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Supply chain simulation has been used to analyze possible scenarios, and one of the most used techniques is system dynamics, and although there are some literature reviews on this topic, the number of papers has increased in recent years, so it is necessary to analyze trends, locate the most relevant research groups, authors, and papers. This article reports a bibliometric review on the dynamic system applied to the supply chain on engineering and analyzes 519 documents found in the Scopus and Web of Science databases using PRISMA methodology and VosViewer software. Findings indicate that China, the United States of America and the United Kingdom are the countries that produce the most papers, although Cardiff University is the institution with the most consolidated research groups. The most productive authors are Towill, D.R., Naim, M.M. and Rabelo, L., although the most cited are Towill, D.R., Naim, M.M. and Wikner, J., while the Journals that most publish this topic are the International Journal Of Production Research, International Journal Of Production Economics and the Journal of Cleaner Production. [ABSTRACT FROM AUTHOR]

This paper proposes a linear search Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm to maximize the signal-to-noise ratio (SNR) index in the least number of iterations and determine which succession measure converges and maximizes the signal-to-noise ratio (SNR) in the least number of iterations. The analysis and validation are performed with experimental simulation and validated with four case studies collected from the literature. The case studies were evaluated in ten experiments with different combinations of the succession measures ak and ck. The results show that the proposed SPSA is an iterative, efficient, and easy to use method to maximize the quality indexes of production processes, which is feasible to implement within the six sigma methodology. Also, the results show that experiments 3 and 5 converge to the best results in the four case studies analyzed. [ABSTRACT FROM AUTHOR]

This paper proposes a linear search Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm to maximize the signal-to-noise ratio (SNR) index in the least number of iterations and determine which succession measure converges and maximizes the signal-to-noise ratio (SNR) in the least number of iterations. The analysis and validation are performed with experimental simulation and validated with four case studies collected from the literature. The case studies were evaluated in ten experiments with different combinations of the succession measures ak and ck. The results show that the proposed SPSA is an iterative, efficient, and easy to use method to maximize the quality indexes of production processes, which is feasible to implement within the six sigma methodology. Also, the results show that experiments 3 and 5 converge to the best results in the four case studies analyzed. [ABSTRACT FROM AUTHOR]