Gregorio, J; Miller, R; Afxentiou, I; Hippolyte, J-L; Morantz, P (2024) A Taxonomy-Based Data Model for Assessing Engineering Skills in an Organizational Context. IEEE Transactions on Engineering Management, 71. pp. 15363-15374. ISSN 0018-9391

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Abstract

A taxonomy-based data model is proposed to create a knowledge system for managing engineering skills within an organisation, motivated by the need to balance organisational expertise requirements and availability. The model, adapted from the “European Skills, Competences, Qualifications, and Occupations” framework, is designed to categorise and evaluate skills relevant to the engineering department of the National Physical Laboratory. The allows extraction of quantitative data on individual staff members’ skills and competency levels, and the necessary skills for specific Job Title and Job Role combinations. It distinguishes between “Job Titles”, official job designations, and “Job Roles”, unofficial designations categorising staff according to their work areas, allowing the model to conform with inherent organisational rigiditiy. The model can cross-reference information using specific queries, such as extracting skills from specific individuals and assessing if they meet their current job functions. This model enhances existing skill management frameworks by allowing for a traceable pathway for skill allocation, allowing for future expansion by including other departments. Integrating validation procedures to assess staff skills, such as the inclusion of proof attached to skills, can also be considered. It offers operational benefits like enhanced capability planning, informed staff development, optimised resource allocation, and improved training programmes.

Item Type:	Article
Keywords:	taxonomy; taxonomical adaptation; data model; skills management; engineering management
Subjects:	Mathematics and Scientific Computing > Modelling
Divisions:	Data Science
Identification number/DOI:	10.1109/TEM.2024.3486812
Last Modified:	16 Feb 2026 09:29
URI:	https://eprintspublications.npl.co.uk/id/eprint/10276