Our aim is to design a sustainable supply chain (SSC) network, which takes into consideration consumer environmental behaviors (CEBs). CEBs not only affect consumers' demand for products with low carbon emissions, they also affect their willingness to pay premium prices for products with low carbon emissions. We incorporate CEBs into the SSC network model involving location, routing and inventory. Firstly, a multi-objective optimization model comprised of both the costs and the carbon emissions of a joint location-routing-inventory model is proposed and solved, using a multi-objective particle swarm optimization (MOPSO) algorithm. Then, a revenue function including CEBs is presented on the basis of a Pareto set of the trade-off between costs and carbon emissions. A computational experiment and sensitivity analysis are conducted, employing data from the China National Petroleum Corporation (CNPC). The results clearly indicate that our research can be applied to actual supply chain operations. In addition, some practical managerial insights for enterprises are offered.
Purpose Global economic growth provides new opportunities for the development of clothing enterprises, but at the same time, the rapid growth of clothing customization demand and the gradual increase of clothing costs also pose new challenges for the development of clothing enterprises. In this context, 3D printing technology is injecting new vitality and providing a new development direction for the vigorous development of clothing enterprises. However, with the application of 3D printing technology, more and more clothing enterprises are facing the problem of business model innovation. In view of the lack of relevant research, it is necessary to carry out exploratory research on this issue. Design/methodology/approach The business model canvas method was adopted to design business model for clothing enterprises using 3D printing. The simulation model of the designed business model was constructed by a system dynamics method, and the application of the designed business model was analysed by a scenario simulation. Findings Mass selective customization-centralized manufacturing (MSC-CM) business model was constructed for clothing enterprises using 3D printing, and a static display was carried out using the BMC method. A dynamic simulation model of the MSC-CM business model was constructed. The future scenario of clothing enterprises using 3D printing was developed, and a simulated enterprise was analysed. The results show that the MSC-CM business model has a good application value. The simulation model of the MSC-CM business model performs the function of a business strategy experiment platform and also has a good practical application value. Research limitations/implications The MSC-CM business model is only a typical business model for clothing enterprises using 3D printing. It is necessary to further develop other business models, and some elements of the MSC-CM business model need to be further improved. In addition, the MSC-CM business model simulation uses a general model, which is not suitable for all clothing enterprises using 3D printing. When the model is applied, the relevant enterprises can further adjust and optimize it, thereby improving the validity of the simulation model. Originality/value To the best of the authors’ knowledge, this is the first paper on the MSC-CM business model for garment enterprises using 3D printing. Secondly, it is the first time that the business model of clothing enterprises using 3D printing has been simulated. In particular, the proposed business model simulation provides the possibility for testing the business strategy of clothing enterprises using 3D printing. In addition, a positive attempt has been made in the collaborative research of using both a static display business model and a dynamic simulation business model.
The research on the business model of garment enterprises (BMGE) has expanded rapidly in the last decade. However, there is still a lack of comprehensive reviews of it, let alone visual research. Based on scientometrics, in this paper 118 papers and their 4803 references from Science Citation Index Expanded, Social Sciences Citation Index, Conference Proceedings Citation Index—Science, and Conference Proceedings Citation Index—Social Science & Humanities for the period 2010–2020 about the BMGE were analyzed by visualizing the co-cited references, co-occurrence keywords, burst references, dual-map overlays, and more with CiteSpace, Google Maps, and VOSviewer. The research revealed the intellectual landscapes of the BMGE for the first time and mapped the landmark papers, hotspots and trends, national or regional distributions and their cooperation networks, highly cited authors, and prestigious journals and disciplines related to the BMGE. The results show that the biggest hotspot is the fast fashion business model; social responsibility, smart fashion, Internet of Things, and sharing fashion are the main emerging hotspots; and the research focuses has evolved from traditional business models to business models driven by new technologies, then to new issues such as circular economy models. The institutions are mainly distributed in China, the United States, and Western Europe, and there is cooperation between more than 11 countries. The most popular disciplines are economics and politics, while psychology, education, and social science are the essential basic disciplines. The Journal of Cleaner Production and Journal of Fashion Marketing and Management, among others, actively promoted the research.
The Made in China 2025 national strategy has prioritized intelligent connected vehicles (ICV) to realize the intelligence and connection transformation and upgrading of the automotive industry, ushering in unprecedented development opportunities. There are two technology paths in the ICV industry: singlevehicle intelligence and vehicle-infrastructure collaboration. Both face problem of low technological innovation efficiency, and key to solving it is breaking down barriers between enterprises and realizing crossborder collaborative innovation. This study offers a new cross-border collaborative innovation development paradigm for the ICV industry, centered on automotive enterprises and technology platform providers. This study examines the impact of changes in key parameters on the evolutionary results using the system dynamics method to analyze the efficiency of cross-border collaborative innovation in the ICV industry. The simulation results showed that cross-border collaborative innovation is inevitable for the ICV industry. Furthermore, compared to the single-vehicle intelligence scenario, the vehicle-infrastructure collaboration scenario shows faster convergence between automotive enterprises and technology platform providers. Finally, the choice of system collaborative innovation strategy is influenced by default cost and the collaborative innovation risk coefficient, whereas the cost-sharing coefficient and network connection fee only have an impact on the cross-border collaborative innovation system's rate of evolution in the ICV industry.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.