Pub Date : 2021-03-03DOI: 10.1142/S2424862221500068
I. Khan, M. Javaid
Internet of Things (IoT) is a critical component of Industry 4.0. It has extensive applications in the monitoring of production systems in manufacturing and services. This technology opens up newer and innovative possibilities in manufacturing by facilitating higher performance. IoT’s major capability is to collect and share information with the help of internet-connected machines and devices. It is associated with unique identification numbers or codes that can be controllable through our daily use devices like smartphones. This technology’s major components are software, hardware with the network’s connectivity for data altercation, and collection. IoT creates disruptive innovation in the field of manufacturing. The need is to understand this technology and how it can help the contemporary production systems. Here, we have studied the potential of this technology to provide better solutions in Industry 4.0. The major drivers of IoT for Industry 4.0 are studied. This paper discusses how Industry 4.0 helps create a smart factory. Finally, we have identified and studied significant IoT applications to adopt Industry 4.0 successfully, and the same is presented in tabular form. With proper implementation of this technology, industries observe an improvement in efficiency during the manufacturing of products. Manufacturing is done with lesser cost and errors. However, there is a long way to reap full benefits for humankind.
{"title":"Role of Internet of Things (IoT) in Adoption of Industry 4.0","authors":"I. Khan, M. Javaid","doi":"10.1142/S2424862221500068","DOIUrl":"https://doi.org/10.1142/S2424862221500068","url":null,"abstract":"Internet of Things (IoT) is a critical component of Industry 4.0. It has extensive applications in the monitoring of production systems in manufacturing and services. This technology opens up newer and innovative possibilities in manufacturing by facilitating higher performance. IoT’s major capability is to collect and share information with the help of internet-connected machines and devices. It is associated with unique identification numbers or codes that can be controllable through our daily use devices like smartphones. This technology’s major components are software, hardware with the network’s connectivity for data altercation, and collection. IoT creates disruptive innovation in the field of manufacturing. The need is to understand this technology and how it can help the contemporary production systems. Here, we have studied the potential of this technology to provide better solutions in Industry 4.0. The major drivers of IoT for Industry 4.0 are studied. This paper discusses how Industry 4.0 helps create a smart factory. Finally, we have identified and studied significant IoT applications to adopt Industry 4.0 successfully, and the same is presented in tabular form. With proper implementation of this technology, industries observe an improvement in efficiency during the manufacturing of products. Manufacturing is done with lesser cost and errors. However, there is a long way to reap full benefits for humankind.","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":null,"pages":null},"PeriodicalIF":9.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82307358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1142/s2424862220500086
Nataša Rupčić, T. Majić, J. Stjepandić
Innovation of products and services is a key success factor for the manufacturing industry. Development of upgraded and innovative products and services usually takes place in partnerships. In this regard, in the manufacturing industry, original equipment manufacturers (OEMs) distribute the development of new and innovative products to many different locations in various countries across the world. Suppliers, especially first-tier suppliers (FTSs), are then involved in the development of new products and services based on strategic firm-specific competences. The OEM–supplier relationship is characterized by a sequential interaction whereby OEMs specify product and production requirements to the suppliers and the suppliers then produce and deliver products or services to OEMs. Tight collaboration between FTSs and OEMs is essential to reduce costs and risks, anticipate potential downstream errors and enable fruitful implementation of FTSs’ knowledge and competencies. The paper presents recent developments in the OEM–FTS supply chain management with particular emphasis on the design of shared platforms which emerge as ecosystems on the basis of the joint organizational learning and stakeholder management. Development of such ecosystems could be viewed as a transdisciplinary process, combining expertise from various fields ranging from engineering, especially IT engineering, to management, especially supply chain, strategic and knowledge management.
