فهرست مطالب مجتبی امیدیان

The purpose of this study was interpretive structural modeling of the factors affecting supply chain optimization in the electricity industry. In this study, using a mixed method, a qualitative-quantitative approach was employed to model and verify the factors affecting supply chain optimization. To this end, through content analysis and thematic analysis using Novo software to form thematic networks, a qualitative model of factors influencing supply chain optimality was designed. In order to formulate a basic pattern of the factors affecting supply chain optimality, 23 experts in the electricity industry were selected through purposeful sampling method. In the quantitative section of the study, interpretive structural modeling (ISM) approach and MICMAC analysis were used. The results showed that the initial framework of supply chain optimality model has six dimensions including supply chain performance, supply chain management, supply chain risk management, supply chain quality management, production and operation, and supply chain stability. In the ISM graph, the variables were categorized at different levels, in which the "agency" and "strategy" factors were the highest and "customer satisfaction" was the lowest factor. After MICMAC analysis, the variables were categorized in three groups of independent variables with 11 variables, with four variables and dependent variables with 10 variables. No variables were included in the PA.

The purpose of this study is to modeling the factors affecting supply chain optimality based on the system dynamics technique in the power industry. The present study uses a hybrid approach to develop a qualitative-quantitative approach to formulate and validate the factors influencing the supply chain optimality. To elaborate and identify the optimal factors, from content analysis and thematic analysis using Novio software to construct themes, a qualitative model of factors influencing supply chain optimality was designed. On the optimality of the supply chain, 23 experts and experts in the electricity industry with purposive sampling method were selected. In the quantitative part, the system dynamics method was used for modeling, and then the data collected was analyzed by Venice software. This simulation was carried out over a one year period and represents a long-term horizon. Then the results of this simulation were presented and interpreted on the variables studied. Determining the relationships between variables and the types of variables can lead to a better understanding of the issue and to making appropriate decisions on the supply chain optimality problem.