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Quality control plays an important role in increasing the product quality. Fuzzy control charts are more sensitive than Shewhart control chart. Hence, the correct use of fuzzy control chart leads to producing better-quality products. This area is complex because it involves a large scope of industries, and information is not well organized. In this research, we provide a literature review of the control chart under a fuzzy environment with proposing several classifications and analysis. Moreover, our research considered both attribute and variable control chart by analyzing the related researches based on the content analysis method, to classify past and current developments in the fuzzy control chart. This work has included a distribution of articles according to the journal, the case studies related to fuzzy control chart, the percentage of types of fuzzy control charts used in the literature, performance evaluation of the fuzzy control chart and summary of key points of each review paper. Finally, this paper discusses some future research direction and our overviews. The results of this study can help researchers become familiar with well-known journals, fuzzy control charts used in sample case studies, and to extract key points of each paper in minimum time.

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System dynamics is a strategic approach for modeling complex systems and analyzing their behavior. Dynamic behavior in entrepreneurial system can be modeled using System Dynamics Approach and dynamic hypotheses about the system`s behavior can be proposed and tested using simulation and computer aided tools. However, as the review of literature shows, studies which link system dynamics modeling with entrepreneurship are rare and fragmented. This article presents a review of studies on the subject followed by integration and discussion on main research issues that have been the focus of previous studies. The main aim of this review is to categorize the available research related to the application of system dynamics modeling in entrepreneurship to integrate research and enable recommendations for future research. The Results reveal that the previous research could be categorized under a two dimensional taxonomy composed of level of analysis and level of modeling. The Level of analysis has three categories: micro level, meso level and macro level. The Level of modeling has six hierarchical levels. This study identifies several gaps in the literature and discusses the future directions in this field.