{"title":"Emergence of Business Ecosystems by Transformation of Platforms Through the Process of Organizational Learning","authors":"Nataša Rupčić, T. Majić, J. Stjepandić","doi":"10.1142/s2424862220500086","DOIUrl":"https://doi.org/10.1142/s2424862220500086","url":null,"abstract":"Innovation of products and services is a key success factor for the manufacturing industry. Development of upgraded and innovative products and services usually takes place in partnerships. In this regard, in the manufacturing industry, original equipment manufacturers (OEMs) distribute the development of new and innovative products to many different locations in various countries across the world. Suppliers, especially first-tier suppliers (FTSs), are then involved in the development of new products and services based on strategic firm-specific competences. The OEM–supplier relationship is characterized by a sequential interaction whereby OEMs specify product and production requirements to the suppliers and the suppliers then produce and deliver products or services to OEMs. Tight collaboration between FTSs and OEMs is essential to reduce costs and risks, anticipate potential downstream errors and enable fruitful implementation of FTSs’ knowledge and competencies. The paper presents recent developments in the OEM–FTS supply chain management with particular emphasis on the design of shared platforms which emerge as ecosystems on the basis of the joint organizational learning and stakeholder management. Development of such ecosystems could be viewed as a transdisciplinary process, combining expertise from various fields ranging from engineering, especially IT engineering, to management, especially supply chain, strategic and knowledge management.","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":null,"pages":null},"PeriodicalIF":9.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82890317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1142/s2424862220500025
L. Mingxing, B. A. Asunka, Su Jialu, Hu Cheng, Wenqin Ming, Wu Yuxiao
Based on the perspective of collaborative innovation, this study employed the social network analysis method to empirically analyze the evolution of knowledge diffusion networks between research institutions and industry in China. The study is centered on the Jiangsu University of China, an institution noted for its innovative activities for a long time. The results show that since 2008 the extent and depth of explicit knowledge diffusion of Jiangsu University have increased significantly. The regional knowledge diffusion network presents a core-edge spatial pattern, but the core-edge structure is weakening; indicating that while the number of partners in the collaboration effort is increasing, the strength of ties between groups is weakening. The tacit knowledge diffusion network presents a small-world effect; small groups of partners clustered around one or a few influential inventors have gradually emerged. It is also evident that enterprises that are located close to the university benefit more from such collaboration than those located in farther provinces. We recommend that universities in such collaborations should reach out to diverse industry players using their specialized expertise.
{"title":"Sustaining Corporate Innovation Through University–Industry Collaborative Research: Evidence from the Jiangsu University of China","authors":"L. Mingxing, B. A. Asunka, Su Jialu, Hu Cheng, Wenqin Ming, Wu Yuxiao","doi":"10.1142/s2424862220500025","DOIUrl":"https://doi.org/10.1142/s2424862220500025","url":null,"abstract":"Based on the perspective of collaborative innovation, this study employed the social network analysis method to empirically analyze the evolution of knowledge diffusion networks between research institutions and industry in China. The study is centered on the Jiangsu University of China, an institution noted for its innovative activities for a long time. The results show that since 2008 the extent and depth of explicit knowledge diffusion of Jiangsu University have increased significantly. The regional knowledge diffusion network presents a core-edge spatial pattern, but the core-edge structure is weakening; indicating that while the number of partners in the collaboration effort is increasing, the strength of ties between groups is weakening. The tacit knowledge diffusion network presents a small-world effect; small groups of partners clustered around one or a few influential inventors have gradually emerged. It is also evident that enterprises that are located close to the university benefit more from such collaboration than those located in farther provinces. We recommend that universities in such collaborations should reach out to diverse industry players using their specialized expertise.","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":null,"pages":null},"PeriodicalIF":9.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78774555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1142/s2424862220500049
Caiming Zhang
Based on the model of Cobb–Douglas (CD) production function, this paper establishes a panel data regression model, and divides all provinces of China except Hong Kong Special Administrative Region, Macao Special Administrative Region and Taiwan Province into four groups according to their levels of logistics development. With the panel data of these four groups for 20 years (1997–2016), the paper analyzes the relationship between the logistics development and the economic growth in various regions in China. In this model, this paper selects the logistics capacity, the logistics investment and the logistics manpower as the three input indicators which represent the logistics development of different regions, and selects the regional GDP as the output indicator which represents economic growth. After empirical analysis, this paper finds that there is a positive correlation between logistics and economic growth, and the impact of each indicator on the regional economic growth is different. There are three conclusions in this paper. Firstly, logistics capacity has the greatest economical promotion effect on areas with advanced logistics. The second is that logistics investment has the greatest economical promotion effect on the developed and general areas of logistics development. And the third is that the economical promotion of the logistics investment and the logistics capacity is more obvious in the underdeveloped areas of logistics.
