Methods for assessing the quality of the talent pool in a project team and how their skills influence the project risk

Aim. To develop a methodology for assessing the quality of the talent pool in a project team and forecasting the impact of their expertise on technical and economic indicators and engineering risks of the safe operation of planned transport facilities. Materials and Methods. The quality of transport facility projects is determined by innovative technical and technological solutions and construction materials. It also depends on the expertise of the design teams. The methods for assessing the quality of the talent pool in a design team is based on the assumption that the skills of a designer are latent (hidden variable) and depend on the skills attributes inherent in an expert. Quantitative characteristics of attributes are random variables. In this regard, the quality indicators of the projects being developed are probabilistic, which calls for an analysis of the risks of design (engineering) errors. To assess the skills of experts and their impact on the technical and economic indicators of projects, along with the general methodological principles of qualimetry theory, methods of multi-criteria and statistical analysis are proposed. Quantitative characteristics of skills attributes (their expected values) are combined into an integral skills indicator through the comprehensive use of mathematical methods of multi-additive and multiplicative convolution. Results. We produced an analytical model of an expert’s skills that takes into account the impact of both interdependent and independent private indicators of professional training on the generalized quality indicator. Conclusion. The problem of professional training is most evident at the stage of designing transport facilities. The project establishes the main properties of safety, reliability, efficiency and other quality indicators of transport facility construction projects based on transport operating and technical and economic indicators. The proposed methodology allows for identifying functional relationships between the quality of the talent pool in a design team and the quality of design solutions. The functional relationships obtained during the development of the methodology make it possible to assess the impact of skills on project risks and ensure thorough hiring, as well as developing training programs for team experts.

Authors

Institutions

Publication Details

Journal
Modern Transportation Systems and Technologies
Published
2025-03-23
DOI
https://doi.org/10.17816/transsyst642802
Citations
1
Primary Topic
Engineering Education and Technology
Type
article
Field-Weighted Citation Impact
3.25
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Methods for assessing the quality of the talent pool in a project team and how their skills influence the project risk

Marina Petrochenko
1 citations
Modern Transportation Systems and Technologies
Engineering Education and Technology
3.25
article

Methods for assessing the quality of the talent pool in a project team and how their skills influence the project risk

Marina Petrochenko
article en
1 citations

Abstract

Aim. To develop a methodology for assessing the quality of the talent pool in a project team and forecasting the impact of their expertise on technical and economic indicators and engineering risks of the safe operation of planned transport facilities. Materials and Methods. The quality of transport facility projects is determined by innovative technical and technological solutions and construction materials. It also depends on the expertise of the design teams. The methods for assessing the quality of the talent pool in a design team is based on the assumption that the skills of a designer are latent (hidden variable) and depend on the skills attributes inherent in an expert. Quantitative characteristics of attributes are random variables. In this regard, the quality indicators of the projects being developed are probabilistic, which calls for an analysis of the risks of design (engineering) errors. To assess the skills of experts and their impact on the technical and economic indicators of projects, along with the general methodological principles of qualimetry theory, methods of multi-criteria and statistical analysis are proposed. Quantitative characteristics of skills attributes (their expected values) are combined into an integral skills indicator through the comprehensive use of mathematical methods of multi-additive and multiplicative convolution. Results. We produced an analytical model of an expert’s skills that takes into account the impact of both interdependent and independent private indicators of professional training on the generalized quality indicator. Conclusion. The problem of professional training is most evident at the stage of designing transport facilities. The project establishes the main properties of safety, reliability, efficiency and other quality indicators of transport facility construction projects based on transport operating and technical and economic indicators. The proposed methodology allows for identifying functional relationships between the quality of the talent pool in a design team and the quality of design solutions. The functional relationships obtained during the development of the methodology make it possible to assess the impact of skills on project risks and ensure thorough hiring, as well as developing training programs for team experts.

Modern Transportation Systems and TechnologiesVol. 11(1)
Peter the Great St. Petersburg Polytechnic University (RU)
Openalex Percentile: Top 10%
Engineering Education and Technology
3.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.