Pub Date : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309919
Jiage Huo, K. L. Keung, C. K. M. Lee, K. Ng, K. C. Li
Nowadays, Hong Kong International Airport faces the issues of saturation and overload. The difficulties of selecting taxiways and reducing the lead time at the runway holding position are the severe consequences that appeared from increasing the number of passengers and increased cargo movement to Hong Kong International Airport but without constructing a new runway. This paper is primarily about predicting flight delays by using machine learning methodologies. The prediction results of several machine learning approaches are compared and analyzed thoroughly by using real data from the Hong Kong International Airport. The findings and recommendations from this paper are valuable to the aviation and insurance industries. Better planning of the airport system can be established through predicting flight delays.
{"title":"The Prediction of Flight Delay: Big Data-driven Machine Learning Approach","authors":"Jiage Huo, K. L. Keung, C. K. M. Lee, K. Ng, K. C. Li","doi":"10.1109/IEEM45057.2020.9309919","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309919","url":null,"abstract":"Nowadays, Hong Kong International Airport faces the issues of saturation and overload. The difficulties of selecting taxiways and reducing the lead time at the runway holding position are the severe consequences that appeared from increasing the number of passengers and increased cargo movement to Hong Kong International Airport but without constructing a new runway. This paper is primarily about predicting flight delays by using machine learning methodologies. The prediction results of several machine learning approaches are compared and analyzed thoroughly by using real data from the Hong Kong International Airport. The findings and recommendations from this paper are valuable to the aviation and insurance industries. Better planning of the airport system can be established through predicting flight delays.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122874072","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309941
G. Hass, Parker Simon, R. Kashef
"The world's most valuable resource is no longer oil, but data" announces the headline of the May 6th, 2017 edition of The Economist; the digital revolution is here to stay. The primary currency of this movement is big data. The complexity of big data is defined as the relationships and how the data can be arranged with one another. Facebook has 30 billion pieces of unique information shared each month; this data's sheer size can cause an immeasurable amount of combinations for relational data. Analyzing this big data can reveal various useful insights for decision-makers. With the adoption of clustering analysis, patterns and hidden information can be developed from big raw data that can be used across many business problems and applications. In this paper, an overview of the state of the art of clustering analysis and its adoption in business applications in the era of big data is presented.
{"title":"Business Applications for Current Developments in Big Data Clustering: An Overview","authors":"G. Hass, Parker Simon, R. Kashef","doi":"10.1109/IEEM45057.2020.9309941","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309941","url":null,"abstract":"\"The world's most valuable resource is no longer oil, but data\" announces the headline of the May 6th, 2017 edition of The Economist; the digital revolution is here to stay. The primary currency of this movement is big data. The complexity of big data is defined as the relationships and how the data can be arranged with one another. Facebook has 30 billion pieces of unique information shared each month; this data's sheer size can cause an immeasurable amount of combinations for relational data. Analyzing this big data can reveal various useful insights for decision-makers. With the adoption of clustering analysis, patterns and hidden information can be developed from big raw data that can be used across many business problems and applications. In this paper, an overview of the state of the art of clustering analysis and its adoption in business applications in the era of big data is presented.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131266149","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309745
Matteo Perno, L. Hvam, Anders Haug
Since its first introduction in 2002, the interest in the concept of "Digital Twins" has grown exponentially among researchers and industry practitioners. An increasing number of Digital Twin implementations are made in many industries. Given the novelty of the concept, companies from any industry type face significant challenges when implementing Digital Twins. Furthermore, only little research has been conducted in the process industry, which may be explained by the high complexity of representing and modeling the physics behind the production processes in an accurate manner. This study aims at filling this gap by providing a clear categorization of the main barriers that process companies face when implementing Digital Twins of their assets, as well as the key enabling factors and technologies that can be leveraged to overcome such challenges. Furthermore, a model based on the findings from the literature study is proposed. The results indicate a dearth in the literature focused on the process industry, therefore, key learnings from other industry sectors are gathered, and suggestions for further research are proposed.