{"title":"Research on the Economical Influence of the Difference of Regional Logistics Developing Level in China","authors":"Caiming Zhang","doi":"10.1142/s2424862220500049","DOIUrl":"https://doi.org/10.1142/s2424862220500049","url":null,"abstract":"Based on the model of Cobb–Douglas (CD) production function, this paper establishes a panel data regression model, and divides all provinces of China except Hong Kong Special Administrative Region, Macao Special Administrative Region and Taiwan Province into four groups according to their levels of logistics development. With the panel data of these four groups for 20 years (1997–2016), the paper analyzes the relationship between the logistics development and the economic growth in various regions in China. In this model, this paper selects the logistics capacity, the logistics investment and the logistics manpower as the three input indicators which represent the logistics development of different regions, and selects the regional GDP as the output indicator which represents economic growth. After empirical analysis, this paper finds that there is a positive correlation between logistics and economic growth, and the impact of each indicator on the regional economic growth is different. There are three conclusions in this paper. Firstly, logistics capacity has the greatest economical promotion effect on areas with advanced logistics. The second is that logistics investment has the greatest economical promotion effect on the developed and general areas of logistics development. And the third is that the economical promotion of the logistics investment and the logistics capacity is more obvious in the underdeveloped areas of logistics.","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":null,"pages":null},"PeriodicalIF":9.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84205967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-29DOI: 10.1142/s2424862220500050
Shahbaz Khan, Abid Haleem
There is a growing concern among consumers for preserving the environment without compromising the current lifestyle. In this context, organizations are looking for a sustainable business model that can fulfil their business objectives effectively. The concept of the circular economy is emerging as a solution that can help the minimization of the ill effect of industrialization. The circular economy is efficiently adopted and implemented through circular practices. However, the adoption of circular economy practices is not easy for the industries, and it requires a comprehensive strategy. Therefore, this study aims to identify and evaluate the key strategies to accomplish circular economy practices in the present-day context. In order to fulfil the research objective, this study identified 11 significant strategies that are needed for the adoption of circular economy practices. These strategies are identified using an integrated approach of the literature review and focus group discussion with experts. Further, these key strategies are evaluated using the fuzzy DEMATEL and developed a causal relationship among the strategies. The applied method has also classified the strategies into cause and effect group. The cause group comprises the five strategies, while effect group contain the six strategies. The finding suggests that “management involvement, support and commitment” and “creating a vision and goals for circular economy” are prominent strategies. This study will facilitate the managers to make the action plan for implementing the circular practices in order to shift towards the circular economy.
{"title":"Strategies to Implement Circular Economy Practices: A Fuzzy DEMATEL Approach","authors":"Shahbaz Khan, Abid Haleem","doi":"10.1142/s2424862220500050","DOIUrl":"https://doi.org/10.1142/s2424862220500050","url":null,"abstract":"There is a growing concern among consumers for preserving the environment without compromising the current lifestyle. In this context, organizations are looking for a sustainable business model that can fulfil their business objectives effectively. The concept of the circular economy is emerging as a solution that can help the minimization of the ill effect of industrialization. The circular economy is efficiently adopted and implemented through circular practices. However, the adoption of circular economy practices is not easy for the industries, and it requires a comprehensive strategy. Therefore, this study aims to identify and evaluate the key strategies to accomplish circular economy practices in the present-day context. In order to fulfil the research objective, this study identified 11 significant strategies that are needed for the adoption of circular economy practices. These strategies are identified using an integrated approach of the literature review and focus group discussion with experts. Further, these key strategies are evaluated using the fuzzy DEMATEL and developed a causal relationship among the strategies. The applied method has also classified the strategies into cause and effect group. The cause group comprises the five strategies, while effect group contain the six strategies. The finding suggests that “management involvement, support and commitment” and “creating a vision and goals for circular economy” are prominent strategies. This study will facilitate the managers to make the action plan for implementing the circular practices in order to shift towards the circular economy.","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":null,"pages":null},"PeriodicalIF":9.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72766943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-29DOI: 10.1142/s2424862220500062
Jin Li, Xiaobo Xu
With the rapid development of the Internet and big data, social self-media such as Weibo, WeChat, Tiktok, and QQ space has directly contributed to the speed and the channel of employees’ public opinions. The convergence and the impact of emotion are getting faster and faster. Timely and effective understanding and grasping the changes and development of employees’ public opinion has become an important topic. The development of big data technology makes it possible and necessary to construct an employees’ public opinion system. Based on big data and theories of public opinion generation and dissemination, this paper built a big data-based employees’ public opinion system and its main functions, including big data collection, data processing, data storage, data analysis, data visualization, and early warning of employees’ public opinion. Finally, we also defined the rich connotation of employees’ public opinion index.
