Title:
Foundational statistical methods in comparative design for simulation experiments
Authors:
- Maximilian Selmair
- Ahmed Tolba
Published in:
(2024). ECMS 2024, 38th Proceedings
Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2024
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-84-5
ISBN: 978-3-937436-83-8 (CD) Communications of the ECMS Volume 38, Issue 1, June 2024, Cracow, Poland June 4th – June 7th, 2024
DOI:
https://doi.org/10.7148/2024-0430
Citation format:
Maximilian selmair, Ahmed tolba (2024). Foundational Statistical Methods in Comparative Design for Simulation Experiments, ECMS 2024, Proceedings Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation. doi:10.7148/2024-0430
Abstract:
This study presents a comprehensive examination of the application of traditional statistical methods to simulation modeling within the hypothetical context of comparing manual and automated production lines in manufacturing. Through a detailed methodology involving the AnyLogic simulation platform and Minitab for statistical analysis, we emphasize the significance of power analysis, two-sample t-tests, and one-way ANOVA in validating and optimizing simulation models. Our hypothetical findings demonstrate the potential of statistical analysis to identify significant efficiency improvements, with a particular focus on the implications of process modifications on automated production lines. The primary contribution of this research lies in illustrating the practical application of statistical tools in simulation studies, serving as a manual for simulation modelers in logistics and manufacturing sectors. By foregrounding the statistical methods over specific operational improvements, this study aims to bridge the gap in literature regarding the integration of foundational statistical analysis within simulation modeling, offering valuable insights for enhancing decision-making and optimization in manufacturing simulations.
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