{"title":"Enablers and Barriers to the Implementation of Digital Twins in the Process Industry: A Systematic Literature Review","authors":"Matteo Perno, L. Hvam, Anders Haug","doi":"10.1109/IEEM45057.2020.9309745","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309745","url":null,"abstract":"Since its first introduction in 2002, the interest in the concept of \"Digital Twins\" has grown exponentially among researchers and industry practitioners. An increasing number of Digital Twin implementations are made in many industries. Given the novelty of the concept, companies from any industry type face significant challenges when implementing Digital Twins. Furthermore, only little research has been conducted in the process industry, which may be explained by the high complexity of representing and modeling the physics behind the production processes in an accurate manner. This study aims at filling this gap by providing a clear categorization of the main barriers that process companies face when implementing Digital Twins of their assets, as well as the key enabling factors and technologies that can be leveraged to overcome such challenges. Furthermore, a model based on the findings from the literature study is proposed. The results indicate a dearth in the literature focused on the process industry, therefore, key learnings from other industry sectors are gathered, and suggestions for further research are proposed.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121276466","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309981
Xiaonan Wang, P. Guo, Ding Wang
An evolutionary game model of knowledge transfer in inter-organizational R&D projects was established, and its local stability was analyzed. Then, the complex network and preference theory are introduced to establish the game model of knowledge transfer in the cooperation network of inter-organizational R&D projects under the condition of preference differences and different network structures. Finally, the influence of key factors, preference difference and network structures on strategy selection is analyzed. The results show that the cost coefficient has a negative correlation with the level of knowledge transfer, while the increase of other coefficients promote knowledge transfer behavior. The increase of altruistic preference degree and the proportion of altruistic preference agents can promote knowledge transfer behavior, while the increase of competitive preference degree and the proportion of competitive preference agents can inhibit knowledge transfer behavior. Moreover, the level of knowledge transfer is higher in the scale-free network than in the small-world network in most cases. However, punishment plays a greater role in the small-world network.
{"title":"Effect of Network Structure and Preference Difference on Knowledge Transfer in Inter-organizational R&D Project","authors":"Xiaonan Wang, P. Guo, Ding Wang","doi":"10.1109/IEEM45057.2020.9309981","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309981","url":null,"abstract":"An evolutionary game model of knowledge transfer in inter-organizational R&D projects was established, and its local stability was analyzed. Then, the complex network and preference theory are introduced to establish the game model of knowledge transfer in the cooperation network of inter-organizational R&D projects under the condition of preference differences and different network structures. Finally, the influence of key factors, preference difference and network structures on strategy selection is analyzed. The results show that the cost coefficient has a negative correlation with the level of knowledge transfer, while the increase of other coefficients promote knowledge transfer behavior. The increase of altruistic preference degree and the proportion of altruistic preference agents can promote knowledge transfer behavior, while the increase of competitive preference degree and the proportion of competitive preference agents can inhibit knowledge transfer behavior. Moreover, the level of knowledge transfer is higher in the scale-free network than in the small-world network in most cases. However, punishment plays a greater role in the small-world network.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133954586","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309953
A. Protic, Ziyue Jin, R. Marian, K. Abd, D. Campbell, J. Chahl
With the increased demands of smarter manufacturing approaches around the world, the process of industrial digital transformation is being pushed in and by both industry and academia. Learning factories and testing laboratories have been developed for decades for teaching and training purposes in Academia. Nowadays, as the future trend in industry, Industry 4 is being merged into the latest development of learning factories and testing laboratories. This paper presents the development and implementation of a bi-directional digital twin application in an Industry 4 testing laboratory at University of South Australia. The solution is based on the establishment of OPC UA connection between two cobots of different brands, the use of NX Siemens as a CAD simulation platform and a SCADA system from Inductive Automation. Due to differences between system interfaces, communication between different modules was challenging. Python OPC UA servers were developed. The digital twin replicates the physical system and is driven by inputs from the assembly cell.
