Open Access

ARTICLE

Competency Driven Resource Evaluation Method for Business Process Intelligence

Abid Sohail^1,*, Dhanapal Durai Dominic², Mohammad Hijji³, Muhammad Arif Butt⁴
1 Department of Computer Science, COMSATS University Islamabad, Lahore, Pakistan
2 Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Tronoh Perak, Malaysia
3 Department of Computer Science, University of Tabuk, Tabuk, Saudi Arabia
4 Punjab University College of Information Technology, University of the Punjab, Lahore, Pakistan
* Corresponding Author: Abid Sohail. Email:
(This article belongs to this Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)

Computers, Materials & Continua 2021, 69(1), 1141-1157. https://doi.org/10.32604/cmc.2021.018023

Received 22 February 2021; Accepted 03 April 2021; Issue published 04 June 2021

Abstract

Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge. One of the established ways of improving process performance is to assign the most appropriate resources to each task of the process. However, evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way, is missing. It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored. To address this problem an integrated resource classification framework is presenting that identifies competence, suitability, and preference as the relationship of task with resources. But, only the competence relationship of human resources with a task is presented in this research as a resource competence model. Furthermore, the competency calculation method is presented as a user guider layer for business process intelligence-based resource competence evaluation. The computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the process. Applicability of method is illustrated through a heathcare case study.

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Keywords

Data sciences; artificial intelligence; business process management; business process improvement; process warehouse; data warehouse; resource competency; resource competency modeling; health care

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