A Comprehensive Analysis of Supply Chain Collaboration, Technological Upgrading, and Market Demand as a Framework for Increasing Competitiveness
Abstract:
The purpose of this study is to evaluate how supply chain collaboration, technical advancement, and consumer demand affect manufacturing firms' ability to compete in Guangdong Province. The particular goals include testing the relationship between supply chain cooperation, technological innovation, and market demand development; evaluating technology maturity, adaptability, and cost-effectiveness; assessing supply chain coordination efficiency, supply chain risk management capability, and supply chain innovation capability; analyzing accuracy of market demand forecasting, speed of market demand response, and customer demand diversity; and developing a comprehensive framework for enhancing enterprise competitiveness. Using surveys, data analysis, and empirical research, this study uses a mixed-method approach that combines quantitative and qualitative analysis to examine the links between the variables and offer management recommendations.
Keywords:
Supply Chain Collaboration, Technological Advancement, Consumer Demand, Manufacturing Competitiveness
APA Citation:
Haoli Tan (2024). A Comprehensive Analysis of Supply Chain Collaboration, Technological Upgrading, and Market Demand as a Framework for Increasing Competitiveness. International Journal of Global Economics and Management, 3(3), 343-362. https://doi.org/10.62051/ijgem.v3n3.40
References
- Baig, H., Ahmed, W., & Najmi, A. (2021). Understanding influence of supply chain collaboration on innovation-based market performance. International Journal of Innovation Science. https://doi.org/10.1108/ijis-03-2021-0054
- Bayraktar, E., Sari, K., Tatoğlu, E., Zaim, S., & Delen, D. (2020). Assessing the supply chain performance: a causal analysis. Annals of Operations Research, 287, 37-60. DOI: 10.1007/s10479-019-03457-y
- Bolatan, G. I., Giadedi, A., & Daim, T. (2022). Exploring Acquiring Technologies: Adoption, Adaptation, and Knowledge Management. IEEE Transactions on Engineering Management, PP, 1-9. https://doi.org/10.1109/TEM.2022.3168901
- Bu, H., Ouyang, L., & Shi, N. (2022). The influence of entrepreneurial growth stage on entrepreneurial performance of high-tech enterprises in the context of 5G. 2022 3rd International Conference on Electronics, Communications and Information Technology (CECIT), 316-322. https://doi.org/10.1109/CECIT58139.2022.00062
- Coghlan, C., Labrecque, J., Ma, Y., & Dubé, L. (2020). A Biological Adaptability Approach to Innovation for Small and Medium Enterprises (SMEs): Strategic Insights from and for Health-Promoting Agri-Food Innovation. Sustainability, 12(10), 4227. DOI: 10.3390/su12104227
- Dai, X. (2022). Supply Chain Relationship Quality and Corporate Technological Innovations: A Multimethod Study. Sustainability. https://doi.org/0.3390/su14159203
- Di, C. (2020). Research on the Product Logistics Cost Control Strategy based on the Multi-source Supply Chain Theory. Intelligent Automation and Soft Computing. https://doi.org/10.31209/2020.100000173
- Erdoğan, S. (2021). Dynamic nexus between technological innovation and buildings sector's carbon emission in BRICS countries. Journal of Environmental
- Gabdulin, R. R., Lyaskovskaya, E., Korovin, A. M., & Rets, E. A. (2022). Forecasting demand in the market of road construction equipment using data mining. Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics. https://doi.org/10.14529/ctcr220311
- Ghani, A. S. (2024). Revolutionizing Supply Chains: A Comprehensive Study of Industry 4.0 Technologies (IoT, Big Data, AI, etc.). International Journal of Supply Chain Management, 3(4), 37-49. https://doi.org/10.55041/ijsrem30037
- Haque, M. S., Amin, M. S., & Miah, J. (2023). Retail Demand Forecasting: A Comparative Study for Multivariate Time Series. ArXiv. https://doi.org/ 10.48550/arXiv.2308.11939
- Hassani, Y., Ceaușu, I., & Iordache, A. (2020). Lean and Agile model implementation for managing the supply chain. Proceedings of the International Conference on Business Excellence. https://doi.org/10.2478/picbe-2020-0081
- Johari, M., & Hosseini-Motlagh, S. M. (2019). Coordination of social welfare, collecting, recycling and pricing decisions in a competitive sustainable closed-loop supply chain: a case for lead-acid battery. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03292-1
- Jiménez-Jiménez, D., Martínez-Costa, M., & Sánchez-Rodríguez, C. (2019). The mediating role of supply chain collaboration on the relationship between information technology and innovation. Journal of Knowledge Management, 23(3), 548-567. https://doi.org/10.1108/JKM-01-2018-0019
- Jung, H., & Park, S. (2020). A Study on the Deep Learning based Prediction of Production Demand by using LSTM under the State of Data Sparsity. IOP Conference Series: Materials Science and Engineering, 926. https://doi.org/10.1088/1757-899X/926/1/012031