{"title":"A Study of Big Data-Based Employees’ Public Opinion System Construction","authors":"Jin Li, Xiaobo Xu","doi":"10.1142/s2424862220500062","DOIUrl":"https://doi.org/10.1142/s2424862220500062","url":null,"abstract":"With the rapid development of the Internet and big data, social self-media such as Weibo, WeChat, Tiktok, and QQ space has directly contributed to the speed and the channel of employees’ public opinions. The convergence and the impact of emotion are getting faster and faster. Timely and effective understanding and grasping the changes and development of employees’ public opinion has become an important topic. The development of big data technology makes it possible and necessary to construct an employees’ public opinion system. Based on big data and theories of public opinion generation and dissemination, this paper built a big data-based employees’ public opinion system and its main functions, including big data collection, data processing, data storage, data analysis, data visualization, and early warning of employees’ public opinion. Finally, we also defined the rich connotation of employees’ public opinion index.","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":null,"pages":null},"PeriodicalIF":9.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79799863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-16DOI: 10.1142/s2424862219500180
Xuemei Li
This paper attempts to identify the value and the use of business analytics (BA) in E-commerce, and challenges and trends associated with BA in E-commerce. It systematically examines the literature about BA, with a particular focus on E-commerce. BA has critical value in E-commerce. BA has been used in customer analysis and website usage analysis. The most important factor for the value of E-commerce BA is customer. This paper also suggests an E-commerce BA research model which describes the BA iterative process from data to analysis, to decision, to estimation, and separates BA analysis results as functional-level and competitive-level results. Challenges identified in this paper hold theoretical and practical implications. Future studies could seek an enhanced understanding of BA through quantitative analysis.
{"title":"Business Analytics in E-Commerce: A Literature Review","authors":"Xuemei Li","doi":"10.1142/s2424862219500180","DOIUrl":"https://doi.org/10.1142/s2424862219500180","url":null,"abstract":"This paper attempts to identify the value and the use of business analytics (BA) in E-commerce, and challenges and trends associated with BA in E-commerce. It systematically examines the literature about BA, with a particular focus on E-commerce. BA has critical value in E-commerce. BA has been used in customer analysis and website usage analysis. The most important factor for the value of E-commerce BA is customer. This paper also suggests an E-commerce BA research model which describes the BA iterative process from data to analysis, to decision, to estimation, and separates BA analysis results as functional-level and competitive-level results. Challenges identified in this paper hold theoretical and practical implications. Future studies could seek an enhanced understanding of BA through quantitative analysis.","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":null,"pages":null},"PeriodicalIF":9.0,"publicationDate":"2019-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82251787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1142/s2424862219500106
A. Virk, Kawaljeet Singh
This paper considers two-dimensional non-guillotine rectangular bin packing problem with multiple objectives in which small rectangular parts are to be arranged optimally on a large rectangular sheet. The optimization of rectangular parts is attained with respect to three objectives involving maximization of (1) utilization factor, minimization of (2) due dates of rectangles and (3) number of cuts. Three nature based metaheuristic algorithms — Cuckoo Search, Bat Algorithm and Flower Pollination Algorithm — have been used to solve the multi-objective packing problem. The purpose of this work is to consider multiple industrial objectives for improving the overall production process and to explore the potential of the recent metaheuristic techniques. Benchmark test data compare the performance of recent approaches with the popular approaches and also of the different objectives used. Different performance metrics analyze the behavior/performance of the proposed technique. Experimental results obtained in this work prove the effectiveness of the recent metaheuristic techniques used. Also, it was observed that considering multiple and independent factors as objectives for the production process does not degrade the overall performance and they do not necessarily conflict with each other.