{"title":"Implementation of a Bi-Directional Digital Twin for Industry 4 Labs in Academia: A Solution Based on OPC UA","authors":"A. Protic, Ziyue Jin, R. Marian, K. Abd, D. Campbell, J. Chahl","doi":"10.1109/IEEM45057.2020.9309953","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309953","url":null,"abstract":"With the increased demands of smarter manufacturing approaches around the world, the process of industrial digital transformation is being pushed in and by both industry and academia. Learning factories and testing laboratories have been developed for decades for teaching and training purposes in Academia. Nowadays, as the future trend in industry, Industry 4 is being merged into the latest development of learning factories and testing laboratories. This paper presents the development and implementation of a bi-directional digital twin application in an Industry 4 testing laboratory at University of South Australia. The solution is based on the establishment of OPC UA connection between two cobots of different brands, the use of NX Siemens as a CAD simulation platform and a SCADA system from Inductive Automation. Due to differences between system interfaces, communication between different modules was challenging. Python OPC UA servers were developed. The digital twin replicates the physical system and is driven by inputs from the assembly cell.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133388660","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309942
Xin Zheng, Yali Zhang, Liaoliao Li, Nan Chang, Jianwu Xue
For the "COVID-19" pandemic spreading around the globe, the successful development of nucleic acid testing reagents is critical to disease prevention and control. It has also made governments and institutions aware of the importance of original innovation for disruptive technology. Based on the literature review, this paper discusses the concepts of disruptive technology and original innovation. It then extracts the core elements for original innovation by analyzing two cases of the fundamental research teams of Tsinghua University. Finally, it puts forward a model on achieving original innovation for disruptive technology to a project or team.
{"title":"Promoting the Original Innovation for Disruptive Technology","authors":"Xin Zheng, Yali Zhang, Liaoliao Li, Nan Chang, Jianwu Xue","doi":"10.1109/IEEM45057.2020.9309942","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309942","url":null,"abstract":"For the \"COVID-19\" pandemic spreading around the globe, the successful development of nucleic acid testing reagents is critical to disease prevention and control. It has also made governments and institutions aware of the importance of original innovation for disruptive technology. Based on the literature review, this paper discusses the concepts of disruptive technology and original innovation. It then extracts the core elements for original innovation by analyzing two cases of the fundamental research teams of Tsinghua University. Finally, it puts forward a model on achieving original innovation for disruptive technology to a project or team.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123007702","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309799
Janne Härkönen, Erno Mustonen, J. Koskinen, Hannu Hannila
Digitalizing business analytics by generating relevant information from data to gain valuable insights and to enhance decision-making is a key ability for companies. We describe a potential concept for digitizing company analytics to enable data-driven approach by providing a path from information needs to visualizing the analysis results, including the consideration of analysis logic, relevant data, and necessary data model. Company business processes, IT applications, and data assets provide the foundations to build upon. The developed concept may enable practitioners to consider possible applications and the needed, already existing technologies.
{"title":"Digitizing Company Analytics – Digitalization Concept for Valuable Insights","authors":"Janne Härkönen, Erno Mustonen, J. Koskinen, Hannu Hannila","doi":"10.1109/IEEM45057.2020.9309799","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309799","url":null,"abstract":"Digitalizing business analytics by generating relevant information from data to gain valuable insights and to enhance decision-making is a key ability for companies. We describe a potential concept for digitizing company analytics to enable data-driven approach by providing a path from information needs to visualizing the analysis results, including the consideration of analysis logic, relevant data, and necessary data model. Company business processes, IT applications, and data assets provide the foundations to build upon. The developed concept may enable practitioners to consider possible applications and the needed, already existing technologies.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125013384","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309865
Vaishnavy Perinparajah, H. Perera, J. Sudusinghe, Uthpalee Hewage
Organizations, suppliers and consumers are highly sensitive towards environmental impacts arising from their operations today. Under this context, green consumption and the green packaging are gaining popularity among consumers. This research evaluates young educated consumer perception towards green packaging in Sri Lanka. A survey was conducted targeting university students who are reading for an undergraduate degree in Sri Lanka. Demographical information, attitudes and beliefs, knowledge about environmental sustainability, environmental awareness, and factors influencing their consumer behavior towards green packaging were collected through the survey. The data was analyzed using the Analytical Hierarchical Process. The findings of the research help understand main factors which influence the young educated consumer behavior towards green packaging and the level of influence of each factor. The analysis also reveals the hindrances and challenges withholding the use of green packaging.
