of the electronics manufacturing companies is being developed for facilitating the future digital transformation of these domain companies. The realization of these use cases and the new reference model are growing up and their future exploitation will be presented as soon as possible.
基金Project supported by the National Key Research and Development Program of China(No.2019YFB1705004)。
文摘Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation.In this study,we propose a new model to measure the intelligent manufacturing readiness for the process industry,which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation.Although some models have already been reported to measure Industry 4.0 readiness and maturity,there are no models that are aimed at the process industry.This newly proposed model has six levels to describe different development stages for intelligent manufacturing.In addition,the model consists of four races,nine species,and 25 domains that are relevant to the essential businesses of companies’daily operation and capability requirements of intelligent manufacturing.Furthermore,these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail.A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis.Using the new method,a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model.Overall,the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies.
