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Ontological Support for Process-Oriented Competency Management

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Information Technology for Management. Ongoing Research and Development (ISM 2017, AITM 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 311))

Abstract

Over the last few decades process-oriented approach has become an essential standard for many companies. This situation requires a new outlook on competency management. The competencies of employees should be considered in the framework of tasks implemented within the business process. According to the author’s observations, the greatest challenge is the lack of a consistent view of the overall aspects of human resources in conjunction with business process flow. In particular there is a gap between the process models defined in BPM systems and the competency descriptions defined in HR systems. The aim of this paper is to propose a solution based on a on Software Competence Ontology designed by the author, which can be used to support process-oriented competency management in a software development company. The primary purpose of the solution is to facilitate finding the best contractors to perform the business process tasks. With this purpose in mind a method of comparing candidates’ skills and knowledge was developed. The presented approach can be useful in process-based and virtual organizations where the environment is dynamic and frequent changes are needed to preserve the company’s competitiveness and agility. An example proposal of practical implementation of ontology-enhanced business process model was presented and illustrated by the process of software configuration management.

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  1. Częstochowa University of Technology, Al. Armii Krajowej 19b, Częstochowa, Poland

    Ilona Pawełoszek

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  1. Ilona Pawełoszek
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Correspondence to Ilona Pawełoszek .

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  1. University of Economics in Katowice, Katowice, Poland

    Ewa Ziemba

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Pawełoszek, I. (2018). Ontological Support for Process-Oriented Competency Management. In: Ziemba, E. (eds) Information Technology for Management. Ongoing Research and Development. ISM AITM 2017 2017. Lecture Notes in Business Information Processing, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-319-77721-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-77721-4_3

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