{"title":"Young Consumers’ Perception Towards Downstream Green Supply Chain Practices","authors":"Vaishnavy Perinparajah, H. Perera, J. Sudusinghe, Uthpalee Hewage","doi":"10.1109/IEEM45057.2020.9309865","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309865","url":null,"abstract":"Organizations, suppliers and consumers are highly sensitive towards environmental impacts arising from their operations today. Under this context, green consumption and the green packaging are gaining popularity among consumers. This research evaluates young educated consumer perception towards green packaging in Sri Lanka. A survey was conducted targeting university students who are reading for an undergraduate degree in Sri Lanka. Demographical information, attitudes and beliefs, knowledge about environmental sustainability, environmental awareness, and factors influencing their consumer behavior towards green packaging were collected through the survey. The data was analyzed using the Analytical Hierarchical Process. The findings of the research help understand main factors which influence the young educated consumer behavior towards green packaging and the level of influence of each factor. The analysis also reveals the hindrances and challenges withholding the use of green packaging.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125226765","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309918
Mahmoud Ershadi, Marcus Jefferies, P. Davis, M. Mojtahedi
The concept of the project management office (PMO) is well-established in academic literature. This organizational phenomenon has revolutionized practices applied by organizations toward coordinating and overseeing multiple projects throughout the design, engineering, initiation, execution, and handover stages. The construction industry is one of the contexts in which more research is still needed to provide practical guidelines for achieving effective PMO functioning. This study explores several core capabilities of these entities from the perspective of construction project management practitioners working in the contracting sector. In this regard, we solicited expert judgment based on an online questionnaire followed by thematic analysis. Respondents suggested six drivers that can contribute to improving the effectiveness of construction PMOs in practice. This study provides insight into some capabilities that can be employed for delivering high-performing PMOs.
{"title":"Breakthrough Capabilities for Delivering High-performing Project Management Offices (PMOs) in Construction Enterprises","authors":"Mahmoud Ershadi, Marcus Jefferies, P. Davis, M. Mojtahedi","doi":"10.1109/IEEM45057.2020.9309918","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309918","url":null,"abstract":"The concept of the project management office (PMO) is well-established in academic literature. This organizational phenomenon has revolutionized practices applied by organizations toward coordinating and overseeing multiple projects throughout the design, engineering, initiation, execution, and handover stages. The construction industry is one of the contexts in which more research is still needed to provide practical guidelines for achieving effective PMO functioning. This study explores several core capabilities of these entities from the perspective of construction project management practitioners working in the contracting sector. In this regard, we solicited expert judgment based on an online questionnaire followed by thematic analysis. Respondents suggested six drivers that can contribute to improving the effectiveness of construction PMOs in practice. This study provides insight into some capabilities that can be employed for delivering high-performing PMOs.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132468469","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309849
Nan Chang, Yali Zhang, Di Lu, Xin Zheng, Jianwu Xue
Most enterprises want to increase their competitiveness through the implementation of disruptive technology, but not all enterprises have achieved their initial expectations after adopting disruptive technologies. One of the most important reasons is that the enterprise itself is not well prepared for a disruptive technology in all aspects, which leads to many problems and risks in the implementation process. As the successful implementation of disruptive technology depends on enterprises’ innovativeness. The extant research focuses on its predictive identification, technical characteristics, and application directions. However, there is a lack of research on organizational preparation for implementing disruptive technologies to promote their success. Based on the technology-organization-environment (TOE) framework, this study reviews the literature to explore the readiness of enterprises adopting and implementing disruptive technologies. It extracts the factors that affect technological readiness, organizational readiness and environmental readiness respectively. Affecting an organization’s choice of adopting disruptive technologies, these three aspects of readiness ultimately determine their success.
{"title":"Is a Disruptive Technology Disruptive? The Readiness Perspective Based on TOE","authors":"Nan Chang, Yali Zhang, Di Lu, Xin Zheng, Jianwu Xue","doi":"10.1109/IEEM45057.2020.9309849","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309849","url":null,"abstract":"Most enterprises want to increase their competitiveness through the implementation of disruptive technology, but not all enterprises have achieved their initial expectations after adopting disruptive technologies. One of the most important reasons is that the enterprise itself is not well prepared for a disruptive technology in all aspects, which leads to many problems and risks in the implementation process. As the successful implementation of disruptive technology depends on enterprises’ innovativeness. The extant research focuses on its predictive identification, technical characteristics, and application directions. However, there is a lack of research on organizational preparation for implementing disruptive technologies to promote their success. Based on the technology-organization-environment (TOE) framework, this study reviews the literature to explore the readiness of enterprises adopting and implementing disruptive technologies. It extracts the factors that affect technological readiness, organizational readiness and environmental readiness respectively. Affecting an organization’s choice of adopting disruptive technologies, these three aspects of readiness ultimately determine their success.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133875414","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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