simulation techniques and expert systems
Jamal Khani Jazani; Amir Azarfar; Sahar Jafari
Abstract
Purpose: It is clear to everyone that in this period, one of the key factors of economic growth of countries is the knowledge-based economy, and in the meantime, the existence of knowledge-based companies forms the main infrastructure for achieving this important goal. This article was conducted with ...
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Purpose: It is clear to everyone that in this period, one of the key factors of economic growth of countries is the knowledge-based economy, and in the meantime, the existence of knowledge-based companies forms the main infrastructure for achieving this important goal. This article was conducted with the aim of developing knowledge-based companies with an emphasis on their business processes.Methodology: For this purpose, business processes were reviewed through a literary review and due to the comprehensiveness of the EFQM model, it was selected for further review. Then, 9 companies were identified as targets, located in the organization's growth center, interviews and the injuries of these companies were identified. Then, decision-making and coordination mechanisms according to the integrity of organizational processes, programs and approaches of large companies in the Excellence Award statement of the organization were reviewed and the approaches used by these companies were extracted and categorized. In this way, the opinions of experts in this field were used to adjust the programs and approaches. Finally, a model based on system dynamics was designed to investigate the key processes of acquiring knowledge-based businesses.Findings: Since increasing the score of EFQM model is considered as increasing the probability of company growth, so it is possible to use this model to provide solutions for the growth of knowledge-based companies.Originality/Value: In this paper, simulation and mathematical models are used to develop the performance of knowledge-based companies and simultaneously analyze the damage of companies and compare it with a comprehensive model, so that appropriate solutions to improve processes can be predicted.