Purpose Additive manufacturing has achieved rapid development in recent years. The purpose of this paper is to visualize the intellectual landscapes of additive manufacturing and identify the hotspots and emerging trends of additive manufacturing, which can provide references for scholars, enterprises and governments to promote the development of theory and practice in the additive manufacturing field. Design/methodology/approach Science mapping is a fast-growing interdisciplinary field originated in information science and technology. Based on this methodology, guided by a computational approach, the paper visualizes the co-occurring keywords network and co-citation references network by CiteSpaceIII software to explore the hotspots and emerging trends of additive manufacturing by the following five indicators: highly cited keywords, burst keywords, clusters, landmark references and burst references. Findings “Additive manufacturing,” “3D printing,” “3D powder printing,” “consolidation phenomena,” “microstructure,” “rapid prototyping,” etc., are the main hotspots of additive manufacturing. The trends of additive manufacturing generally consist of three stages: the fundamental concepts stage from 1995 to 2000 (“rapid prototyping,” “additive manufacturing,” etc.), the approaches and techniques applications stage from 2001 to 2010 (“stereolithography,” “scaffold,” etc.), and the emerging trends stage from 2011 to the present (“stem cell”, “selective laser,” “ti-6al-4v,” etc.). The research is most abundant in 2010 and 2012. The medical field is an important hotspot of additive manufacturing. Additive manufacturing has been researched in interdiscipline. Originality/value The paper maps the perspective of additive manufacturing and explore the hotspots and emerging trends of additive manufacturing.

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Networking in Internet of Things (IoT) has had an immeasurable impact on the existing business models. In this context, exploring the hotspots and trends of business model innovation has become particularly necessary. For the topic literature over the past 20 years retrieved from Science Citation Index and Social Sciences Citation Index databases, scientometrics with information visualization technology was used to carry on the knowledge mapping with the following indicator: the co-cited reference networks, reference bursts, keyword bursts, and keyword co-occurrence networks. The results show (1) "e-commerce," "open source," "performance," "entrepreneurship," etc. are the main hotpots, and "value creation," "open innovation," "small business," "networks," etc. are the new hotpots; (2) the trends of hotspots transited from early "information technology" to later "self-service," "mass customization," and "biotechnology" and to present "cloud manufacturing," "telemedicine," "climate change," and "sustainable development,". etc.; (3) "intelligent robot," "3D printing," and the methodology of business mode innovation may be the future hotspots. This is the first paper visualizing the hotspots and emerging trends of business model innovation specially through scientometrics from a global perspective.

PurposeMore and more enterprises have realized the importance of business model innovation. However, the model tools for it are still scarce. There is a clear research gap in this academic field. Therefore, the aim of this study is to put forward a visual business model innovation model.Design/methodology/approachThe scientific literature clustering paradigm of grounded theory is used to design business model innovation theory model (BMITM). BMITM and the business model innovation options traced back from 870 labels in the grounded process are integrated into a unified framework to build the business model innovation canvas (BMIC).FindingsBMIC composed of three levels and seven modules is successfully developed. 145 business model innovation options are designed in BMIC. How to use BMIC is explained in detail. Through the analysis of innovation hotspots, the potential business model innovation directions can be found. A new business model of clothing enterprises using 3D printing is innovated with BMIC as an example.Research limitations/implicationsCompared with the previous tools, BMIC owns a clearer business model innovation framework and provides a problem-oriented business model innovation process and mechanism.Practical implicationsBMIC provides a systematic business model innovation solution set and roadmap for business model innovation practitioners.Originality/valueBMIC, a new tool for business model innovation is put forward for the first time. “Mass Selection Customization-Centralized Manufacturing” designed with BMIC for the clothing enterprises using 3D printing is put forward for the first time.

Purpose 3D printing is believed to be driving the third industrial revolution. However, a scientometric visualizing of 3D printing research and an exploration its hotspots and emerging trends are lacking. This study aims to promote the theory development of 3D printing, help researchers to determine the research direction and provide a reference for enterprises and government to plan the development of 3D printing industry by a comprehensive understanding of the hotspots and trends of 3D printing. Design/methodology/approach Based on the theory of scientometrics, 2,769 literatures on the 3D printing theme were found in the Web of Science Core Collection’ Science Citation Index Expanded (SCI-EXPANDED) index between 1995-2016. These were analyzed to explore the research hotspots and emerging trends of 3D printing with the software CiteSpaceIII. Findings Hotspots had appeared first in 1993, grew rapidly from 2005 and peaked in 2013; hotspots in the “medical field” appeared earliest and have remained extremely active; hotspots have evolved from “drug”, “printer”, “rapid prototyping” and “3D printing” in the 1990s, through “laser-induced consolidation”, “scaffolds”, “sintering” and “metal matrix composites” in the 2000s, to the current hotspots of “stereolithography”, “laser additive manufacturing”, “medical images”; “3D bioprinting”, “titanium”, “Cstem cell” and “chemical reaction” were the emerging hotspots in recent years; “Commercial operation” and “fusion with emerging technology such as big data” may create future hotspots. Research limitations/implications It is hard to avoid the possibility of missing important research results on 3D printing. The relevant records could be missing if the query phrases for topic search do not appear in records. Besides, to improve the quality of data, this study selected articles and reviews as the research objects, which may also omit some records. Originality/value First, this is the first paper visualizing the hotspots and emerging trends of 3D printing using scientometric tools. Second, not only “burst reference” and “burst keywords” but also “cluster” and “landmark article” are selected as the evaluation factors to judge the hotspots and trends of a domain comprehensively. Third, overall perspective of hotspots and trends of 3D printing is put forward for the first time.

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Considering the advantages of 3D printing, intelligent factories and distributed manufacturing, the 3D printing distributed intelligent factory has begun to rise in recent years. However, because the supply chain network of this kind of factory is very complex, coupled with the impact of customized scheduling and environmental constraints on the enterprise, the 3D printing distributed intelligent factory is facing the great challenge of realizing green supply chain networks and optimizing production scheduling at the same time, and thus a theoretical gap appears. This paper studies the hybrid optimization of green supply chain networks and scheduling of the distributed 3D printing intelligent factory. Firstly, according to the green supply chain network architecture of the distributed 3D printing intelligent factory, the cost minimization model is constructed. Secondly, mathematical software is used to solve the model, and the scheduling plan can be worked out. Finally, through the simulation analysis, it is concluded that the influencing factors such as demand, factory size and production capacity complicate the production distribution, and it can be observed that the carbon emission cost has gradually become the main factor affecting the total cost. The study has a reference value for the management decision making of the distributed 3D printing intelligent factory under the background of carbon emissions.