Dataset for assessing the efficiency factors in Malaysian ports: Dry bulk terminal

This research paper provides for the identification of dry bulk terminal efficiencies on the basis of 10 key performance factors in Malaysian ports. Data were collected from 18 dry bulk ports in Malaysia in 2017 through an online questionnaire and distributed via e-mail. The dispersion of the respondents corresponds approximately to the structure of the Malaysian maritime terminal in dry bulk. The data provides port management perceptions towards 10 variables that have been surveyed. Each perception assessed the level of efficiency factors based on a percentage rate of 100%. Efficiency factors in dry bulk terminals have been identified with varying characteristics based on a descriptive analysis table. The dataset presented consists of a brief analysis of all 10 variables involved, including the minimum, maximum, mean, interquartile median and standard deviation. In addition to the descriptive analysis, the normality test and histogram were also performed. Data can be used to measure ports-efficiency factors in another research.


a b s t r a c t
This research paper provides for the identification of dry bulk terminal efficiencies on the basis of 10 key performance factors in Malaysian ports. Data were collected from 18 dry bulk ports in Malaysia in 2017 through an online questionnaire and distributed via e-mail. The dispersion of the respondents corresponds approximately to the structure of the Malaysian maritime terminal in dry bulk. The data provides port management perceptions towards 10 variables that have been surveyed. Each perception assessed the level of efficiency factors based on a percentage rate of 100%. Efficiency factors in dry bulk terminals have been identified with varying characteristics based on a descriptive analysis table. The dataset presented consists of a brief analysis of all 10 variables involved, including the minimum, maximum, mean, interquartile median and standard deviation. In addition to the descriptive analysis, the normality test and histogram were also performed. Data can be used to measure ports-efficiency factors in another research.

Value of the data
• In dry bulk terminal, the data encapsulates a large number of Malaysian ports efficiency dataset. • The data offers insight for assessing Malaysian Ports efficiency in dry bulk terminal where it can be used to comprehend the other terminals of Malaysian ports (e.g. changes in coastal shipping services and port facilities) into regional economic change; in the long run, give broad geographical and temporal coverage of the data. • The data uncovers the variances of efficiency factors in dry bulk terminal ports and for port managers in order to build a long-term action strategy.      . These are one of the facilities for Malaysians' port managers to achieve higher level of efficiency in the port operation and it was categorised of cargo handling technology and equipment, and port information technology. Thus, affected in port trade to take initiatives to expand port capacity for trucking efficiency [ 1 -2 ].

Data description
While, at Table 2 shows the normality test for Stockpile Locations as at Table 1 . were consisted Stockpile Locations < 1 km (VA.5), Stockpile Locations 1 km -3 km (VA.6) Stockpile Locations 3 km -5 km (VA.7), Stockpile Locations 5 km -10 km (VA.8), Stockpile Locations > 10 km (VA.9) are normal. Table 3 and Fig. 2 show the variability of all variables, i.e. the minimum, maximum, interquartile, median, mean standard deviation, Variance, skewness and Kurtosis. Figs. 1 and 2 show the normality test and histogram for each variable, respectively. The strategic location of a port significantly increases its efficiencies. From Fig. 1 , the mean value for 18 ports are mostly equivalent for all types of variables. However, Stockpile Locations 5 km -10 km (VA.10) consistently showed low value. The results were related with the position refers to of "diversion distance" concept where ships deviate from main trunk routes to the port. It was discussed by [3] said that the centrality of shipping routes is vital not only because it acts a port gateway but also as a hub for transhipment.

Experimental design, materials, and methods
In summary, our ports data includes 18 different places. These ports are appearing to be consistently important places for ocean shipping. Others appear in the data in different benchmark years, which indicates real changes in use and was similar with the concept of the study by [4] , but in this data has also distinct recording practices at different times and between the sources. Fig. 2 shows the aggregate distribution of the number of appearances of each variables for all ports.
Appendix A. Supplementary data Supplementary data to this article can be found online at https://data.mendeley.com/datasets/ jxj6dt54w6/1 Fig. 1. The normality test chart for port efficiency in dry bulk terminal.