Competence Assessment as an Expert System for Human Resource Management: A Mathematical Approach

Abstract: Efficient human resource management needs accurate assessment and representation of available competences as well as effective mapping of required competences for specific jobs and positions. In this regard, appropriate definition and identification of competence gaps express differences between acquired and required competences. Using a detailed quantification scheme together with a mathematical approach is a way to support accurate competence analytics, which can be applied in a wide variety of sectors and fields. This article describes the combined use of software technologies and mathematical and statistical methods for assessing and analyzing competences in human resource information systems. Based on a standard competence model, which is called a Professional, Innovative and Social competence tree, the proposed framework offers flexible tools to experts in real enterprise environments, either for evaluation of employees towards an optimal job assignment and vocational training or for recruitment processes. The system has been tested with real human resource data sets in the frame of the European project called ComProFITS.

• The paper introduces a two-phase mathematical model that evaluates candidate competence and clusters individuals based on job-specific requirements.
• It emphasizes competence gap analysis, quantifying discrepancies between actual and required skills to align human resources effectively.
• The integration of the PIS competence tree and Hierarchical Cumulative Voting allows for customizable, strategic workforce management.

Competence Assessment as an Expert System for Human Resource Management: A Mathematical Approach

The paper explores a mathematically driven framework for competence assessment within Human Resource Management (HRM), integrating software technologies and statistical methods. The proposed system is based on a structured competence model, the Professional, Innovative, and Social (PIS) competence tree, aimed at optimizing workforce alignment and development. This approach is particularly significant for enterprises seeking to bridge competence gaps, improve recruitment processes, and leverage employee potential.

Key Contributions

The primary contribution of the paper is the systematic development of a competence reference model that facilitates the accurate assessment of individual competencies in a manner that aligns with organizational demands. This model is inherently adaptable, providing a framework that can be tailored across various sectors.

1. Mathematical and Statistical Integration: The authors introduce a mathematical model for competence assessment that consists of two phases: Evaluation Phase (EP) and Assessment Phase (AP). The EP focuses on obtaining Acquired Competence Data (ACD), while the AP involves ranking and clustering candidates based on the competence requirements for specific job roles, using statistical measures such as ANOVA and clustering algorithms like the Scott-Knott method.
2. Competence Gap Analysis: The approach centers on quantifying the discrepancies between actual and required competences through a gap function. This is a pivotal step for organizations aiming to strategically select and develop human resources to meet job-specific demands.
3. The PIS Competence Tree: Inspired by the CoMaVet project, the PIS tree provides a hierarchical framework for evaluating competences across three main categories and their respective sub-components. This structure supports diverse assessment methods, including 360-degree feedback and self-assessment, offering a comprehensive representation of an individual's capabilities.
4. Hierarchical Cumulative Voting (HCV): The prioritization of competences is achieved using HCV, allowing organizations to assign relative importance to various competences tailored to specific job roles. This method embeds flexibility, enabling companies to customize competence assessment according to the unique strategic needs of their industry and organizational culture.

Practical Implications

The implementation of this competence assessment framework offers several practical benefits for HR departments:

• Enhanced Recruitment and Training: By clearly identifying competence gaps, organizations can effectively orient their recruitment and training efforts, thereby reducing costs associated with skill mismatches.
• Strategic Workforce Management: The detailed competence profiles generated by this system aid high-level decision-making, fostering improved workforce planning and the alignment of employee strengths with organizational objectives.
• Cross-Industry Applicability: The model’s adaptability is evidenced by the testing outcomes within the ComProFITS project, showcasing successful validation with real-world employee data from various sectors.

Methodological Rigor and Validation

The research employs robust quantitative techniques for competence assessment, ensuring methodological rigor:

• DOE and Repeated Measures Design: These are utilized to handle the inherent variability in competence scores, reflecting the complex interdependencies of competence categories.
• Multiple Hypothesis Testing and SK Algorithm: These facilitate the formation of homogeneous groups of candidates, aiding in the selection process by highlighting those best fit for specific roles.

Future Directions

The paper concludes by outlining future research avenues, emphasizing the need for broader application of the developed algorithms across diverse domains such as medical science. The potential integration of social media analytics and big data technologies is also discussed, aimed at enhancing the dynamism and accuracy of competence assessments in real-time, large-scale environments.

In summary, this research provides a mathematically rigorous and practically relevant framework for competence assessment in HRM. Its innovative integration of mathematical modeling with HR practices bridges a critical gap in workforce optimization, offering a strategic tool for organizations to enhance their competitive edge through informed human resource development.