A simulation model to predict and improve the performance of the working team and achieve better human-resource management strategies (case study: industrial management group, Faculty of Management and Economics of Azad University, Tehran University of Sciences and Research)

Given the importance of the role of human resources in organizations, strategic planning for achieving the optimum number of human resources in an organization is vital. The purpose of this research is to present a model for simulating and predicting team performance in industrial management teams in order to identify human-resource performance and improve human-resource management strategies. The factors that influence team performance are identified and extracted from previous studies, and then the timing of each task is examined in three scenarios, i.e. optimistic, probabilistic and Web-based, by identifying the processes that influence team performance. Model inputs are the number of students, the number of faculty, and the number of experts. Based on related studies, team performance outputs are three categories: The number of books, articles and theses; the desirability of team members; and the number of tasks completed, rejected, needed to revise or waiting in queue. The simulation was performed using AnyLogic software. The results show that the desirability of the group manager and the training expert have the highest values in most scenarios, and the number of tasks in the execution queue has a significant value in all scenarios. In some cases it is essential that new policies be adopted to improve HRM strategies in the industrial management group.