A human resource allocation method for business processes using team faultlines

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 50; no. 9; pp. 2887 - 2900
• Main Authors: Zhao, Weidong, Pu, Shi, Jiang, Danni
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Business process management; Clustering; Composition effects; Computer Science; Geological faults; Human performance; Human resources; Machines; Manufacturing; Mechanical Engineering; Multilayers; Performance prediction; Prediction models; Processes; Resource allocation; Process mining; Business process; Resource allocation; Team faultlines
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-020-01686-4

Equitable human resource allocation can maximize the efficiency of resources and optimize business performance. Despite numerous methods that have been suggested for solving the allocation problem, most of the existing methods focus on a single resource or task, and neglect the effects of team composition in business performance. In this paper, we introduce team faultlines to the human resource allocation problem. We first analyze resource characteristics from a demographic perspective and business process, then utilize the information value to select key characteristics and determine the corresponding weight. Second, we qualitatively identify team faultlines based on the clustering results of human resources and quantitatively measure the strength and distance of team faultlines. Multi-layer perceptron is utilized to build the base and ensemble performance prediction model. The allocation model and flow are designed subsequently. The reasonableness and effectiveness are evaluated with a real-world scenario, and the results show that our human resource allocation method using team faultlines can allocate human resources with high performance and optimize the business process.