Abstract
Performance of business processes is affected by capabilities of employees who execute activities in these processes. Resource allocation approaches aim to identify an optimal allocation of resources to activities which supports the achievement of some process performance goals (e.g., in terms of time or cost). The majority of resource allocation approaches proposed in the Business Process Management community are process-centric (i.e., they aim to optimise process performance) and they neglect capability development needs of employees. In this article, we propose a novel approach for recommending unfamiliar process activities to employees which is based on the application of machine learning techniques to information extracted from process execution data. The goal of the approach is to assist organisations in their quest for capacity development by providing employees with opportunities to gain experience through the execution of new activities. The approach was implemented and evaluated by conducting experiments with real publicly available event logs. In the experiments, we compared the predictions provided by the approach with actual activity executions recorded in the logs. The experiments demonstrated the effectiveness of different approach configurations and showed that this machine-learning based approach significantly outperforms an existing algorithm proposed in earlier work.
Highlights
Many activities in business processes are performed by employees, and performance of such processes can be significantly affected by capabilities of these employees
EVALUATION we first describe event logs used in the evaluation (Section IV-A) and the experimental setup (Section IV-B), we discuss the results of the experiments (Section IV-C)
DATA SETS We evaluated the performance of the approach using two publicly available real event logs, referred to here as BPIC124 and BPIC175
Summary
Many activities in business processes are performed by employees, and performance of such processes can be significantly affected by capabilities of these employees. Business Process Management (BPM) community has proposed several resource allocation approaches [4], which consider different resource allocation criteria; for example, resource preferences, experience, compatibility and previous performance [3]. These approaches usually focus on improving process performance and allocate process activities to experienced resources. Information about executions of business processes is typically recorded in process execution data (so called event logs). We assume that some employees in an organisation (e.g., those in the same role) follow similar learning paths [25] and we can propose recommendations of new activities by analysing work histories of employees recorded in event logs
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have