Article 2024

A Taxonomy-Based Data Model for Assessing Engineering Skills in an Organizational Context

IEEE Transactions on Engineering Management
Journal · Vol. 71 · pp. 15363-15374
Abstract

A taxonomy-based data model is proposed to create a knowledge system for managing engineering skills within an organization, motivated by the need to balance organizational expertise requirements and availability. The model, adapted from the "European Skills, Competences, Qualifications, and Occupations"framework, is designed to categorize and evaluate skills relevant to the engineering department of the National Physical Laboratory. This 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 categorizing staff according to their work areas, allowing the model to conform with inherent organizational 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, optimized resource allocation, and improved training programmes. © 1988-2012 IEEE.

Keywords

Author Keywords

Taxonomy engineering management Data model skills management taxonomical adaptation

Index Keywords

Taxonomies Organizational context Organisational Quantitative data Knowledge system Engineering skills Engineering department Engineering management National Physical Laboratory Skills managements Taxonomical adaptation
Author Affiliations
Data Science Department, National Physical Laboratory, Middlesex, United Kingdom
Department of Engineering, National Physical Laboratory, Middlesex, United Kingdom
Funding & Acknowledgements
No funding information
References 10 References
1 J Manage Stud, (1996)
2 Hum Resource Manage Rev, (2000)
3 Planning for Higher Education, (2020)
4 Fergusson, Lee, Learning by… Knowledge and skills acquisition through work-based learning and research, Journal of Work-Applied Management, 14, 2, pp. 184-199, (2022)
5 Alibasic, Armin, Evaluation of the trends in jobs and skill-sets using data analytics: a case study, Journal of Big Data, 9, 1, (2022)
6 Skills Gaps and the Path to Successful Skills Development Emerging Findings from Skills Measurement Surveys in Armenia Georgia Fyr Macedonia and Ukraine, (2015)
7 World Economic Forum, (2020)
8 McKenney, Martin J., Using the DSRM to Develop a Skills Gaps Analysis Model, IEEE Engineering Management Review, 48, 4, pp. 102-119, (2020)
9 Learning Pool, (2023)
10 Shrm Foundation, (2021)
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