This study is an endeavor to forecast the quality of port infrastructure of Asian port economies. A sample of 32 economies are selected counting the year from 2015-2019. Data are collected from the Global Competitiveness Report. A data forecasting algorithm, the GM (1, 1) approach, is applied, and finally the economies are clustered with their forecasted values using Hierarchical Cluster Analysis (HCA). The empirical findings demonstrated that there would be change in the quality of port infrastructure. Economies with high quality of port infrastructure will be the best performer, while the others with poor infrastructure will do better in future as well. The modified Grey model showed excellent accuracy and better performance in forecasting the quality of port infrastructure. The forecasting values of each country offer valuable insight to formulate individual policies and strategies. The port facilitators and the decision makers would be able to distinguish the higher and the lower performer from the cluster analysis and focus on the underperformed region undertaking individual measures to improve the quality of their port infrastructure as well. Additionally, it will contribute as a hybrid methodology that the individual country can apply practically in prediction and clustering them to take decision on the quality of port infrastructure for next 5 years. Finally, it will theoretically extend the frontier of knowledge in port infrastructure research.

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@article{oscm-2022-139,
  title={Forecasting the Quality of Port Infrastructure of Asian Port Countries: An Application of the Model GM (1, 1) and Clustering Them Using HCA Algorithm},
  author={},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2022},
  volume={15},
  number={1},
  pages={0--0},
  doi={http://doi.org/10.31387/oscm0480334}
}

 (2022). Forecasting the Quality of Port Infrastructure of Asian Port Countries: An Application of the Model GM (1, 1) and Clustering Them Using HCA Algorithm. Operations and Supply Chain Management: An International Journal, 15(1), 0-0. https://doi.org/http://doi.org/10.31387/oscm0480334