{"title":"Application of Nature Inspired Algorithms to Optimize Multi-objective Two-Dimensional Rectangle Packing Problem","authors":"A. Virk, Kawaljeet Singh","doi":"10.1142/s2424862219500106","DOIUrl":"https://doi.org/10.1142/s2424862219500106","url":null,"abstract":"This paper considers two-dimensional non-guillotine rectangular bin packing problem with multiple objectives in which small rectangular parts are to be arranged optimally on a large rectangular sheet. The optimization of rectangular parts is attained with respect to three objectives involving maximization of (1) utilization factor, minimization of (2) due dates of rectangles and (3) number of cuts. Three nature based metaheuristic algorithms — Cuckoo Search, Bat Algorithm and Flower Pollination Algorithm — have been used to solve the multi-objective packing problem. The purpose of this work is to consider multiple industrial objectives for improving the overall production process and to explore the potential of the recent metaheuristic techniques. Benchmark test data compare the performance of recent approaches with the popular approaches and also of the different objectives used. Different performance metrics analyze the behavior/performance of the proposed technique. Experimental results obtained in this work prove the effectiveness of the recent metaheuristic techniques used. Also, it was observed that considering multiple and independent factors as objectives for the production process does not degrade the overall performance and they do not necessarily conflict with each other.","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":null,"pages":null},"PeriodicalIF":9.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72679253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-12DOI: 10.1142/s2424862219500155
Jin Li
{"title":"The Construction of Monitoring and Analysis System of China Worker's Public Opinions in the Era of Big Data","authors":"Jin Li","doi":"10.1142/s2424862219500155","DOIUrl":"https://doi.org/10.1142/s2424862219500155","url":null,"abstract":"","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":null,"pages":null},"PeriodicalIF":9.0,"publicationDate":"2019-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81116472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-04DOI: 10.1142/s2424862219300011
Abid Haleem, M. Javaid
Additive manufacturing (AM) is a set of technologies and are vital to fulfilling different requirements of Industry 4.0. So, there is a need to study different additive manufacturing applications toward its achievement. From the Scopus database, different research articles on “Industry 4.0” and “additive manufacturing applications in Industry 4.0” are identified and studied through a bibliometric analysis. It shows that there is an increasing trend of publications in this new area. Industry 4.0 has entered new markets which focus on customer delight by adding values in product and services. It supports automation, interoperability, actionable insights and information transparency. There are different components vital to implement Industry 4.0 requirements. Through this extensive literature review based work, we identified different components of Industry 4.0 and explained the critical ones briefly. Finally, 13 important AM applications in Industry 4.0 are identified. The main limitation of the AM manufactured part is of comparable low strength and associated quality, coupled with a high cost of the printing machine system. In this upcoming industrial revolution, AM is a crucial technology which has become the main component of product innovation and development. This disruptive technology can fulfil different challenges in the future manufacturing system and help the industry to produce innovative products. For this futuristic manufacturing system, additive manufacturing is an upcoming paradigm, and Industry 4.0 will use its potential to achieve required goals.
{"title":"Additive Manufacturing Applications in Industry 4.0: A Review","authors":"Abid Haleem, M. Javaid","doi":"10.1142/s2424862219300011","DOIUrl":"https://doi.org/10.1142/s2424862219300011","url":null,"abstract":"Additive manufacturing (AM) is a set of technologies and are vital to fulfilling different requirements of Industry 4.0. So, there is a need to study different additive manufacturing applications toward its achievement. From the Scopus database, different research articles on “Industry 4.0” and “additive manufacturing applications in Industry 4.0” are identified and studied through a bibliometric analysis. It shows that there is an increasing trend of publications in this new area. Industry 4.0 has entered new markets which focus on customer delight by adding values in product and services. It supports automation, interoperability, actionable insights and information transparency. There are different components vital to implement Industry 4.0 requirements. Through this extensive literature review based work, we identified different components of Industry 4.0 and explained the critical ones briefly. Finally, 13 important AM applications in Industry 4.0 are identified. The main limitation of the AM manufactured part is of comparable low strength and associated quality, coupled with a high cost of the printing machine system. In this upcoming industrial revolution, AM is a crucial technology which has become the main component of product innovation and development. This disruptive technology can fulfil different challenges in the future manufacturing system and help the industry to produce innovative products. For this futuristic manufacturing system, additive manufacturing is an upcoming paradigm, and Industry 4.0 will use its potential to achieve required goals.","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":null,"pages":null},"PeriodicalIF":9.0,"publicationDate":"2019-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88698180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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