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Showing 2 results for Pilevari
Mr. Milad Rezaeefard, Dr Nazanin Pilevari, Dr. Farshad Faezy Razi, Prof. Reza Radfar,
Volume 13, Issue 2 (12-2022)
Abstract
Demand planning based on demand data in the supply chain includes the most significant steps in production planning. Therefore, the supply chain's correct demand forecasting may reduce this effect, known as the bullwhip effect or uncertainty concerning customer demand, thus reducing companies' and organizations' costs and surplus activities. Therefore, this article examined the statistical population characteristics to test the hypotheses through the path analysis drawn using descriptive statistics and FCM(fuzzy cognitive map) method. Then, the model strength was investigated using structural equation modeling (SEM) in AMOS software, and structural equations were presented. This article selected the Aftab oil factory as a case study. The findings of this study emphasized that demand management performance is highly essential for industries. Companies can design the sector independently as a demand management sector for evaluating customer demands at different levels of the supply chain. According to the fit of the main model, CFI and NFI indices are equal to 0.99 and 0.97, respectively, which are close to the optimal fit threshold. RMSEA and SRMR indices are equal to 0.01 and 0.01, respectively, both showing a relatively good fit of the model.
Farzaneh Rezaee, Nazanin Pilevari, Reza Radfar,
Volume 14, Issue 1 (6-2023)
Abstract
Abstract
Objective: From economic, environmental and social perspectives, the sustainability of the supply chain can give a competitive advantage to organizations. By designing a hybrid discrete event agent-based simulation model based on the simulation-optimization approach and meta-heuristic algorithms, this study has sought to evaluate the sustainability of the supply chain and improve the economic, environmental and social objectives of the supply chain.
Method: First, by identifying supply chain agents, an agent-based simulation model is developed. After designing the hybrid simulation model, the verification and validation phases are performed. By combining the simulation model with meta-heuristic algorithms and using the simulation-optimization approach, the optimal/near-optimal values of the components affecting the sustainability of the supply chain are finally extracted.
Findings: In addition to being able to reflect all the complexities of supply chains, the hybrid simulation optimization approach can also improve the key components affecting the sustainability of the supply chain.
Results: Implementation of sustainable supply chain components without optimizing the key variables of the supply chain can lead to the deterioration of performance and sustainability of the supply chain. The components of the maximum levels of product and inventory maintenance and how to implement environmental and social aspects in all the elements of the supply chain have a direct effect on the chain performance and should have appropriate values in different scenarios.
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