A Conceptual Approach to Managing Labor Resources in the Maritime Industry
Bibliographic record
Abstract
Motivation: More than 70% of accidents in the fleet are due to the "human factor" - a wide range of psychological and psychophysiological qualities of the person, which in some way affect the result of its operations. Statistics of major ship accidents in recent decades show that not a single element of the shipping system is aloof from them. That is ship crews, shipowners, charterers, consignees, classification societies and other organizations associated with maritime transportation form a “chain” of risk. The correct combination of human abilities and machine capabilities significantly increases the efficiency of the "person - machine" systems and determines the optimal use by a person of technical means for their intended purpose. Unfortunately, an adequate model that would allow both quantitative and qualitative optimization of the project team, especially in conditions of incomplete determination of the volume of work, today does not exist. In addition, the existing methods do not take into account the specifics of the formation and conditions for the implementation of projects by such teams as the ship's crew, namely the increased level of danger, the inability to make replacements during the voyage, the international composition, the language barrier, etc. In this context, the issue of human resources management as a basic element of achieving the efficiency of project implementation in the field of maritime transport is also urgent, which implies an emphasis on the problem of project-oriented management of crewing and activity of the crew of marine vessels. The issue of clarifying the role, importance and key elements of HRM strategies and policies in the implementation of these projects requires special consideration. The aim of the study is to develop methodological approaches for the forming of quantitative and qualitative composition and effective management of project teams, as a variable component of the project management system of marine vessels.Novelty: Develop methodological approaches for the quantitative and qualitative composition of project teams, as a variable component of the project team on the example of crews of marine vessels. The task of acceptability of the structure, quantitative and qualitative composition of the team was solved. The terminological base of project management was developed by more clearly defining the concepts of “project team” and “project management team”. The approach to the organization of the crewing company recruitment system to work on ships was proposed.Methodology and Methods: In this scientific research to achieve the objective and test the hypotheses suggested in the research paper was used: 1) project teams methodology in project management, in particular, the Project Management Institute Classification (2017) of types of teams in the organizational structure of complex projects and in their management and the taxonomy of SNCB Version 4.1 are designed to provide a comprehensive professional assessment of the level of training of project managers; 2) method for calculating the size of the project team is based on the condition of minimizing its number, which reduces operating costs for the implementation of the project by the Ringelmann effect is known - a formula that provides the ability to quantify and optimize the parameters of the project team; 3) the method of planning of the minimum crew of The International Association of Sea Pilots considering the role of the “human” element in preventing accidents and environmental pollution (the ISM Code and the STCW Convention 78/95 as amended).Data and Empirical Analysis: For the purpose of the study, data were collected and empirical analysis was conducted concerning the analysis of the accident rate of ships and crews for 2009-2019 (causes of accidents: damage to the case and mechanisms; clash; shipwreck and landing; fires and explosions; submergence; contact with the ground; varied; hostilities), that can have a result severe damage or loss of the ship.Policy Considerations: Human factor is the most important aspect that determines the efficiency of shipping development; maintaining of technical and technological processes of the ship puts certain requirements to the quantitative and qualitative composition of the team, deviation from which leads to the occurrence of certain risk events; formation of an effective model of ship's crew manning is the main link in ensuring effective shipping project management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".