Machinery productivity management requires forward-looking decision-making with a comprehensive approach. In today's competitive world, machinery productivity is one of the fundamental and important components of ensuring success and profitability in construction projects. Given that the largest portion of the country's development budget is spent on construction machinery costs every year, increasing productivity in this field requires a precise and systematic conceptual framework. Machinery management is a dynamic phenomenon that changes over time and is influenced by several factors, each of which also changes over time. As a result, we form these models based on systems thinking, and the science of system dynamics is a management tool based on this approach. Therefore, in this research, we used the system dynamics tool (cause and effect diagrams) to implement and better understand the modeling of machinery productivity management. Since models that are far from the insight of experts, experts and project agents using the model will not be practical and applicable. Therefore, by examining and identifying the factors affecting the productivity of machinery by project agents (employer, contractor and consultant), the models presented in this research have been confirmed by statistical analysis, factor analysis and structural equations. In this regard, as a case study, 20 large construction projects (over one billion) of Isfahan Municipality between 2013 and 2016 were examined using 52 questionnaires distributed among project agents (employer, contractor and consultant). The research results also showed that the management method, technical performance of the machine, workshop conditions, human resources and training are among the most important factors affecting the productivity of machinery in projects.