Organizational agility considering enablers and capabilities of agility with RBF neural network approach and multiple regressions

Abstract

The main purpose of this research is to identify the agility enabler indicators of the organization and the dimensions of agility enabler and how the agility enabler indicators can be used to achieve the agile system. In this study, the RBF neural network model and multiple regression models were used to achieve the ideal level of agility and to analyze the effect of indicators as well as to provide an optimal path to achieve agility. According to the results of the neural network approach, each organization should focus on automation to increase its agility level. Also in output variables, speed, responsiveness and flexibility have the most impact on model inputs. To achieve the agility of the organization, output variables must be upgraded to a certain level, and this upgrade is only possible by increasing the level of input variables. Further, the results of multiple regression showed that the automation and staff variables had the most and technology had the least impacts, respectively. Consequently, in order to reach an acceptable level of the dimensions of agility capabilities, the key variable (s) extracted from the analysis of this research must be identified and we must achieve the desired results by modifying them. In this case, maximum efficiency and upgrading agility level could be achieved by minimal time and cost.