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Human resources have become integral to every successful business, so companies without motivated and enthusiastic employees quickly become redundant. There has been an enormous change in the role of human resources as an operational, strategic component in recent years. This study aims to help a national organization refine its human resource (HR) strategy using the Data-driven decision support framework (DDSF) to navigate through existing reports, perform surveys and conduct in-depth interviews with organization management and experts to compile the information collection. Using SWOT analysis, human resource plans have been developed. Regarding internal and external variables, the organization under investigation is in an above-average condition regarding human resources. It is advised that the organization prioritize defensive strategies when preserving and producing human resources and aggressive strategies when entering human resources because it fails to perform well in external factors and is performing below average in these areas. In order to promote and enhance data-driven decision-making in HRM, policymakers will need to establish legal frameworks, data governance frameworks, innovation incentives and training and development programs. Firms using data to enhance labor management processes and promote organizational performance in digitalized environments might benefit from this framework’s incisive analysis and helpful advice. The results of the experiments prove that the proposed DDSF model may improve the efficiency and effectiveness of the organization.

This paper proposes a blockchain-based framework to improve the efficiency of ship traffic in port. In the framework, ship agents, terminals, tug company, pilot station, and government share information and the information is stored in a blockchain. Based on the shared information, we discuss three categories of data-driven models that can improve the operations management of the above five parties. The first category is decisions made by a single party. The second category involves decisions of at least two ship agents. The third category relates to multi-party decision-making under uncertainty. This study hopes to stimulate maritime practitioners to embrace blockchain technology and data-driven approaches to enhance the competitiveness of the industry.

In the fixed-block railway traffic, the trains adjust their speeds in view of their preceding allowable spaces caused by their respective front adjacent trains or specified by scheduling commands. The railway lines have the line-type speed limits within some block sections and the point-type ones at the terminals of block sections. Those speed limits originate from line conditions, scheduling commands and indications of signal equipment. This paper attempts to in detail reveal the train movement mechanism synthetically considering those temporal–spatial constraints. The proposed train movement model defines four kinds of target points and utilizes them to successively engender the instantaneous target points with their corresponding target speeds. It adopts the rule-based description mechanism in cellular automata (CA) but with continuous spaces to replicate restrictive, autonomous and synergistic behaviors of and among trains. The selections of accelerations and decelerations are based upon the data models of practical acceleration and deceleration processes; thereupon, the model is data-driven. The analysis of train movement dynamics through case studies demonstrates that the extended CA model can reproduce the train movement mechanism of grading speed control to satisfy the aforementioned temporal–spatial constraints. The model is applicable to represent the as-is or should-be states of train movements when adjustable parameters are properly configured.