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ProST-re: Simulation-Based Analytics for Finding Appropriate Substitutes and Task Reallocation
DOI:10.1109/ACCESS.2022.3225442.png)
摘要
En 中文
Absence of an organization's employees often brings negative impacts on an organization's performance and profitability. To address this problem, various studies have been conducted to find ways to identify an appropriate substitute employee for an absent employee. However, previous studies have not reflected changes in employee work relationships over time. Furthermore, the studies have not considered the effects of task reallocation on an organization due to an absent employee. To address these limitations, this paper proposes an approach to process-oriented substitute-task reallocation when there is an absent employee. This approach includes multiple steps to find process-oriented substitute-task reallocation. First, a cooperation network, a social network that demonstrates employee work relationships, is built using the attributes of the employees obtained from event logs. Then, the node2vec algorithm is used to predict links in the cooperation network to select appropriate candidates for substitute employees. Finally, a simulation model evaluates the probable impact of the preliminary reallocation of the candidates' tasks on the business-process cycle time and eventually finds process-oriented substitute-task reallocation that takes the shortest time to complete process instances. This research validates the approach by using a real-world event log. The validation result explains that reflecting changes in employee work relationships is critical in selecting substitute employees. By adopting the proposed approach in this research, organizations could maintain the appropriate number of employees and reallocate tasks optimally in a rapidly-changing environment.
Keyword:
Task analysis
Organizations
Predictive models
Social networking (online)
Profitability
Pandemics
Measurement
Business process simulation
cooperation network
link prediction
substitute
task reallocation
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
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